JP2019098350A - Plan creation device, plan creation method, and program - Google Patents

Abstract

To provide a plan creation device, a plan creation method, and a program where a plan for producing or treating a product per lot unit can be formed in a short time without remarkably reducing the precision of the plan-formed result, and further, a plan-formed person can make the same closer to a forming plan.SOLUTION: Different steel kind serial serial numbers g are classified into a different steel kind serial serial number gbeing made into a product steel material and a different steel kind serial serial number gbeing made into a non-product steel material, and, as weighting coefficients to them, a weighting coefficient Cto a combination of steel kinds k,k' being made into a product steel material and a weighting coefficient Cto a combination of steel kinds k, k' being made into a non-product steel material are used, respectively. In the ranges where the weighting coefficient Cto a combination of steel kinds k, k' being made into a non-product steel material being a fixed value, and the weighting coefficient Cto a combination of steel kinds k, k' being made into a product steel material being the weighting coefficient Cto a product steel material or more and being below the weighting coefficient Cto a non-product steel material, as actual numbers NPare larger, the same is made into smaller value.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a plan creation device, a plan creation method, and a program, and is particularly suitable for use for assembling a plurality of products in a lot to produce or process a plurality of products in a lot unit.

When making a production plan of products in the manufacturing industry, it is often the case that a production plan is formulated so that a plurality of products are assembled in an appropriate size and an appropriate number of lots and manufactured in lots.
Here, the case where continuous casting is performed in a steelmaking plant will be described as an example of collectively manufacturing a plurality of products in lots.

  In a steelmaking plant, molten iron supplied from a blast furnace is charged into a converter and oxygen is blown to remove carbon in the molten iron to produce molten steel. This is called primary refining. Next, the molten steel is poured into a container called a ladle and conveyed to the secondary refining process. This ladle full of molten steel is called charge. For example, there is a RH (Ruhrstahl Heraeus) process as a secondary refining process, and impure gas in the molten steel is removed in vacuum by circulating the molten steel in a vacuum tube to adjust the components of the molten steel. When the secondary refining process is completed, the ladle is conveyed to a continuous casting machine. In a continuous casting machine, a cast slab which is a semifinished product is manufactured by simultaneously pouring molten steel from a ladle to a mold and cooling it. In a continuous casting machine, it is possible to cast a plurality of charges in succession, and a group of a plurality of charges cast in succession is called cast. Further, continuous casting of molten steels of a plurality of charges is called continuous casting. If the number of times of continuous casting is increased too much, melting damage of components of the continuous casting machine (refractories of tundish and mold, immersion nozzle, etc.) will occur, so continuous casting may be performed according to the degree of melting failure depending on types. The number of charges to be implemented must be properly defined.

The slab produced in the continuous casting machine is called a slab and is transported to the rolling process. In the rolling process, the slab is reheated by a heating furnace and rolled to a thickness of several millimeters by a rolling mill in a high temperature state and coiled. There is a limit to the number of slabs that can be continuously rolled by a rolling mill depending on the shape and hardness of the slabs. A group of slabs rolled continuously by a rolling mill is called a chance.
Each slab is given information such as components, shape, desired hot rolling date from order information, and lots such as casts and opportunities are organized in consideration of constraints in the steel making process and the rolling process.

  Also, in recent years, attempts have been made to shorten lead time and reduce heat loss by making cast, which is a lot of steelmaking process, equal to the chance of being a lot of rolling process. In the case of performing such an operation, it is necessary to determine a cast formation (slabs included in each cast) simultaneously satisfying the constraints in the steel making process and the rolling process.

  In cast formation, the number of castings is increased by taking a large number of slabs by incorporating a large number of slabs in the cast, and preparation time for reducing defective slabs at the start and end of continuous casting and starting recasting An increase in production volume can be expected due to the reduction of On the other hand, there are limitations on slabs that can be incorporated into the same cast depending on conditions such as components, shape, and desired hot rolling date. For this reason, it is required to carry out cast organization that produces as large a lot as possible within the range that satisfies the constraint conditions.

In cast organization work, the number of slab combinations is generally large because the number of slabs such as hundreds or thousands is handled. Therefore, it takes a long time for the planner (planner) to perform cast formation satisfying all constraints.
Then, there exists a technique of patent document 1 as a technique which performs cast formation automatically.
Patent Document 1 discloses that each charge is represented by a node, and that a network is created by tying the intervals between castable charges by tying them together, and searching for the longest cast route on the network. It is done.

JP, 2012-11451, A

  However, in the technique described in Patent Document 1, there is no description on a method of putting slabs into charges, and cast organization will be performed based on information of given charges. Therefore, if the charges are not properly organized, there is a possibility that the result of the optimal cast formation can not be obtained. Further, as described above, since there are many combinations of slabs, if the charge is appropriately included in the cast in the technique described in Patent Document 1, the scale of the problem may become too large and the calculation time may become long. Therefore, it is not easy to shorten the planning time without significantly reducing the accuracy of the planning result.

  Also, in general, the planners perform cast formation with reference to past operation results. However, the technology described in Patent Document 1 does not consider such operation results. Therefore, it is not easy to get closer to the cast organization performed by the planner.

  The present invention has been made in view of the above problems, and enables a plan for producing or processing a product in units of lots to be formulated in a short time without largely degrading the accuracy of the drafting result. In addition, it aims to be able to get close to the plan that the planner makes.

  The plan creating device of the present invention is a plan creating device for creating a plan for collectively producing and processing a plurality of products in units of lots, which is information of the plurality of products, and the manufacturing conditions of the products The product can be included in the same lot among the acquisition means for acquiring product information including, the selection means for selecting a part of the plurality of products, and the subset of the products selected by the selection means Lot candidate deriving means for deriving as a lot candidate a subset satisfying the first lot inclusion condition that is expressed using the manufacturing conditions of the product as conditions, and optimum from the lot candidate derived by the lot candidate deriving means Optimization means for deriving optimal lot candidates by performing optimization calculation for maximizing or minimizing the value of the objective function within the range satisfying the constraint expression, and the result of the optimization calculation When it is determined by the determination unit that determines whether or not the calculation result has converged, and the determination unit determines that the result of the optimization calculation has not converged, the optimal lot candidate included in the optimal lot candidate derived by the optimization unit When the result of the optimization calculation is determined to be converged by the redefinition means for redefining a product as one product and the determination means, the optimal lot candidate derived by the optimization means is optimized And output means for outputting information indicating which product is included in the optimal lot, the objective function includes a first lot candidate derivation evaluation index including the number of lots, and the objective function. A product of a weighting factor for the first lot candidate derivation evaluation index and a product of a second lot candidate derivation evaluation index including a remainder and a weighting factor for the second lot candidate derivation evaluation index The excess amount is an amount of a portion which becomes excess in the lot when one or more of the products are put together in the lot, and at least one of the second lot candidate derived evaluation indexes Are classified according to the classification condition represented using the manufacturing condition of the product, and the weighting factor for the second lot candidate derived evaluation index is classified according to the classification condition represented using the manufacturing condition of the product The weight index for each of the second lot candidate derivation evaluation index is included, and the constraint equation is that the number of products included in the optimum lot candidate is the same as the number of products selected by the selection unit. And the formula formulated that the same product is formulated not to be included in the different lot candidates, and the optimization means is based on the classification condition represented using the production condition of the product Therefore, the weighting factor for the second lot candidate derivation evaluation index classified is the manufacturing result data of the product, and is based on the manufacturing result data classified according to the classification condition represented using the manufacturing condition of the product. And the selecting means, when the product is redefined by the redefining means, all of the products redefined by the redefining means and some of the unselected products. The selection of the product by the selection means, the derivation of the lot candidate by the lot candidate derivation means, and the optimization until the determination means determines that the result of the optimization calculation has converged. And performing the optimization calculation by means.

  The plan creation method of the present invention is a plan creation method for creating a plan for collectively producing and processing a plurality of products in units of lots, which is information of the plurality of products, and the manufacturing conditions of the products A product can be included in the same lot among an acquisition step of acquiring product information including a selection step, a selection step of selecting a portion of the plurality of products, and a subset of the products selected by the selection step A lot candidate derivation step of deriving as a lot candidate a partial set satisfying a first lot inclusion condition that is expressed using the manufacturing condition of the product as a condition, and optimum from the lot candidate derived by the lot candidate derivation step Optimization step for deriving candidate lots by performing an optimization calculation that maximizes or minimizes the value of the objective function within the range satisfying the constraint expression, and the result of the optimization calculation When it is determined in the determination step of determining whether or not convergence has occurred and the result of the optimization calculation has not converged in the determination step, the optimal lot candidate included in the optimal lot candidate derived in the optimization step When the result of the optimization calculation is determined to have converged by the redefinition step of redefining the product as one product and the determination step, the optimal lot candidate derived by the optimization step is optimized. And an output step of outputting information indicating which product is included in the optimal lot, the objective function includes a first lot candidate derivation evaluation index including the number of lots, and the objective function. A product of a weighting factor for the first lot candidate derivation evaluation index and a product of a second lot candidate derivation evaluation index including a remainder and a weighting factor for the second lot candidate derivation evaluation index The excess amount is an amount of a portion which becomes excess in the lot when one or more of the products are put together in the lot, and at least one of the second lot candidate derived evaluation indexes Are classified according to the classification condition represented using the manufacturing condition of the product, and the weighting factor for the second lot candidate derived evaluation index is classified according to the classification condition represented using the manufacturing condition of the product The weight index for each of the second lot candidate derivation evaluation indexes is included, and the constraint equation is that the number of products included in the optimal lot candidate is the same as the number of products selected by the selection step. And the formula that formulates that the same product is not included in different lot candidates, and the optimization step is performed on classification conditions represented using manufacturing conditions of the product. Therefore, the weighting factor for the second lot candidate derivation evaluation index classified is the manufacturing result data of the product, and is based on the manufacturing result data classified according to the classification condition represented using the manufacturing condition of the product. And the selection step is a step of selecting all of the products redefined by the redefinition step and some of the products not selected when the products are redefined by the redefinition step. Are selected, the selection of the product by the selection step, the derivation of the lot candidate by the lot candidate derivation step, and the optimization until the determination step determines that the result of the optimization calculation has converged. And performing the optimization calculation according to a process.

  A program according to the present invention causes a computer to function as each means of the plan creation device.

  According to the present invention, since the optimum lot is finally derived while the size of the lot candidate is gradually enlarged, the production or processing of the product in units of lots is performed as compared to the case of deriving the lots collectively from the product. Can be made in a short time without significantly reducing the accuracy of the planning result. At the same time, the evaluation index for evaluating the lot candidate is classified, and a weighting factor representing the degree of importance is set for each evaluation index.

It is a figure explaining an example of the outline | summary of the method of creating a cast plan. It is a figure which shows an example of a functional structure of a cast formation apparatus. It is a figure which shows the 1st example of slab information. It is a figure which shows an example of the slab information after rearrangement. It is a figure which shows an example of slab group information. It is a figure which shows an example of slab group information after rearrangement. It is a figure which shows notionally an example of the method of solving a set division | segmentation problem. It is a figure which illustrates a matrix notionally. It is a figure which shows an example of slab group information after redefinition. It is a flow chart explaining an example of processing of a cast formation device. It is a flowchart explaining an example of processing of Step S1002. It is a figure which shows the 1st example of the result of the application example of a 1st reference form, and a non-application example in a table form. It is a figure which shows the relationship of the number of slabs and calculation time in the application example of a 1st reference form, and a non-application example. It is a figure which shows the evaluation value in the application example of a 1st reference form, and a non-application example. It is a figure explaining an example of the relationship between the steel type of a slab, and charge. It is a figure which shows the 2nd example of slab information. It is a flow chart explaining an example of processing of a cast candidate derivation part. It is a figure which shows the 2nd example of the result of the application example of a 2nd reference form, and a non-application example in a table form. It is a figure explaining an example of a weighting factor with respect to the combination of the steel type used as product steel materials. It is a figure which shows an example of the actual number of times of different steel type continuous casting. It is a figure which shows an example of the weighting coefficient with respect to a different steel type | mold series number. It is a figure which shows the number of different steel type | molds in cast candidates according to the combination of steel type.

  The embodiment of the present invention described later is premised on the embodiment described in Japanese Patent Application No. 2016-104543. Therefore, before describing the embodiment of the present invention, the embodiment described in Japanese Patent Application No. 2016-104543 will be described as a reference embodiment. In the following description, as a production plan, a case where a cast plan in a continuous casting machine is created (that is, cast formation is performed) will be described as an example. In this case, "slab" corresponds to "product", "cast" corresponds to "lot", and "casting" corresponds to "manufacturing". In addition, “product” refers to a product in which the raw material has been modified, and is not limited to the final product etc. which is marketed. For example, intermediate products (semi-finished products) are also included in the "products".

[First embodiment]
(Overview)
The outline of the first embodiment will be described.
FIG. 1 is a diagram for explaining an example of an outline of a method of creating a cast plan in the present embodiment.
Slab information including the manufacturing condition (manufacturing specification) of the slab is associated with each of the plurality of slabs to be manufactured during the planning target period. Based on the slab information, each of the plurality of slabs is grouped such that the slabs whose manufacturing conditions coincide within a predetermined range belong to the same slab group.

  In FIG. 1, the slab group No. which identifies the slab group obtained in this manner. And the slab group No. The number of slabs belonging to the slab group (the number of slabs) and the slab group No. 1 shows the earliest (the earliest) of the desired hot rolling days given as one of the manufacturing conditions of the slabs belonging to the slab group of After being placed in the yard, the slab produced by the continuous casting machine is hot-rolled in the next hot-rolling step. As a day on which the hot rolling is to be performed, a day desired by the factory side is a hot rolling desired day. That is, the desired hot rolling date is the delivery date of the slab to the hot rolling plant. The desired hot-rolling date is set based on, for example, the delivery date for the customer of the coil obtained by winding the hot-rolled steel sheet obtained by hot rolling, the operating condition of the equipment of the hot-rolling line, and the like.

In the example illustrated in FIG. 1, the slab groups are sorted in order from the earliest hot spread desired day (the earliest date). In FIG. 1, for the convenience of explanation, the order of arrangement and the slab group No. when the slab groups are arranged in order from the one with the heat spread desired day (the earliest date) being earlier. And the same.
Next, a part of the plurality of slab groups is selected, and a subset of the selected slab group is created. In the example shown in FIG. 1, ten slab groups out of the 27 slab groups are selected from those with an earlier desired hot-rolling date (earliest date), and subsets of these ten slab groups are created. For each of these subsets, it is determined whether or not the condition (constraint condition) that can be manufactured by the same cast is satisfied, and the subset satisfying this condition is set as a cast candidate.

Next, an optimal combination of cast candidates is derived from the cast candidates by solving the optimization problem. Details of the optimization problem will be described later. In the following description, the optimal cast candidate obtained in this manner is referred to as a "cast piece" as needed.
FIG. 1 shows that cast pieces 101a to 101d are obtained as a result of the calculation of such an optimization problem (the result of "first division optimization"). In FIG. 1, one square shown in the cast pieces 101a to 101r indicates a slab group constituting the cast piece, and the numbers in the square indicate the slab group No. of the slab group. Indicates

  Next, each of the cast pieces 101a to 101d is redefined as a new slab group, all the new slab groups, and some slab groups among the unselected slab groups are selected, and the selected slab group is selected. Create a subset of In the example shown in FIG. 1, six slab groups are selected from among the 17 slab groups that have not been selected from the earliest ones with a desired hot-rolling date (the earliest date), and four new slab groups are selected. Create a subset of Slab groups. As described above, a subset satisfying a condition (constraint) that can be included in the same cast among these subsets is set as a cast candidate, and an optimal combination of cast candidates (cast piece) is selected from the cast candidates. , Derivation by solving the optimization problem.

FIG. 1 shows that cast pieces 101 e to 101 h are obtained as a result of calculation of this optimization problem (result of “second split optimization”). In FIG. 1, the part which has already been derived | led-out as a cast piece and becomes a new slab group is filled and shown with gray.
Next, each of the cast pieces 101e to 101h obtained in this manner is redefined as a new slab group. Then, the cast pieces 101i to 101m and 101n to 101r are derived in this order in the same procedure as described above. In this way, the slab group is gradually added to the cast piece while maintaining the already derived cast piece, and the size of the cast piece is expanded. In practice, the arrangement order of the slab groups included in the cast is determined based on the width of the slabs belonging to the slab group, but in this case the size is expanded while maintaining the already derived cast piece In order to clearly show the situation, without reordering by width, a newly added slab group is connected behind the cast piece already created.

When the slab group included in the cast piece derived as described above satisfies the predetermined convergence determination condition, the cast piece is decided as a cast. In the example shown in FIG. 1, when all the slab groups are included in any of the cast pieces, it is assumed that the convergence determination condition is satisfied. Therefore, cast pieces 101n to 101r are cast. From the casts obtained in this way, the cast to which each slab belongs is output as the drafting result of the cast plan.
FIG. 1 shows the concept of the present embodiment, and does not necessarily correspond to the contents described below. For example, the number of slabs included in the slab group shown in FIG. 1 and the contents of the desired hot-rolling day (the earliest date) are different from those shown in FIGS.

(Cast knitting device 200)
FIG. 2 is a diagram showing an example of a functional configuration of the cast knitting device 200. As shown in FIG. The hardware of the cast organization apparatus 200 is realized by using, for example, an information processing apparatus provided with a CPU, a ROM, a RAM, an HDD, and various interfaces, or dedicated hardware.

<Slab Information Acquisition Unit 201>
The slab information acquisition unit 201 acquires and stores slab information. The slab information acquisition unit 201 acquires slab information, for example, by performing an operation by the operator on the cast formation device 200, receiving slab information transmitted from an external device, or reading slab information stored in a portable storage medium. Do.

FIG. 3 is a diagram showing an example of the slab information 300. As shown in FIG.
In FIG. 3, the slab information 300 includes slab No. Material, slab weight, slab width, slab thickness, coil width, coil thickness, coil length, and desired hot-rolling date are mutually correlated information. Slab information is individually provided for each of the slabs to be planned.

Slab No. Is a number identifying a slab.
The material indicates the component of the slab and the like. Here, the material is represented by a symbol that identifies the material.
The slab weight, slab width and slab thickness are the slab weight, width and thickness, respectively. Material, slab weight, slab width, slab thickness, and desired hot-rolling date are manufacturing conditions in a process of manufacturing a slab (continuous casting process).

  The coil width, the coil thickness, and the coil length respectively correspond to slab No. The width, thickness and length of the coil obtained by hot rolling the slab identified in The coil width, the coil thickness, the coil length, and the desired hot rolling date are manufacturing conditions in a process (hot rolling process) subsequent to the process of manufacturing the slab. As described above, the desired hot-rolling day is a day desired by the factory as a day for hot rolling.

<Slab Group Creation Unit 202>
Based on the slab information 300 acquired by the slab information acquisition unit 201, the slab group creation unit 202 is included in the slab information 300 so that slabs whose manufacturing conditions coincide within a predetermined range belong to the same slab group. Group each of the slabs.
First, the slab group creation unit 202 rearranges the slabs (records) of the slab information 300 based on at least one of the slab manufacturing conditions included in the slab information 300. In the present embodiment, the slabs (records) of the slab information 300 are rearranged in order from the slab with the earliest desired hot-rolling date. FIG. 4 is a view showing an example of the slab information 400 in which the slabs (records) of the slab information 300 shown in FIG. 3 are rearranged in order from the one with a desired hot rolling date. In order to clearly show the relationship between FIG. 3 and FIG. 4, in FIG. 3 and FIG. Is attached. However, in FIG. 4, slab No. Is “1”, and thereafter slab No. 1 in ascending order. You may add it again. Furthermore, the slab may be sorted based on the item to be emphasized next to the desired hot-rolling date in the slab (record) having the same desired hot-rolling date.

  Next, the slab group creation unit 202 selects one slab at the highest position among the unselected slabs (records) included in the slab information 400 sorted as described above. That is, the slab group creation unit 202 selects one of the non-selected slabs (records) included in the slab information 400, which has the earliest desired date of hot rolling.

Next, the slab group creation unit 202 determines whether or not there is a slab group to which the slab selected from the slab information 400 can be added, among the already created slab groups. As described above, in the present embodiment, slabs having the same slab manufacturing condition within a predetermined range are included in the same slab group.
Specifically, in the present embodiment, the slab group creation unit 202 selects from the slab information 400 among the already created slab groups when all the determination conditions (A1) to (D1) below are satisfied. It is determined that there is a slab group to which added slabs can be added.

(A1) The maximum value of the difference between the width of the slab (slab width) included in the already created slab group and the width of the slab selected from the slab information 400 (slab width) is 100 [mm] or less.
(B1) The maximum value of the difference between the thickness of the slab (slab thickness) included in the already created slab group and the thickness of the slab (slab thickness) selected from the slab information 400 is 2 [mm] or less.
(C1) The maximum value of the difference between the desired hot-rolling date of the slab included in the already created slab group and the desired hot-rolling date of the slab selected from the slab information 400 is 2 [day] or less.
(D1) The material of the slab included in the already created slab group and the material of the slab selected from the slab information 400 are the same.

If the slab group creation unit 202 determines that there is no slab group to which a slab selected from the slab information 400 can be added, among the already created slab groups, a new slab group is created and the selected slab group is selected Include slabs in the new slab group created.
On the other hand, when there is a slab group to which a slab selected from the slab information 400 can be added among the already created slab groups, the slab group creation unit 202 determines the slabs included in the slab group and the selected slabs. It is determined whether the total number of sheets is equal to or less than the upper limit value. Since the rolling rolls wear in the hot rolling process, there is a restriction on the number of slabs of the same width band to be continuously hot-rolled at the same chance. Therefore, in the present embodiment, an upper limit value is set for the number of slabs included in one slab group. In the present embodiment, this upper limit value is set to 40 [sheets] or less.

The slab group creation unit 202 creates a new slab group if the sum of the number of slabs included in the slab group to which the slab selected from the slab information 400 can be added and the number of the selected slabs is not less than the upper limit. The selected slab is included in the new slab group created.
On the other hand, when the sum of the number of slabs included in the slab group to which the slab selected from the slab information 400 can be added and the number of the selected slabs is equal to or less than the upper limit, the slab group creation unit 202 determines the selected slabs Include in a slab group.

  The slab group creation unit 202 selects, one by one, the slab at the top of the slab information 400 among the unselected slabs (records) included in the slab information 400 sorted in ascending order of desired hot rolling dates. Then, the above processing is performed, and the slabs included in the slab information 400 are included in any slab group.

FIG. 5 is a view showing an example of slab group information 500 created from the slab information 400 shown in FIG. The slab group information 500 is a list of slab groups.
In FIG. 5, slab group information 500 includes slab group No. , Material, slab width (maximum value, minimum value), slab thickness (maximum value, minimum value), slab weight, coil length, desired hot-rolling date (early day, latest date), and number of slabs mutually relate Information.

Slab group No. Is a number identifying a slab group.
The material is a material of the slab belonging to the slab group. In the present embodiment, slabs of the same material belong to one slab group under the condition (D1) described above.
The maximum value of the slab width means the maximum value of the width (slab width) of the slabs included in the slab group. The minimum value of the slab width means the minimum value of the width (slab width) of the slabs included in the slab group.

The maximum value of the slab thickness refers to the maximum value of the thickness (slab thickness) of the slabs included in the slab group. The minimum value of the slab thickness means the minimum value of the thickness (slab thickness) of the slabs included in the slab group.
The slab weight is a total value of the weights (slab weights) of the slabs included in the slab group.

The coil length is a total value of coil lengths of the slabs included in the slab group.
The earliest date of the preferred hot-rolling day is the earliest of the preferred hot-rolling days of the slabs included in the slab group. The latest date of the hot-spreading request day means the latest hot-spreading day of the slabs included in the slab group.
The slab group creation unit 202 creates the slab group information 500 as described above.

  As described above, in the present embodiment, a plurality of slab groups are combined to form a cast. For this reason, when the manufacturing conditions (for example, at least one of slab thickness, slab width, and desired rolling date) of slabs belonging to one slab group vary, the slab manufacturing conditions also in the cast combining these Variation of Therefore, in the present embodiment, the slab group creation unit 202 generates a record (slab) of the slab information 300 in accordance with at least one value of the slab manufacturing condition included in the slab information 300 (in the present embodiment, a desired hot rolling date). Determine the slab group to which the slab belongs in the sorted and sorted order. In this way, it is possible to suppress that slabs with largely different manufacturing conditions belong to the same slab group.

<Slab Group Selection Unit 203>
The slab group selection unit 203 rearranges the slab groups (records) of the slab group information 500 created by the slab group creation unit 202 in order from the slab group with the earliest earliest date of the desired hot rolling date. FIG. 6 is a diagram showing an example of slab group information 600 in which the slab groups (records) of the slab group information 500 shown in FIG. 5 are rearranged in order from the earliest to the desired hot rolling date. In order to clearly show the relationship between FIG. 5 and FIG. 6, in FIG. 5 and FIG. Is attached. However, in FIG. 6, the slab group No. Is “1”, and the slab group No. You may add it again.

  The slab group selection unit 203 selects a part of the slab groups (records) included in the slab group information 600 rearranged as described above in order from the upper slab group (record) by a predetermined number. That is, the slab group selection unit 203 sets a predetermined number of slab groups (records) included in the slab group information 600 sorted as described above, in order from the earliest hot spread desired day to the earliest one. Only select.

  When the number of selected slab groups per one time (the predetermined number) is “5”, when the slab group selection unit 203 selects the first slab group, the slab group information 600 shown in FIG. Select a slab group on the first to fifth lines. The case of selecting the second and subsequent slab groups will be described later. The calculation time required for optimization calculation becomes longer as the number of slab groups to be selected increases, so the number of selected slab groups is determined to be at least 2 or more based on the time allowed for this calculation.

<Cast candidate derivation unit 204>
The cast candidate derivation unit 204 derives cast candidates from the slab group selected by the slab group selection unit 203.
First, the cast candidate derivation unit 204 enumerates all possible combinations (subsets) of slab groups from the set of slab groups selected by the slab group selection unit 203. When the number of selected slab groups per one time in the slab group selection unit 203 is “5”, the cast candidate derivation unit 204 performs 32 (= 2 ⁵ ) combinations (combinations of subsets) as a combination (partial set) of slab groups. List subsets).

  Next, the cast candidate derivation unit 204 determines whether or not all the constraint conditions (A2) to (E2) below are satisfied for each of the listed combinations (subsets) of slab groups, and only those that satisfy the conditions Adopt as a cast candidate.

(A2) Material restriction The materials of the slabs included in the combination (partial set) of the slab groups do not include materials prohibited to be included in the same cast.
For example, when a slab of material A and a slab of material C can not be included in the same cast, a combination of a slab group to which a slab of material A belongs and a slab group to which a slab of material C belongs violates material constraints. Therefore, it is not adopted as a cast candidate.

(B2) Cast Weight The total weight of the slabs included in the combination (subset) of the slab groups does not exceed the upper limit value of the (one) cast weight.
For example, in the case where the upper limit value of the weight of cast is 1300 [ton], slab group No. 1 shown in FIG. Since the sum of the slab weights of the slab groups of “1”, “2”, “5” and “3” is 1321.7 [ton], the combination (subset) of these slab groups is adopted as a cast candidate I will not.

  In this embodiment, in order to ensure that slabs included in the same cast are hot-rolled in the hot-rolling process as the same lot (chance), it is necessary to derive a cast that satisfies the constraints that occur during hot-rolling. There is. Therefore, in the present embodiment, the following constraints (C2) to (E2) are adopted.

(C2) Coil length The sum of the coil lengths of the slabs included in the combination (subset) of the slab groups does not exceed the upper limit value.
For example, when the upper limit value is 100 [km], slab group No. 1 shown in FIG. Since the sum of the coil lengths of the slab groups “1”, “2”, “5” and “3” is 104.7 [km], the combination (subset) of these slab groups is a cast candidate Not adopted

(D2) Width transition restriction When the rolling order is rearranged, the difference between the widths of two slabs which precede and follow one another is equal to or less than the upper limit value.
In the hot rolling process, there may be a restriction that the so-called narrow down where hot rolling is performed in order from a slab having a large width. In this case, when the slabs included in the combination (partial set) of the slab groups are rearranged in order from the one having the largest slab width, the difference between the widths (slab widths) of two adjacent slabs needs to be equal to or less than the upper limit. is there. For example, when the upper limit is 150 [mm], the slab group No. Consider whether slab groups (subsets) of “2” and “5” can be adopted as cast candidates. Slab group No. The minimum value of the slab width of the slab group having “2” is 1600 [mm], and the slab group No. The maximum value of the slab width of the “5” slab group is 1400 [mm]. For this reason, when the slabs included in these slab groups are rearranged to be narrow down, a difference of 200 [mm] or more is generated as a difference in slab width between any slabs. Therefore, a combination (subset) of these slab groups is not adopted as a cast candidate.

(E2) Same Width Number Constraint The number of slabs having a width difference equal to or less than a predetermined value is equal to or less than the upper limit.
If a large number of slabs having a width difference equal to or less than a predetermined value are included in the same chance, the wear of the rolling rolls may cause a coil shape defect and the quality of the coil may be degraded. Therefore, a constraint is provided that the number of slabs having a width difference equal to or less than a predetermined value is equal to or less than the upper limit value. For example, in the case where the number of slabs having a width difference of 100 [mm] or less becomes 40 [sheets] or less is the same width number restriction, slab group No. In the slab group (subset) of “2” and “4”, there are 49 [the number of slabs in the range of 1600 [mm] to 1700 [mm] in slab width. Therefore, a combination (subset) of these slab groups is not adopted as a cast candidate.

<Optimization unit 205>
The optimization unit 205 derives an optimal combination of cast candidates (cast pieces) from the cast candidates derived by the cast candidate derivation unit 204 by solving an optimization problem. In this embodiment, a set division problem is used as an optimization problem.

FIG. 7 is a diagram conceptually showing an example of a method for solving the set division problem.
As shown in FIG. 7, it is assumed that a set M of m elements is provided (the black circle in FIG. 7 is one element). In the example shown in FIG. 7, ten elements are given (m = 10). In the present embodiment, one element corresponds to one slab group.

Next, n subsets are derived as subsets of the set M. For example, let all combinations that can be taken as combinations of elements be a subset. However, instead of using all combinations that can be taken as combinations of elements as a subset, some combinations may be used as a subset. In FIG. 7, only the subsets 701a to 701g are shown for convenience of notation. In this embodiment, one subset corresponds to one cast candidate.
Next, an evaluation index (cost) is derived for each of these n subsets.

Next, a combination of subsets for which the sum of evaluation index (cost) values is optimal is taken as an optimal solution. The smaller the value of the evaluation index is, the smaller the optimum value is in the case of the desired problem setting, and the larger the value of the evaluation index is the larger in the case of the desirable problem setting. FIG. 7 shows that the subsets 701a, 701d, and 701f are optimal solutions. In this embodiment, each such subset corresponds to a cast piece.
In the set division problem, in general, an optimal solution is derived under the constraint that the same element is not included in multiple subsets. As shown in FIG. 7, ten elements are included only in any one subset.
Since the set division problem itself can be realized by a known technique, the detailed description thereof is omitted here.

On the premise of the above, an example of processing performed by the optimization unit 205 will be described.
In this embodiment, an optimization problem (set division problem) for deriving an optimal cast (cast piece) is formulated by the following equations (1) to (3). That is, in the present embodiment, the optimization unit 205 minimizes the value of the objective function f of the following equation (1) within the range satisfying the constraint equations of the following equations (2) and (3): The decision variable x _j is derived. The optimization problem (set partitioning problem) can be solved, for example, by known mixed integer programming, and it is also possible to use a commercial solver (cplex or the like) at that time.

Here, it is assumed that the set of slab groups i included in the cast candidate j is i∈N _I , and the set of cast candidates j is j∈N _J. Further, the decision variable x _j is a 0-1 variable (x _j ∈ {0, 1}) which becomes “1” when adopting the cast candidate j as a cast piece and becomes “0” when not adopting it Do. Also, c _j represents the evaluation value of the cast candidate j. In the present embodiment, the evaluation value c _j is expressed by the following equation (4).
c _j = C _W × W + C _T × T + C _D × D + C _N- C _S × S (4)

In the equation (4), W is the difference between the maximum value and the minimum value of the width (slab width) of the slab included in the cast candidate j. C _W is a weighting factor for W.
T is the difference between the maximum value and the minimum value of the thickness of the slab (slab thickness) included in the cast candidate j. C _T is a weighting factor for T.
D is the difference between the average value of the heat spread desired dates of the slabs included in the cast candidate j and the earliest date. _CD is a weighting factor for D.

C _N is a weighting factor for the number of casts. As described above, the decision variable x _j is a 0-1 variable which becomes “1” when adopting the cast candidate j as a cast piece, and becomes “0” when not adopting it. Therefore, in the calculation of equation (1), the integrated value of the decision variable x _j is the number of casts. Therefore, the number of casts can be evaluated by integrating the value obtained by multiplying the decision variable x _j by the weighting factor C _N.
S is the number of slabs included in the cast candidate j. C _S is a weighting factor for S. As the yield improves as the number of slabs included in one cast increases, the evaluation value c _j of the cast candidate j becomes a value indicating that the evaluation is high (the value decreases in the equation (4)) ).
The weighting factors C _W , C _T , C _D , C _N , and C _S are preset according to how much each evaluation item is emphasized, and represent the balance of evaluation among the respective evaluation items. The weighting factor is also referred to as a cost factor.

For example, the slab group numbers in slab group information 500 and 600 shown in FIGS. The evaluation value c _j of the cast candidate j consisting of slab groups of “1” and “2” is as follows. First, W is 200 (= 1700-1500). Moreover, T is 0 (= 250-250). Moreover, D is 0.4 (= 1.4-1). Here, when obtaining D, the slab group No. It is assumed that the average value of the desired hot-rolling dates of slabs included in the slab groups of “1” and “2” is June 1.4. In addition, S is 37 (= 9 + 28).
From the above, for example, when C _W = 1, C _T = 1, C _D = 200, C _N = 1000, C _S = 1, the evaluation value c _j is 1243 (= 1 × 200 + 1 × 0 + 200 × 0.4 + 1000-1 × 37).

In the equations (2) and (3), M _i is the number of slabs belonging to the slab group i selected as the cast candidate j. H _MAX is the upper limit of the number of slabs. In each of the slab groups i included in the cast candidate j, i∈N _{I and} M _i HH _MAX in the equation (2) is satisfied when the number M _{i of} slabs belonging to the slab group _i is the upper limit value H _MAX or more. Means that the equation (2) is applied. Further, for each of the slab group i included in the cast candidate j, the number M _{i of} slabs belonging to the slab group i is less than the upper limit value H _{MAX for} i∈N _{I and} M _i <H _MAX in the equation (3) In the case, it means that the equation (3) is applied.

A _ij in the equations (2) and (3) is a matrix having the slab group i as a row and the cast candidate j as a column, and is a matrix having “0” or “1” as an element.
FIG. 8 is a diagram for conceptually explaining the matrix A _ij .
In FIG. 8, each column indicates a slab group i included in one cast candidate j. "1" is given to the line corresponding to the slab group i included in the cast candidate j, and "0" is given to the line corresponding to the slab group i not included in the cast candidate j. For example, cast candidate 1 (j = 1) includes slab groups 1 and 2 (i = 1 and 2) and does not include other slab groups.

The result of the assignment of “1” or “0” to each of the cast candidates j derived by the cast candidate deriving unit 204 is a matrix A _ij . The lower part of the arrow line in FIG. 8 indicates that the evaluation value c _j and the decision variable x _j can be obtained for each cast candidate j.
In the example shown in FIG. 8, the matrix A _ij is expressed as the following equation (5).

Therefore, the constraint condition of equation (2) represents that a slab group i having the number M _{i of} slabs to which it belongs is equal to or more than the upper limit H _MAX is always adopted once as a slab group to be included in a cast piece.
On the other hand, in the constraint condition of equation (3), a slab group i having a number M _i of slabs less than the upper limit H _MAX is adopted once as a slab group to be included in a cast piece, or not adopted once. It represents that (the number of times of adoption is 0 (zero)).
The optimization unit 205 executes the derivation of the decision variable x _j as described above each time the cast candidate derivation unit 204 derives a cast candidate. The cast candidate j corresponding to the decision variable x _j given "1" becomes a cast piece.

<Judgment unit 206>
The determination unit 206 determines whether the result of the optimization calculation in the optimization unit 205 satisfies the convergence determination condition.
In this embodiment, the slab group creation unit 202 or the slab group redefinition unit 207 described later creates one of the cast candidates j (cast pieces) corresponding to the decision variable x _j given “1”. If all (latest) slab groups are included one by one, it is determined that the result of optimization calculation in optimization unit 205 satisfies the convergence judgment condition, otherwise the optimization unit 205 It is determined that the result of the optimization calculation does not satisfy the convergence determination condition.

As a result of the determination, when the result of the optimization calculation in the optimization unit 205 satisfies the convergence determination condition, the determination unit 206 determines that the determination variable is given “1” in the result of the optimization calculation in the optimization unit 205. Determine cast candidate j (cast piece) corresponding to x _j as a cast. Then, the determination unit 206 activates the output unit 208.
On the other hand, when the result of the optimization calculation in the optimization unit 205 does not satisfy the convergence determination condition, the determination unit 206 activates the slab group redefinition unit 207.

<Slab Group Redefinition Unit 207>
The slab group redefinition unit 207 determines a plurality of slabs included in the cast candidate j (cast piece) corresponding to the decision variable x _j given "1" as the (latest) result of the optimization calculation in the optimization unit 205. Redefining the group as one slab group is performed individually for each of the cast candidates j (cast pieces).
FIG. 9 is a diagram showing an example of slab group information 900 after the slab group has been redefined.
In FIG. 9, in the slab group information 500 shown in FIG. The slab groups of “1” and “2” are included in the first cast piece, and slab group No. The slab group of "3" and "4" is included in the second cast piece, and slab group No. The case where the slab group of "5" is included in the third cast piece is shown as an example.

  Slab group No. 1 included in the first cast piece. The slab group information 500 shown in FIG. 5 and the slab group information 900 shown in FIG. 9 will be exemplified taking the case of redefining a new slab group by combining the slab groups “1” and “2” into one. Explain the relationship with

The slab group redefinition unit 207 has a slab group No. From the maximum value and the minimum value of the slab width of the slab group of “1” and “2”, 1700 and 1500 are derived as the maximum value and the minimum value of the slab width of the new slab group, respectively.
The slab group redefinition unit 207 has a slab group No. From the maximum value and the minimum value of the slab thickness of the slab group of “1” and “2”, 250 and 250 are derived as the maximum value and the minimum value of the slab thickness of the new slab group, respectively.

The slab group redefinition unit 207 has a slab group No. Adds the slab weights of the “1” and “2” slab groups to derive 736.4 (= 185.3 + 551.1) as the slab weight of the new slab group.
The slab group redefinition unit 207 has a slab group No. Add the coil lengths of the slab groups “1” and “2” to derive 62.2 (= 25.4 + 36.8) as the coil length of the new slab group.

The slab group redefinition unit 207 has a slab group No. Are the earliest and latest dates of the desired rolling date of the slab group “1” and “2”, and the earliest and latest dates of the desired rolling date of the new slab group on June 1 and June 2 respectively Derive the day.
The slab group redefinition unit 207 has a slab group No. The number of slabs in the slab group of “1” and “2” is added, and 37 (= 9 + 28) is derived as the number of slabs in the new slab group.
The slab group No. Since the material of the slab group of “1” and “2” is “A”, it is not changed.

  The slab group redefinition unit 207 sets the slab group No. of the slab group information 500 shown in FIG. A slab group consisting of the material derived as described above, slab width, slab thickness, slab weight, coil length, desired hot rolling date, and number of slabs, with the slab groups of “1” and “2” deleted A slab group No. 1 is newly generated and a slab group No. Is assigned to the slab group (record). In FIG. 9, the slab group no. Slab group No. of the new slab group redefined from the slab groups “1” and “2”. The case where "1" is given as is shown as an example.

  The slab group No. included in the second cast piece. Similarly, new slab groups are obtained for the slab groups of "3" and "4". In FIG. 9, the slab group No. of slab group information 500 shown in FIG. Slab group No. of the new slab group redefined from the slab groups “3” and “4”. The case where “2” is given as is shown as an example.

  The slab group included in the third cast piece is the slab group No. There are only 5 slab groups. Therefore, slab group No. For a slab group having a “5”, the slab group No. Only changes. In FIG. 9, the slab group No. of slab group information 500 shown in FIG. New slab group No. of the new slab group redefined from the slab group of “5” The case where "3" is given as is shown as an example.

  Also, the slab group No. of slab group information 500 shown in FIG. Also for slab groups “6” to “10”, slab group no. Only changes. In FIG. 9, the slab group No. of slab group information 500 shown in FIG. Slab group No. of the new slab group redefined from slab groups “6” to “10”. The case where "4"-"8" are provided as is shown as an example.

  Then, the slab group selection unit 203 selects some slab groups from the slab group information 900 including the slab groups redefined as described above, and the cast candidate derivation unit 204 selects the selected slab groups from the selected slab groups. , The cast candidate is derived, and the optimization unit 205 derives a cast piece from the cast candidate, and the determination unit 206 determines whether the result of the optimization calculation in the optimization unit 205 satisfies the convergence determination condition or not. If it is determined that the result of the optimization calculation in the optimization unit 205 does not satisfy the convergence determination condition, the slab group redefinition unit 207 determines a plurality of slab groups included in the cast piece obtained by the optimization unit 205. Redefine as a single slab group. The above process is repeated until the determination unit 206 determines that the result of the optimization calculation in the optimization unit 205 satisfies the convergence determination condition. Here, when the number of selected slab groups per one is “5”, when the slab group selection unit 203 selects the second and subsequent slab groups, one row of slab group information 900 shown in FIG. Select a slab group on the fifth to fifth rows.

  By doing this, as shown in FIG. 1, every time optimization calculation is performed in the optimization unit 205, cast pieces 101a to 101d → cast pieces 101e to 101h → cast pieces 101i to 101m. The size of the piece gradually increases, and finally the cast pieces 101n to 101r become casts.

  If it is attempted to derive casts from all of the slab groups included in the slab group information 500 at one time, the number of combinations of slab groups becomes enormous (ie, the problem scale becomes large), and casts can be performed within a practical calculation time It is not easy to derive. On the other hand, in the present embodiment, a part of the slab group included in the slab group information 500 is selected to derive a cast piece, and the slab group included in the derived cast piece is redefined as one slab group. The size of the cast piece is gradually expanded by repeating taking out the unselected slab group and deriving the cast piece again. Therefore, even for a large-scale problem, it is possible to derive a cast plan with practical calculation time. In addition, since the slab group is sorted in advance by important items in operation and the generation of cast pieces and the redefinition of the slab group are repeated in the sorted order, the variation of the items emphasized in the generated cast pieces is suppressed It is possible to minimize the decrease in accuracy due to dividing the problem and finding the solution as described above.

<Output unit 208>
When it is determined by the determination unit 206 that the result of the optimization calculation in the optimization unit 205 satisfies the convergence determination condition, the output unit 208 determines that the cast included in each cast is a cast plan when the cast is determined. Output as planning result. The output unit 208 performs information on slabs included in each cast, for example, by performing at least one of display on a computer display, transmission to an external device, and storage on an internal or external storage medium. Output For example, as the item of the slab information 300 shown in FIG. The added information can be output as information on slabs included in each cast.

  As shown in the equation (3), in the present embodiment, there may be slab groups not included in the cast. The output unit 208 can also output information on such slab groups. In this way, the operator can instruct by operation of the cast formation device 200 which cast of the cast plan derived as described above to incorporate the slab group (slab included therein). Thereby, the cast organization device 200 can include the slab group in the instructed cast. In addition, the slab group may be included in the cast plan in the next planning target period without doing so.

(flowchart)
An example of the process of the cast knitting device 200 will be described with reference to the flowchart of FIG.
First, in step S1001, the slab information acquisition unit 201 acquires slab information 300 (see FIG. 3).
Next, in step S1002, the slab group creation unit 202 groups slabs included in the slab information 300 acquired in step S1001 to create a slab group, and derives slab group information 500 (see FIG. 5). reference). The details of step S1002 will be described later with reference to FIG.

Next, in step S1003, the slab group selection unit 203 sorts slab groups (records) of the slab group information 500 created in step S1002 in order from the slab group with the earliest earliest hot spread desired date. From 600 (see FIG. 6), a predetermined number of slab groups are selected in order from the earliest to the desired hot-rolling date, in order from the earliest.
Next, in step S1004, the cast candidate derivation unit 204 derives, from the slab group selected in step S1003, one that satisfies all the constraint conditions (A2) to (E2) described above as a cast candidate.

Next, in step S1005, the optimization unit 205 determines the decision variable x _j when minimizing the value of the objective function f in equation (1) within the range satisfying the constraint equations in equations (2) and (3). Deriving the cast piece by deriving.
Next, in step S1006, the determination unit 206 determines whether the result of the optimization calculation in step S1005 satisfies the convergence determination condition.

As a result of the determination, when the result of the optimization calculation does not satisfy the convergence determination condition (step S1006: no), the process proceeds to step S1007. In step S1007, the slab group redefinition unit 207 determines a plurality of slabs included in the cast candidate j (cast piece) corresponding to the decision variable x _j given "1" as a result of the optimization calculation of step S1005 Redefinition of the group as one slab group is performed individually for each of cast candidates j (cast pieces), the slab group is redefined, and slab group information 900 is generated (see FIG. 9). Then, the process returns to step S1003, and the process of step S1003 is performed using the slab group information 900 of the redefined slab group.

  In step S1006, it is determined that the result of the optimization calculation satisfies the convergence determination condition, and when the cast is determined (step S1006: yes), the process proceeds to step S1008. In step S1008, the output unit 208 outputs slab information included in each cast as a casting plan planning result. Then, the process according to the flowchart of FIG. 10 is ended.

FIG. 11 is a flowchart illustrating an example of the process of step S1002 in FIG.
First, in step S1101, the slab group creation unit 202 rearranges the slabs (records) included in the slab information 300 acquired in step S1001 of FIG. See Slab Information 400).
Next, in step S1102, the slab group creation unit 202 sets “0 (zero)” in the variable u specifying the row of the rearranged slab information 400.

Next, in step S1103, the slab group creation unit 202 adds “1” to the variable u specifying the row of the rearranged slab information 400.
Next, in step S1104, whether or not the slab group creation unit 202 can add a slab group registered in the u-th row of the rearranged slab information 400 among the already created slab groups? It is determined whether or not. As described above, in the present embodiment, when all determination conditions (A1) to (D1) are satisfied, it is determined that there is a slab group to which the selected slab can be added among the already created slab groups. .

  As a result of this determination, if there is no slab group to which a slab registered in the u-th row of the sorted slab information 400 can be added among the already created slab groups (step S1104: no), The process advances to step S1105. In step S1105, the slab group creation unit 202 creates a new slab group, and includes the slab registered in the u-th row of the sorted slab information 400 in the created new slab group. Then, the process proceeds to step S1108 described later.

  On the other hand, if it is determined in step S1104 that there is a slab group to which the slab registered in the u-th row of the rearranged slab information 400 can be added (step S1104: yes), the process proceeds to step S1106. In step S1106, the slab group creation unit 202 determines the number of slabs included in the slab group determined to be added in step S1104 and the number of slabs registered in the u-th row of the rearranged slab information 400. It is determined whether the sum is less than or equal to the upper limit value.

  If it is determined that the total number of slabs is equal to or less than the upper limit (step S1106: yes), the process proceeds to step S1107. In step S1107, the slab group creation unit 202 includes the slab registered in the u-th row of the sorted slab information 400 in the slab group determined to be able to add the slab in step S1104. Then, the process proceeds to step S1108 described later.

  On the other hand, if it is determined in step S1106 that the total number of slabs is not less than or equal to the upper limit (step S1106: no), the process proceeds to step S1105 described above. As described above, in step S1105, the slab group creation unit 202 creates a new slab group, and registers the slab registered in the u-th row of the rearranged slab information 400 into the created new slab group. include. Then, the process proceeds to step S1108.

  In step S1108, the slab group creation unit 202 determines whether all slabs included in the rearranged slab information 400 have been allocated to any slab group. As a result of the determination, when all the slabs included in the rearranged slab information 400 are not allocated to any slab group (step S1108: no), the process returns to step S1103. Then, the processing of steps S1103 to S1108 is repeated until all the slabs included in the rearranged slab information 400 are allocated to any slab group (step S1108; yes).

  When all the slabs contained in the slab information 400 rearranged as described above are assigned to any slab group, slab group information 500 is obtained (see FIG. 5). Then, in step S1109, the slab group creation unit 202 outputs the slab group information 500 to the slab group selection unit 203. Then, the process proceeds to step S1003 in FIG.

(Example of calculation)
Next, a calculation example will be described.
In this calculation example, cast plans were created for each of the six cases with the number of slabs “50”, “100”, “150”, “200”, “250”, and “300”. In the application example, 15 parts of a slab group are selected at a time, a cast candidate is derived, optimization calculations are performed, and an optimal cast candidate (cast piece) is repeatedly derived to create a cast plan. . On the other hand, in the non-application example, it is a method of selecting all slab groups created in step S1002 in step S1003 of FIG. Created the best cast plan. In the applied example and the non-applied example, the set division problem was adopted as the optimization problem, the equation (1) was used as the objective function, and the equations (2) and (3) were used as the constraint equation. In addition, the evaluation value c _j is represented by equation (4). Here, the values of the weighting factors C _W , C _T , C _D , C _N and C _S are respectively set to “0”, “0”, “1”, “100” and “10” (C _W = 0, C _T = 0, C _D = 1, C _N = 100, C _S = 10).

FIG. 12 is a table showing the results of application examples and non-application examples of the present embodiment. FIG. 13 is a diagram showing the relationship between the number of slabs shown in FIG. 12 and the calculation time. FIG. 14 is a diagram showing evaluation values in each of the six cases described above.
In FIG. Is a number identifying the six cases described above. Case No. 1 shown in FIG. And Case No. shown in FIG. Is the same thing.
The draft target slab number is the number of slabs to be cast target. As described above, in the present calculation example, six cases of “50”, “100”, “150”, “200”, “250”, and “300” are considered, and these are the number of draft target slabs.
The number of slab groups is the number of slab groups obtained by grouping slabs to be cast planning targets by the process of the slab group creation unit 202.

  The number of casts is the number of casts finally obtained. Further, the number of slabs is the number of slabs included in any of the casts finally obtained. In this calculation example, as described in the present embodiment, the optimization calculation is ended when all of the slab groups are included in any cast (cast piece). Therefore, in FIG. 12, the original draft target slab number and the number of slabs become the same.

The number of iterations is the number of iterations of the optimization calculation. The number of iterations of the optimization calculation is the same as the number of executions of step S1006 in FIG. In the non-application example, the number of optimization calculations is “1” in any case (ie, the optimization calculation is performed only once).
The computation time is the time taken to obtain a cast plan.

As shown in FIGS. 12 and 13, in the application example of the present embodiment, the calculation time can be set to about one second. On the other hand, in the non-application example of the present embodiment, the calculation time is several tens of seconds to several hundreds of seconds. Further, as shown in FIG. 14, in the application example of the present embodiment, the evaluation value is not significantly deteriorated compared to the non-application example of the present embodiment. In this calculation example, since the value of the objective function f in the equation (1) is minimized, the smaller the evaluation value, the better the result.
As described above, compared with the non-application example, the application example of the present embodiment can obtain a cast plan with a calculation time of about one second without significantly deteriorating the evaluation value. It takes about several minutes as the waiting time for the optimization calculation time in the operator. Therefore, according to the method described in the present embodiment, it is possible to create and output a cast plan having practically sufficient accuracy within such a waiting time.

(Summary)
As described above, in the present embodiment, among the slabs included in the slab information 300, the slabs having the same manufacturing condition within the predetermined range (slabs satisfying all determination conditions of (A1) to (D1)) are the same. The slabs included in the slab information 300 are grouped so as to belong to the slab group. Therefore, even when there are many slabs for which cast plans are to be made, it is possible to suppress an increase in calculation time.

Further, in the present embodiment, some of the plurality of slab groups are selected in order from the earliest to the desired hot-rolling date, in order from the earliest. Then, among the selected slab groups, those that satisfy the conditions (all of the constraints of (A2) to (E2)) that can be included in the same cast are derived as cast candidates. Objective function f having as variables the decision variable x _j which is a variable indicating whether or not to adopt a cast candidate (as a cast piece or cast), and an evaluation value c _i represented using an evaluation index including the number of casts The determination variable x _j which minimizes X is derived by performing optimization calculation. As a constraint in the optimization calculation, the number of slab groups included in the optimal cast candidate is the same as the number of slab groups selected prior to deriving the cast candidate, and the same slab group is Adopt the condition that it is not included in different cast candidates. Slab groups included in cast candidates adopted by such optimization calculation are put together as a new slab group (ie, the slab group is redefined). The above process is repeated until the result of the optimization calculation converges.

  As described above, since a part of the slab group is taken in and the size of the cast piece is gradually enlarged to create the final cast plan, it is possible to derive the cast from the slab group at one time in comparison with the case of creating the cast plan The cast plan can be derived with a practical calculation time, and the calculation time required to create a cast plan can be shortened. Also, since the slab groups are sorted in advance according to important items in operation, such as the earliest date of the desired hot-rolling day, and the cast pieces are generated in the sorted order and the slab group is redefined. It is possible to suppress the variation in the items emphasized, and to minimize the reduction in accuracy due to the problem of cast organization being divided and a solution being sought.

Further, in the present embodiment, the constraint in the optimization calculation is made different depending on whether the number of slabs included in the slab group is equal to or more than the upper limit value H _MAX .
That is, the following restrictions are imposed on a slab group in which the number of contained slabs is equal to or more than the upper limit value H _MAX . That is, for slab groups having the same content, a constraint is imposed to make the number of slab groups included in the optimal cast candidate equal to the number of love groups selected prior to deriving the cast candidate. Therefore, a slab group to which a large number of slabs belong is easily adopted as a cast piece. Thus, the calculation time can be further shortened.

On the other hand, the following restrictions are imposed on slab groups in which the number of contained slabs is not the upper limit value H _MAX or more. That is, for slab groups having the same content, a constraint is imposed to make the number of slab groups included in the optimal cast candidate equal to or less than the number of slab groups selected prior to deriving the cast candidate. This allows the slab group selected prior to deriving the cast candidate not to be included in the optimal cast candidate. Therefore, it is possible to suppress that a slab group to which a small number of slabs belong is included in the undesirable cast piece.

  Further, in the present embodiment, of the slab groups, those satisfying the constraints of (C2) coil length, (D2) width shift restriction, and (E2) same width number restriction are adopted as cast candidates. Thus, casting, which is a lot of steelmaking process, can be made equal to the chance of being a hot rolling process lot. Thus, for example, the slab can be prevented from staying in the yard for a long time before being hot-rolled in the hot rolling process. Thereby, for example, shortening of the manufacturing period and suppression of the fuel consumption rate of the heating furnace in the hot rolling process can be realized by lowering the temperature of the slab.

[Second embodiment]
Next, a second embodiment will be described. In the first embodiment, the case has been described by way of example in which it is assumed that slabs to be cast targets are all made of the same steel type. On the other hand, in the present embodiment, a case will be described in which there is a freedom in selecting a steel type (a steel type can be selected from a plurality of steel types) for at least one slab to be a cast plan. As described above, the present embodiment is mainly different from the first embodiment in the configuration and the processing due to the freedom of choice in the steel type of the slab. Therefore, in the description of the present embodiment, the same parts as those in the first embodiment are denoted by the same reference numerals as those in FIGS. 1 to 14, and the detailed description will be omitted.

  As described in the background art, in primary refining (converter) and secondary refining process (RH etc.) of a steelmaking plant, components are adjusted in units of ladle, so one charge (one ladle full) A collection of slabs of corresponding weight must be manufactured as slabs of the same steel type. Since the cast of multiple charges to be cast in succession is cast, in the problem of determining the configuration of slabs included in the cast and the steel type of each slab, the same steel type is manufactured when selecting the slabs constituting the cast. It is necessary to aggregate possible slabs and determine the steel grade on a charge basis. Here, the steel types that can be manufactured are determined by the component specifications (components of molten steel) defined for each charge.

  FIG. 15 is a view for explaining an example of the relationship between the steel type of the slab and the charge. As shown in FIG. 15, it is assumed that ranges of double-arrow lines shown in FIG. 15 are defined as the range of the component a in each of the steel types I, II, and III. In this case, although charge 1 can be manufactured as steel type I or steel type II that falls within the range of component a included in charge 1, charge 1 can not be manufactured as steel type III that does not fall within this range.

As described above, the types of steel that can be manufactured are determined by the component specifications determined for each charge. Further, depending on the component standard, there may be a case where a plurality of steel types that can be manufactured for one slab are set. In addition, for example, when the component range of the molten steel is narrow and it is a steel type in which adjustment of the component is difficult, or when it is necessary to put an alloy into the molten steel to adjust the component value of the molten steel, the manufacturing cost increases. . As described above, since the manufacturing cost varies depending on the type of steel to be manufactured, there is a demand for manufacturing as inexpensively as possible within the range satisfying the component specification.
Based on the above, an example of a cast knitting device of the present embodiment will be described below.

<Slab Information Acquisition Unit 201>
In addition to the information included in the slab information 300 illustrated in FIG. 3, the slab information acquisition unit 201 acquires and stores information on steel types that can manufacture each slab as slab information. FIG. 16 is a diagram showing an example of slab information 1600 acquired by the slab information acquisition unit 201 in the present embodiment.

As shown in FIG. , Material, slab weight, slab width, slab thickness, coil width, coil thickness, coil length, and desired date of hot rolling, and steel types (steel types I, II, III) that can manufacture the slab are associated with each other Information. In the example shown in FIG. 16, each slab shows that manufacture with the steel type shown by "(circle)" is possible. For example, slab No. In the case of slabs having a “1”, production is possible only with steel type I, while slabs no. The “2” slab can be manufactured with either steel type I or steel type II. Thus, the material, slab weight, slab width, slab thickness, coil width, coil thickness, coil length, and desired hot-rolling date have no freedom of choice for given slab information 1600. On the other hand, steel types that can be manufactured may or may not have freedom of choice for given slab information 1600.
The slab information 1600 includes, in addition to the types of steel that can be manufactured (types I, II, and III), the manufacturing cost (not shown) when manufacturing a slab of the type of steel.

<Slab Group Creation Unit 202>
Based on the slab information 1600 acquired by the slab information acquisition unit 201, the slab group creation unit 202 is included in the slab information 1600 so that slabs whose manufacturing conditions coincide within a predetermined range belong to the same slab group. A slab group is created by grouping each of the slabs. In the present embodiment, the slab group creation unit 202 is already created when the following determination condition (E1) is satisfied in addition to the determination conditions (A1) to (D1) described in the first embodiment. It is determined that there is a slab group to which a slab selected from the slab information can be added among the existing slab groups. As described in the first embodiment, the slab information is obtained by rearranging the slabs (records) of the slab information 1600 shown in FIG. Slab information 400)).

(E1) At least one of the producible steel types of the slabs included in the already created slab group and the producible steel types of the slab selected from the slab information overlap.
For example, slab No. 1 in FIG. And the slab No. 1 and the slab No. In the case where a slab of “4” is included in the same slab group, a steel grade that overlaps as a producible steel grade is steel grade I. Therefore, slab No. Although the slabs with "5" can be added to the corresponding slab group, slab No. A slab with "6" can not be added to the slab group.
The processing of the other slab group creation unit 202 is the same as the processing described in the first embodiment. The slab group creation unit 202 may create the slab group in the same manner as in the first embodiment (ie, in consideration of the determination conditions of (A1) to (D1), the determination condition of (E1) You do not have to consider

<Cast candidate derivation unit 204>
The cast candidate derivation unit 204 derives cast candidates from the slab group selected by the slab group selection unit 203.
FIG. 17 is a flowchart for explaining an example of processing of the cast candidate derivation unit 204. The process of the flowchart of FIG. 17 is, for example, a process replacing the process of step S1004 of FIG. 10 described in the first embodiment. An example of the process of the cast candidate derivation unit 204 of the present embodiment will be described with reference to the flowchart of FIG.

In step S1701, the cast candidate derivation unit 204 enumerates all possible combinations (subsets) of slab groups from the set of slab groups selected by the slab group selection unit 203. This process is the same as in the first embodiment.
Next, in step S1702, the cast candidate derivation unit 204 satisfies all the constraint conditions (A2) to (E2) described in the first embodiment among the listed combinations (subsets) of slab groups. Extract

  In the first embodiment, a cast candidate satisfying all the constraints (A2) to (E2) is a cast candidate. However, in the embodiment, in addition to the constraints (A2) to (E2), The cast candidate is one that satisfies the constraint condition of (F2).

(F2) Width transition restriction in steel type When the slabs contained in the combination (partial set) of slab groups are divided by steel type and the slabs divided by steel type are sorted such that the width of the slab is in ascending order or descending order In each steel type, the difference in width between adjacent slabs is equal to or less than the upper limit value.
For example, 100 [mm] can be adopted as the upper limit value.

  While "(D2) width transition constraint" described in the first embodiment is a constraint on the transition of the slab width during rolling, "(F2) width transition constraint in steel type" is during casting It is a restriction on the transition of the width of the slab for each steel type. The "(F2) width transition constraint in steel type" is a constraint that is required because the width of the mold in the continuous casting machine can not be changed rapidly.

  In order to determine the satisfaction of “(F2) Width transition constraint in steel type”, it is necessary to determine an appropriate steel type out of producible steel types for each slab included in the combination (partial set) of slab groups. You must. However, as shown in FIG. 16, there is a slab whose steel type that can be manufactured is not uniquely determined (for example, a slab having a slab No. “2”). The problem of determining the steel type so as to satisfy “(F2) width transition constraint in steel type” can be regarded as an optimization problem. Therefore, in the present embodiment, an appropriate steel type is determined from the producible steel types as in the processing in step S1703 and subsequent steps, and the steel type of each slab is the determined steel type, “(F2 ) Determine the satisfaction of the width transition constraint in the steel type.

First, in step S1703, the cast candidate derivation unit 204 selects one unselected combination among the combination (subset) of slab groups extracted in step S1702.
Next, in step S1704, the cast candidate derivation unit 204 selects the steel type of each slab included in the combination (partial set) of slab groups selected in step S1703 with the lowest manufacturing cost among the steel types that can manufacture the slab. Temporarily determine the appropriate steel grade. For example, in FIG. 16, when the production cost of steel type I is lower than that of steel type II, slab No. 1 is produced. The steel type I is tentatively determined for the slab with "2". For a slab with one steel type option, the steel type is tentatively determined for the slab. For example, in FIG. The steel type II is tentatively determined for the slab of which is “6”.

Next, in step S1705, cast candidate derivation unit 204 sets a plurality of steel types as producible steel types in slab information 1600 among the slabs included in the combination (partial set) of slab groups selected in step S1703 Extract the existing slabs. In the example shown in FIG. The slabs of “2”, “3”, “5”, “8” and “10” to “12” are extracted. The number of slabs extracted in this step S1705 is N _s .
Next, in step S1706, the cast candidate derivation unit 204 sorts the N _s slabs extracted in step S1705 in ascending or descending order according to the manufacturing cost. As the manufacturing cost at this time, the manufacturing cost for the steel type temporarily determined in step S1704 is used.

Next, in step S1707, the cast candidate derivation unit 204 selects one unselected slab s from the N _s slabs extracted in step S1705 in the order sorted in step S1706.
Next, in step S1708, the cast candidate derivation unit 204 calculates an evaluation value c _k for the steel type k temporarily determined in step S1704 as the steel type k of the slab s selected in step S1707.

Here, the evaluation value c _k will be described.
In the present embodiment, the steel grade of each slab is determined using the following four evaluation indexes (A3) to (D3).
(A3) Number of steel types When a plurality of different steel types coexist in the same cast, a mixed portion of molten steel is generated in the tundish between charges at which the steel types are switched. For this reason, when the component of the mixing part does not satisfy the requirement for the slab, a part of the slab is cut and discarded. Therefore, since the yield decreases when referring to the operation by different types of steel continuously cast (hereinafter the same), it is desirable that the number of different types of steel in the same cast be as small as possible. The term "different steel types" means continuous casting of different steel types continuously. Therefore, it is desirable that the number of steel types of the slabs included in the combination (subset) of the slab groups is small.

(B3) Amount of excess material The excess material is a product to be produced in the lot when the order is assigned to the lot whose production amount is fixed but it does not meet the production amount. A product that is undecided whether it can be tied to an order. The amount of excess material means the amount of such products.
Since the preparation of the components of the molten steel is referred to in the converter and the secondary refining process, the steel grade is manufactured on a charge basis. Therefore, when the total weight of the slab assigned one steel grade in one cast is less than the charge weight (the weight of one charge (the weight of molten steel put in one ladle)), it is manufactured as a surplus material not related to the order Be done. For example, if the charge weight is 300 ton, and the total weight of the slab to which one steel grade is allocated in one cast is 1000 ton, the amount of surplus material is 1000 ton divided by 300 ton. It becomes 100 [ton] which is the remainder. Since the excess material stays in the yard as a slab until it is linked to an order, it causes an increase in in-process inventory. For this reason, it is desirable that the total weight of the slab assigned a certain steel grade be close to (preferably matched to) the charge weight so as to reduce the amount of excess material as much as possible.

(C3) Manufacturing cost As described above, since it is necessary to accurately adjust the component range and to input the alloy, more steel types having a narrow component range and steel types that require the input of the alloy are manufactured more It costs money. For this reason, when there is a choice of steel grade for a certain slab, it is preferable to manufacture it as the lowest possible steel grade.
However, it is possible to consolidate steel types by manufacturing low cost steel types that are inexpensive to manufacture as more expensive steel types, and it may be possible to reduce the number of steel types and the amount of remaining material. On the other hand, consolidating lower-grade steel types that are inexpensive to manufacture into expensive steel types exacerbate manufacturing costs. Therefore, “(A3) number of steel types” and “(B3) amount of remaining material” and “(C3) manufacturing cost” are in a trade-off relationship.

(D3) Improvement amount of width transition restriction in steel type As described above, in order to enable continuous casting in a continuous casting machine, the aforementioned “width transition restriction in steel type” in each steel type is eliminated. It is desirable to change the steel grade.

In the present embodiment, the evaluation value c _k for the steel type k is represented by the following equation (6) using the evaluation indexes of (A3) to (D3) above.
c _k = W _W -W _N -W _Y -W _P (6)
In the equation (6), W _W is the improvement amount of violation of internal transition of the steel grade. If the violation of the width transition restriction in steel type k (the above-mentioned “(F2) Width transition restriction in steel type”) violation is resolved by changing the steel type k of the slab s, the intra-steel type width transition restriction violation improvement amount W _W Is a number C _W (W _W = C _W (> 0)) having a preset positive value. If there is no width transition constraint violation in the steel type k both before and after the change of the steel type k of the slab s, or if the width transition constraint violation in the steel type k does not disappear even if the steel type k of the slab s is changed The steel type internal width transition restriction violation improvement amount W _{W is set} to 0 (W _W = 0).

W _N is the cost of steel type. Assuming that the number of steel types of slabs included in the combination (partial set) of slab groups selected in step S 1703 is N _k and the weighting coefficient for N _k is C _N , _where the steel type of slab s is steel type _k , steel types The number cost W _N is represented by the product of these (W _N = N _k × C _N ).
W _Y is the amount of excess material cost. Assuming that the steel type of slab s is steel type k, the total weight of the slabs of steel type k included in the combination (partial assembly) of slab groups selected in step S1703 is Q _k and the charge weight is Q _ch , the total weight In order to manufacture a slab of Q _k , it is necessary to charge n _ch (= integer part of the value of Q _k ÷ Q _ch + 1) cups. Then, the amount of remaining material Q _Y is n _ch × Q _ch −Q _k . Then, excess material amount cost W _Y is a surplus material amount Q _Y, represented by the product of the weight coefficient C _Y for _{Q Y (W Y = Q Y} × C Y).

W _P is the manufacturing cost. Assuming that the integrated value of the manufacturing cost for each steel type is E _k and the weight coefficient for E _k is C _P , the manufacturing cost W _P when the steel type of the slab s is steel type k is the product of these (W _P = E _k × C _P ).
The weighting factors C _N , C _Y , and C _P are preset according to how much each evaluation item is emphasized, and represent the balance of evaluation among the evaluation items.

In step S1708, the cast candidate derivation unit 204 calculates the evaluation value c _k for the steel type k temporarily determined in step S1704 of the slab s selected in step S1707, using the above equation (6).

  Next, in step S1709, the cast candidate derivation unit 204 is a steel type that can manufacture the slab s as the steel type k of the slab s selected in step S1707, and is not selected other than the steel types temporarily determined in step S1704. Select one steel grade. For example, the cast candidate derivation unit 204 can select one steel type according to any predetermined order (for example, the lowest cost or expensive steel type in manufacturing cost) among such unselected steel types. The steel type that can produce the slab s can be identified from the slab information 1600.

Next, in step S1710, the cast candidate derivation unit 204 calculates an evaluation value c _k for the steel type k selected in step S1709 as the steel type k of the slab s selected in step S1707 by equation (6).
Next, in step S1711, the cast candidate derivation unit 204 is a steel type that can manufacture the slab s as the steel type k of the slab s selected in step S1707, and all steel types other than the steel types temporarily determined in step S1704. It is determined whether or not it has been selected. As a result of this determination, when all the steel types have not been selected (step S1711: no), the process returns to step S1709. Then, the processes in steps S1709 to S1711 are repeatedly performed until the evaluation values _ck for all the steel types that can produce the slab s are calculated as the steel type k of the slab s selected in step S1707.

When the evaluation value _ck for all steel types that can produce the slab s is calculated as the steel type k of the slab s selected in step S1707 (step S1711: yes), the process proceeds to step S1712.
In step S1712, the cast candidate derivation unit 204 extracts the evaluation value c _k of the largest value among the evaluation values c _k for each steel type k of the slab s selected in step S1707. Then, the cast candidate derivation unit 204 determines the steel type k used when obtaining the extracted evaluation value c _k as the steel type k of the slab s selected in step S1707.

Next, in step S1713, the cast candidate derivation unit 204 determines whether all of the N _s slabs s extracted in step S1705 have been selected. As a result of this determination, when all the slabs have not been selected (step S1713: no), the process returns to step S1707. Then, the processes of steps S1707 to S1713 are repeated until the steel types k of the N _s slabs s extracted in step S1705 are determined.

When determining the steel grade k of N _s number of slab s extracted in step S1705 (step S1713: yes), the process proceeds to step S1714.
In step S1714, the cast candidate derivation unit 204 determines whether or not each slab included in the combination (partial set) of slab groups selected in step S1703 satisfies the above-described “(F2) width shift constraint in steel type”. Determine if For this determination, the steel type k determined in step S1712 is used for each slab s included in the combination (partial set) of slab groups selected in step S1703.

  As a result of this determination, if “(F2) Width transition restriction in steel type” is satisfied (step S1714: yes), the process proceeds to step S1715, and the cast candidate derivation unit 204 combines the slab groups selected in step S1703 ( Subset) is adopted as a cast candidate. Then, the process proceeds to step S1717. On the other hand, if “(F2) width transition constraint in steel type” is not satisfied (step S1714: no), the process proceeds to step S1716, and the cast candidate derivation unit 204 combines the slab groups selected in step S1703 (partial set Do not use) as a cast candidate. Then, the process proceeds to step S1717.

  In step S1717, the cast candidate derivation unit 204 determines whether all combinations (subsets) of slab groups extracted in step S1702 have been selected. If the combination (subset) of all slab groups is not selected as a result of this determination (step S1717: no), the process returns to step S1703. Then, the process of steps S1703 to S1717 is repeatedly performed until it is determined whether or not to adopt as a cast candidate for the combination (subset) of all the slab groups. Then, when it is determined whether or not to adopt as a cast candidate for all the slab group combinations (subsets) (step S1717: yes), the processing according to the flowchart of FIG. 17 ends, and the process proceeds to step S1005 of FIG.

<Optimization unit 205>
The optimization unit 205 derives an optimal combination of cast candidates (cast pieces) from the cast candidates derived by the cast candidate derivation unit 204 by solving an optimization problem.
In this embodiment, the evaluation value c _j of the cast candidate _j is represented by the following equation (7), instead of the equation (4) described in the first embodiment.
c _j = C _W × W + C _T × T + C _D × D + C _N- C _S × S + C _n × n + C _y × y + C _p × p (7)

The first to fifth terms on the right side of the equation (7) are the same as the equation (4).
n is the total number of steel types of slabs included in the cast candidate j. C _n is a weighting factor for n.
y is the sum of the amount of excess material in the cast candidate j. C _y is a weighting factor for y.

p is the sum of the manufacturing costs of the slabs included in the cast candidate j. C _p is a weighting factor for p.
The weighting factors C _W , C _T , C _D , C _N , C _S , C _n , C _y , and C _p are preset according to how much each evaluation item is emphasized, and between each evaluation item Represents the balance of the evaluation of
The processing of the other optimization units 205 is the same as the processing described in the first embodiment.

(Example of calculation)
Next, a calculation example will be described.
In this calculation example, for each of nine cases of the number of slabs “50”, “100”, “150”, “200”, “250”, “300”, “350”, “400” and “450” Created a cast plan. In any case, a slab capable of selecting any of a plurality of steel types and a slab in which only one steel type is designated is included, and a slab capable of selecting any of a plurality of steel types is the present embodiment. The steel type was determined as described in the reference embodiment. In the application example of the present embodiment, it is repeated to select 20 partial slab groups at a time, derive cast candidates, perform optimization calculation, and derive optimal cast candidates (cast pieces), and cast I made a plan. On the other hand, in the non-application example of the present embodiment, the method of selecting all slab groups created in step S1002 in step S1003 of FIG. 10 does not pass from the branch of step S1006 through step S1007. The optimal cast plan was created by optimization calculation.

In the applied example and the non-applied example of this embodiment, the set division problem is adopted as the optimization problem, the equation (1) is used as the objective function, and the equations (2) and (3) are used as the constraint equation. In addition, the evaluation value c _j is represented by equation (7). Here, the values of the weighting factors C _W , C _T , C _D , C _N , C _S , C _n , C _y , and C _p are “0”, “0”, “1”, “100”, “10,” respectively. “1”, “1”, “1” (C _W = 0, C _T = 0, C _D = 1, C _N = 100, C _S = 10, C _n = 1, C _y = 1 , C _p = 1).

FIG. 18 is a table showing the results of application examples and non-application examples of the present embodiment.
In FIG. 18, in the non-application example of the present embodiment, when the number of slabs is 200 or more, the calculation time exceeds 1 hour, and the cast organization result can not be obtained within the practical calculation time. I cut it off. On the other hand, in the application example of the present embodiment, a cast formation result is obtained within a practical calculation time of 10 minutes or less in all cases.

Therefore, since several minutes are allowed as the waiting time for the optimization calculation time in the operator, it is possible to obtain an optimal cast organization result with sufficiently possible waiting time by the calculation method described in this embodiment.
Also, as a result of assigning the steel type to each slab, (A2) to (A2) can be obtained by registering only the combination (partial set) of slab groups that satisfy the aforementioned “(F2) width transition constraint in steel type”. It is possible to obtain cast formation results that satisfy all the constraints of F2).

(Summary)
As described above, in the present embodiment, the slabs included in the slab information 1600 are grouped such that the slab satisfying the determination condition of (E1) in addition to (A1) to (D1) belongs to the same slab group. . In addition, some of the plurality of slab groups are selected in order from the earliest hot spread request date, and in addition to the constraints (A2) to (E2) of the selected slab groups, (F2) Deriving cast candidates that satisfy the constraints of Then, in addition to the difference between the maximum value and the minimum value of the width of the slab, the difference between the maximum value and the minimum value of the thickness of the slab, the difference between the average value and the re-early date for the desired hot rolling of the slab, and The evaluation value of the cast candidate j is derived using the number of steel types, the amount of excess material, and the manufacturing cost as evaluation indexes. Therefore, in addition to the effects described in the first embodiment, the cast design is performed within the practical time, which adheres to the casting restrictions and includes the number of different steels, the amount of remaining material, and the manufacturing cost. Can be created by

Further, in the present embodiment, with regard to slabs that can be manufactured by a plurality of steel types, the amount of violation of the width transition constraint within the same steel type, the total number of steel types of slabs included in the combination (partial set) of slab groups, and the slab group One of the plurality of steel types is determined using the total amount of excess material in the combination (subset) and the manufacturing cost as an evaluation index. Therefore, the steel type of each slab can be appropriately determined according to these evaluation indexes.
Further, in this embodiment, a combination (subset) of the slab groups satisfying the constraint condition (F2) is searched from among the combination (subsets) of the slab groups satisfying the constraint conditions (A2) to (E2). . Therefore, the calculation time can be shortened as compared with the case where it is determined at one time whether or not the constraints (A2) to (F2) are satisfied.

  Further, in the present embodiment, as the steel type of each slab included in the combination (partial set) of the slab groups, a steel type having the lowest manufacturing cost among the steel types capable of manufacturing the slab is tentatively determined. Therefore, even in the method of the present embodiment which satisfies “(F2) width transition restriction in steel type”, it is possible to easily select a steel type having a low manufacturing cost as the steel type of each slab.

[Modification]
<Modification 1>
In the first and second embodiments described above, the case where the slabs (records) of the slab information 300 are rearranged in order from the slab with the earliest desired hot-rolling date has been described as an example. However, if the slabs (records) of the slab information 300 are rearranged based on the slab manufacturing conditions included in the slab information 300, this is not necessarily required. In the rolling process, although the operation is generally performed to roll from a wide slab to a narrow slab, the operation tends to be more stable as the variation of the width of the slab rolled back and forth is smaller. Therefore, for example, the slabs may be rearranged in order from the narrow slab (record). By doing this, the variation in slab width in the same cast is suppressed, and the operation is stabilized. In addition, about what is represented numerically like slab width, slab thickness, and slab weight, a slab (record) is rearranged so that the said numerical value may become descending order or ascending order, for example. On the other hand, for material represented by symbols, for example, the slabs (records) are rearranged so that the symbols are in ascending or descending order according to the dictionary order (alphabetical order or alphabetical order).
The above is the same for the case where the slab group (record) of the slab group information 500 is rearranged in the slab group selection unit 203.

<Modification 2>
In the first and second embodiments described above, when all the determination conditions (A1) to (D1) described above are satisfied, the slab selected from the slab information 400 is included in the already created slab group. The case of inclusion is described as an example. However, whether to include the slab selected from the slab information 400 in the already created slab group is determined based on the determination condition determined based on the slab manufacturing condition included in the slab information 300. If you do, you do not have to do this. For example, the condition (D1) may be omitted.

<Modification 3>
In the first and second embodiments described above, for each combination (subset) of the listed slab groups, only those satisfying all the constraints (A2) to (E2) described above are adopted as cast candidates. The case was described as an example. However, this is not necessarily required as long as a candidate that satisfies the condition (constraint condition) that can be included in the same cast is adopted as a cast candidate. For example, at least one of (B2) to (E2) may be employed.

<Modification 4>
In the first and second embodiments described above, the case where the number of slab groups selected by the slab group selection unit 203 is constant has been described as an example. However, the number of slab groups selected by the slab group selection unit 203 may be different. When the processes of steps S1003 to S1006 shown in FIG. 10 are repeated, the number of slabs belonging to the slab group is increased by the redefinition of the slab group. Then, cast candidates that do not satisfy the above-described constraints (A2) to (E2) are likely to be generated, so the number of cast candidates decreases, and the processing time per step S1003 to S1006 becomes short. It becomes a tendency. Therefore, the process shown in steps S1003 to S1007 shown in FIG. 10 may be repeated to increase the number of slab groups selected by the slab group selection unit 203 as the number of slabs included in the slab group increases. As a result, the reduction in the number of cast candidates derived in step S1004 is suppressed, and cast pieces are obtained as optimization results from among a larger number of combinations, and the number of times of repeated calculation of steps S1003 to S1007 until convergence is required. It becomes possible to suppress.

<Modification 5>
In the first and second embodiments described above, (C2) to (C2) can be used as constraints for causing slabs included in the same cast to be hot-rolled in the hot-rolling step as the same lot (chance). The constraint conditions of E2) have been described as an example. However, the constraints for causing the slabs included in the same cast to be hot-rolled in the hot rolling process as the same lot (chance) are at least one of the constraints (C2) to (E2). It should be included. For example, it is possible to determine which of the constraints (C2) to (E2) is to be adopted, depending on the slab or equipment to be subjected to the hot rolling.

<Modification 6>
In the first and second embodiments described above, the difference W between the maximum value and the minimum value of the slab width of the slab included in the cast candidate j, and the maximum value and the minimum value of the slab thickness of the slab included in the cast candidate j The case of using the difference T between T, the average value of the heat spread desired dates of slabs included in cast candidate j and the earliest date D, and the number of casts as an evaluation index of cast candidate j is described as an example. However, in the case of creating a production plan for products produced in lots, if the number of lots is evaluated, other evaluation indicators may be determined as appropriate. That is, in the example of the present embodiment, at least the number of casts may be included in the evaluation index. Also, in addition to or instead of the difference D between the average value of the heat spread desired days of the slabs included in the cast candidate j and the earliest date, the latest date and the earliest date of the heat spread desired dates of the slabs included in the cast candidate j You may use the difference with and as an evaluation index. Also, the material may be included in the evaluation index.

<Modification 7>
In the first and second embodiments described above, it is assumed that there is not a plurality of slab groups i having the same contents (that is, a plurality of slab groups i having the same combination of constituent slabs), the equations (2) and (3) The value of the right side of the constraint expression in the) expression was "1". However, for example, when there are a plurality of slab groups i having the same content, the values on the right side of the constraint equations of the equations (2) and (3) become the number. For example, when there are two slab groups i having the same contents, a constraint equation in which the value on the right side of the constraint equation of Equations (2) and (3) is “2” is given as the constraint equation for the slab group i. . By doing this, even if there are a plurality of slab groups i having the same content, it is possible to determine the slab group to be included in the optimal cast candidate according to the number of the belonging slabs.

<Modification 8>
In the first and second embodiments described above, the determination unit 206 sets the slab group creation unit 202 or any of the cast candidates j (cast pieces) corresponding to the decision variable x _j to which “1” is given. When it is determined that the result of the optimization calculation in the optimization unit 205 satisfies the convergence determination condition when all of the (latest) slab groups created in the slab group redefinition unit 207 are included one by one This is explained by taking the example as an example. However, the convergence determination condition is not limited to such.

  In order to determine the convergence of calculations even when so-called “set excesses” occur because it is impossible to take a slab into any cast piece, for example, a slab included in any of the cast pieces with respect to the total number of slab group i If the ratio of the total number of groups i (i.e., the rate of adoption of a slab group i to a cast piece) is equal to or more than a predetermined ratio, it may be determined that the convergence determination condition is satisfied. Also, when the ratio of the total number of slabs in the slab group i included in any of the cast pieces to the total number of slabs in the slab group i (that is, the adoption rate of slabs to the cast pieces) is a predetermined ratio or more It may be determined that the convergence determination condition is satisfied.

  In addition, the difference between the value of the objective function f obtained as a result of the previous optimization calculation and the value of the objective function f obtained as a result of the current optimization calculation is equal to or less than a predetermined value, or the previous optimization If the value of the objective function f obtained as a result of the calculation is the same as the value of the objective function f obtained as a result of the current optimization calculation, it may be determined that the convergence determination condition is satisfied.

<Modification 9>
When the number of slabs to be cast is large, it is preferable to create a slab group as described in the first and second embodiments. This is because the calculation time can be shortened as described above. However, for example, when the number of slabs to be cast is not large, the slab group may not be created.

<Modification 10>
In the first and second embodiments described above, when the upper limit value of the calculation time in one optimization calculation is provided and the calculation time in one optimization calculation becomes the upper limit, at that time The obtained solution may be regarded as an optimal cast candidate.

<Modification 11>
In the second embodiment described above, the evaluation value c _k for the steel type k is evaluated using the internal width transition restriction violation improvement amount W _W , the steel type cost W _N , the remaining material amount cost W _Y , and the manufacturing cost W _P The case where it represents is mentioned as an example and demonstrated (refer Formula (6)). However, the evaluation value c _k for the steel type _k is represented using at least one of the in-class width transition restriction violation improvement amount W _W , the steel type cost W _N , the remaining material cost W _Y , and the manufacturing cost W _P Just do it. For example, it is possible to decide which of these to be adopted according to the drafting policy of the cast plan.

<Modification 12>
In the first and second embodiments described above, the case of creating a cast plan has been described as an example. However, the method described in the present embodiment can be applied to production planning of a plurality of products produced by lot unit together with a plurality of products in a lot other than cast planning.

  For example, the method described in the present embodiment may be applied to a thick plate production plan (plate taking problem). After rolling the slab to a target thickness, the rolled slab is sheared according to order to obtain a thick plate. Therefore, it is necessary to decide which slab to cut out from which slab. This content is prepared as a plate production plan. In this case, the "thick plate" corresponds to the "product", the "slab" corresponds to the "lot", the "shearing (cut-out)" corresponds to the "manufacturing", and the "adjusting process" is the next process of shearing. "Corresponds to" manufacturing conditions with freedom of choice ", and the" surplus portion "of the cut slab corresponds to the" remaining material amount ".

  The thick plate information corresponding to the slab information includes, for example, manufacturing conditions such as the material, size, and delivery time of the thick plate. As conditions corresponding to (A2) to (E2), conditions (constraint conditions) which can cut out a thick plate from the same slab are given. The constraint conditions include, for example, that the same slab does not contain a thick plate different from a predetermined material, that the total weight of the thick plate does not exceed the upper limit, and the thickness of the thick plate included in the same slab It can be included that is a thickness within a predetermined range. A slab that satisfies this constraint is a slab candidate. Note that thick plates can also be grouped. For example, the thick plate group can be created by performing the processing described in the section of the slab group creation unit 202 by replacing the “desired rolling date” described in the present embodiment with “delivery date”. As an evaluation index at the time of optimization, in addition to the number of slabs, for example, a surplus portion of slabs can be included. The surplus portion of the slab is a portion of the slab which is not a thick plate after being cut out from the slab. The smaller the surplus portion of this slab is, the evaluation value indicates that the evaluation is higher. Besides, as described in the present embodiment, an evaluation index using the size of the product (thick plate) and the delivery date can be adopted. Further, the constraint equation in the optimization calculation can be set in the same manner as the constraint equation in the cast plan (refer to the equations (2) and (3)). For example, as a constraint equation corresponding to equation (2), the number of planks (or plank groups) included in the optimal slab candidate and the slab candidate are derived for planks (or plank groups) having the same content. It is possible to adopt a constraint formula formulated to be the same as the number of planks (or plank groups) selected prior to the In addition, since the sheared plate is sent to different adjustment processes such as painting, maintenance, and slow cooling, in the case of board removal, the "steel type" described in the second embodiment is replaced with the "adjustment process". Plates sheared from the same slab may be assigned to the refining process in order to maximize processing efficiency under processing constraints in each refining process.

  In addition, the method described in the present embodiment may be applied to a hot rolling plan (chance formation problem). It is necessary to determine a plurality of slabs to be hot rolled continuously. These multiple slab units are called chances. In this case, “hot-rolled sheet (coil)” corresponds to “product”, “chance” corresponds to “lot”, “rolling” corresponds to “production”, and “selection of furnace number for heating furnace” is Correspond to "manufacturing conditions with freedom of choice". The hot rolled sheet information corresponding to the slab information includes, for example, the material of the hot rolled sheet (coil), the size of the slab, the size of the hot rolled sheet (coil), and manufacturing conditions such as a desired hot rolling date. As conditions corresponding to (A2) to (E2), for example, (C2) to (E2) can be used. A chance that meets this constraint is a chance candidate. In addition, a hot-rolled sheet can also be grouped. For example, the hot-rolled sheet group can be created by performing the process described in the section of the slab group creating unit 202. The number of opportunities can be used as a metric for optimization. Besides, as described in the present embodiment, an evaluation index using the size of a product (hot-rolled sheet) and a desired hot-rolling date can be adopted. Further, the constraint equation in the optimization calculation can be set in the same manner as the constraint equation in the cast plan (refer to the equations (2) and (3)). For example, as a constraint equation corresponding to equation (2), the number of hot-rolled plates (or hot-rolled plate groups) included in the optimal chance candidate and the chance for the same hot-rolled plate (or hot-rolled plate group) A constraint formula formulated to have the same number of hot rolled sheets (or hot rolled sheet groups) selected prior to deriving a candidate can be employed. Moreover, when distributing the slab which constitutes a chance to a plurality of heating furnaces and heating them, the “steel grade” described in the second embodiment is replaced with “heating furnace” and the heating furnace determined according to the capacity of the heating furnace Slabs may be allocated for each furnace to maximize efficiency for heating under constraints.

Further, the application target of the method described in the present embodiment is not limited to a plan for collectively producing a plurality of products in lot units, but is a plan for processing a plurality of products collectively in lot units, It is also good.
For example, the method described in the present embodiment may be applied to a plan for making a plan (plan on problem). It is necessary to determine the pile to which each slab belongs and the position (stack stage) of each slab in each mountain when stacking multiple slabs into multiple piles by using transport equipment such as cranes at steel storage yard (yard) There is. Create such content as a plan. In this case, the “slab” corresponds to the “product”, the “mountain” corresponds to the “lot”, and the “stacking (conveyance of the slab by the conveyance device)” corresponds to the “processing”. The slab information 300 described in the present embodiment can be used as the slab information. Conditions (constraint conditions) corresponding to (A2) to (E2) can include conditions in which “cast” in (A2) to (E2) is replaced with “mountain”. A mountain that meets this constraint is a mountain candidate. As in the present embodiment, slabs can be created by grouping slabs. As the evaluation index at the time of optimization, in addition to the number of mountains, for example, the number of rounds can be included. In the case of making a mountain, it is to temporarily place a slab at a place different from the final place. As the number of rounds is smaller, the evaluation value indicates that the evaluation is higher. Besides, as described in the present embodiment, an evaluation index using a slab size and a desired hot-rolling date can be adopted. Further, the constraint equation in the optimization calculation can be set in the same manner as the constraint equation in the cast plan (refer to the equations (2) and (3)).

Embodiment
The embodiment of the present invention will be described on the premise of the first and second embodiments described above.
In the first and second embodiments, it is possible to create a cast plan within a practical time while minimizing the decrease in accuracy due to dividing the cast organization problem and finding a solution.

Here, as shown in equation (1), in the first and second embodiments, a decision variable x _j when the value of the objective function J is minimized is derived. Also, the evaluation value c _j of the cast candidate j included in the objective function J is represented by the sum (weighted sum) of the product of the evaluation index and the weighting factor, as shown in equations (4) and (7). Ru. The value of the weighting factor represents the importance of the evaluation index, and the value of the weighting factor leads to a different solution (decision variable x _j ). In the first and second embodiments, it is assumed that each of the weighting factors used when the optimization unit 205 derives the evaluation value c _j of the cast candidate j is a preset constant value.

However, in this case, there is a possibility that the cast plan created by the cast formation device can not be made close to the cast formation performed by the planner.
For example, with regard to the amount of excess material, if it is a steel type that frequently receives orders, even if a slab that is not tied to the order is manufactured as excess material, it can be expected to be early linked to the order of the steel type newly received. . For this reason, the planner organizes the cast taking into consideration whether or not the excess material is likely to be linked to the order for each steel type. Therefore, for example, when the amount of excess material is used as an evaluation index, it is desirable to determine a weighting factor for the amount of excess material for each steel type. The amount of excess material is the amount of excess material (B3) described in the second embodiment.

  Also, with respect to different casts of different steel types, the planner does not evaluate only the number of different casts of different steels (the number of joints of two different steel types cast continuously), but two different casts are continuously cast. As a combination of steel types, priority is given to combinations that have no problems in operation and quality. Therefore, for example, in the case where the number of different steel types is used as an evaluation index, it is desirable to determine a weighting factor for the number of different steel types continuously for each combination of two different steel types that are continuously cast. The number of different steel types is the number of different steel types described in (A3) number of steel types in the second embodiment.

  Therefore, in order to realize the evaluation by each of the evaluation indicators classified and classified according to the steel type, it is necessary to set a weighting factor for each evaluation indicator. For example, it is necessary to set a weighting factor for the amount of excess material for each steel type, or set a weighting factor for the number of different types of steels for each combination of two different steel types that are continuously cast. There are generally 100 or more types of steel in a steelmaking plant. For this reason, it takes much time and effort for the planner to set the weight coefficient of the steel type. Even if a weighting factor is set for each steel type at a certain time, a large amount of time is required to maintain a number of weighting factors to correspond to the respective operation conditions.

Therefore, in the present embodiment, it is possible to determine the weighting factor for making the evaluation according to the evaluation index different according to the steel type, by the cast knitting device without imposing a great deal of effort on the planner. . As described above, the present embodiment is different from the first and second embodiments in that the optimization unit 205 derives the weighting factor used when deriving the evaluation value c _j of the cast candidate j. Further, in the present embodiment, a case in which the second embodiment is assumed will be described as an example. Therefore, in the description of the present embodiment, the same parts as those in the second embodiment are denoted by the same reference numerals as those in FIGS. 1 to 18, and detailed description thereof will be omitted.

  Here, in the present embodiment, among the plurality of evaluation indexes, the number of different steel types in succession is classified according to the steel type, and a case where the weighting factor for the classified number of different steel types in series is derived is described as an example. The weighting factors for the evaluation indexes other than the number of different steel types are set to predetermined fixed values as in the second embodiment.

Therefore, in the present embodiment, a case where the evaluation value c _j of the cast candidate _j is represented by the following equations (8) to (9) in place of the equation (7) will be described as an example.
c _j = C _N + C _y × y + C C _G × g + C _p × p + C _D × D (8)
CC _G × g = Σ (C _{Gk, k '} x g _{k, k'} ) + C _G2 x g ₂ (9)

In equation (8), C _N is the same as C _N shown in equation (4), and is a weighting factor for the number of casts. y is the same as y shown in the equation (7), and is the sum of the amount of excess material in the cast candidate j. C _y is the same as C _y shown in equation (7) and is a weighting factor for y. p is the same as p shown in equation (7), and is the sum of the manufacturing costs of the slabs included in the cast candidate j. C _p is the same as C _p shown in equation (7), and is a weighting factor for p. D is the same as D shown in equation (4), and is the difference between the average value of the heat spread desired days of the slabs included in the cast candidate j and the earliest date. C _D is the same as C _D shown in equation (4), and is a weighting factor for D.

g is the number of different steel types, and C _G is a weighting factor for the number g of different steel types.
When two different types of molten steel are cast in succession, the two types of molten steel are mixed in the tundish. When this mixing part becomes a slab, there are cases where the slab becomes a valuable product and when it becomes a valueless product which can not be a product. In the following description, this valuable product is referred to as a product steel as needed, and the valueless product is referred to as a non-product steel as required.

In the right side of the equation (9), Σ represents that integration is performed for all combinations of steel types k and k ′ which are product steels. (9) As shown in equation, the weighting factor C _G and different steels communicating s evaluation index g of the inter steels communicating s number g is, when continuously cast two steels different from each other, of the two steels of the molten steel mixing portion depending on whether becomes product steel material, or becomes non-product steel material, g _k, it is classified as _k'or g _2. Here, it is a steel type used as a product steel material with respect to the steel type k, and sets of steel types k 'and different from steel type k are set to k' k N _K1 (k).
g _{k, k '} is the steel type of the slab included in the cast candidate j, and is the number of sets of steel types k, k' to be the product steel among the sets of two different steel types. In the following description, _{gk, k '} will be referred to as the number of different steel types, which are product steels, as needed. The number g _{k, k ′} of different steel types that are product steels is “1” when the slab included in the cast candidate j includes steel types k, k ′, and is “0” otherwise. That is, the number g _{k, k '} of different steel types, which are product steels, takes any value of “0” or “1”).
g ₂ is the steel type of the slab included in the cast candidate j, and is the total number of pairs of two different steel types that will be non-product steel materials. In the following description, g ₂ will be referred to as the number of different steel types, which are non-product steel materials, as needed.

C _{Gk, k '} is a weighting factor for the combination of steel types k and k _{' which} are product steels, and is expressed by the following equation (10).
C _{Gk, k '} = C _G1 + (C _G2- C _G1 ) / (NP _{k, k'} + L) (10)

FIG. 19 is a view for explaining an example of a weighting factor _{CGk, k} 'for a combination of steel types k and k' which are product steels. An example of the weighting factor _{CGk, k} 'for the combination of the steel types k, k', which are product steels, will be described with reference to FIG.
Planners generally think that it is better to produce product steel than non-product steel. That is, the planner thinks that, when performing casting of different steel types, it is preferable that a combination of two different steel types be made to be a product steel material. As shown in the equation (1), also in the present embodiment, the optimization unit 205 derives the decision variable x _j when the value of the objective function J is minimized. Therefore, in order to make it easy to select a cast candidate j including a large number of sets of steel types that become product steels rather than a set of steel types that become non-product steels as a cast candidate included in the optimal combination of cast candidates. is different steels communicating s number g _k as a product steel _material, the weighting factor for _k'is, it is sufficient to fall below the weighting factor for different steels communicating s number g ₂ as a non-product steel material.

Therefore, the weighting factor for product steels is C _G1 , and the weighting factor for non-product steels is C _G2 (C _G1 <C _G2 ).
19, graph 1901 represents a weighting coefficient C _G2 of the inter steels communicating s number g ₂ as a non-product steel material. Moreover, NP _{k, k '} is the number of results of continuous casting of molten steel of steel type k' subsequently to molten steel of steel type k in a past fixed period (for example, one year). As shown in FIG. 19, the weight coefficient C _G2 for the non-product steel material is a constant value. In the following description, the actual number NP _{k, k '} of continuous casting of the molten steel of the steel type k' subsequently to the molten steel of the steel type k during a fixed period in the past _, and NP _{K, k '} Abbreviated.

On the other hand, with respect to a set of two steel types which are different from each other and to be a product steel material, there are a set having a high occurrence frequency and a set which is not so generated from the viewpoints of operation and quality. In general, a planner thinks that it is preferable to set a pair of two steel types which are different from each other and to be a product steel material so that the number of occurrences is high. Therefore, as shown by the graphs 1902a and 1902b in FIG. 19, the maximum value is taken when the actual number NP _{k, k '} is 0 (zero), and the smaller the actual number NP _{k, k', the} smaller the value. And the weighting factor C _{Gk, k} 'for the combination of steel types k and k' as the product steel material, such that the minimum value becomes the weighting factor C _G1 for the product steel material.

As described above, a cast candidate j including a large number of sets of steel types to be product steels is more likely to be selected as a cast candidate to be included in an optimal combination of cast candidates than a set of steel types to be non-product steels. There is a need. Therefore, when the actual number NP _{k, k '} is 0 (zero), the weighting factor C _{Gk, k} ' for the combination of steel types k, k 'serving as product steel falls below the weighting factor C _G2 for non-product steel Let's do it. Therefore, in the equation (10), the constant L is set to a value (L> 1) exceeding “1”. In this way, regardless of the combination of steel types k and k ', the weight coefficient C _{Gk, k} ' for the combination of steel types k and k _'which is the product steel is the weight coefficient C _G2 for non-product steel Can be less than The graph 1902a shows a weighting factor _{CGk, k} 'for the combination of steel types k and k' as product steels when the constant L is relatively small, and the graph 1902b shows the case where the constant L is relatively large. The weighting factor C _{Gk, k '} with respect to the combination of the steel types k and k' used as product steel materials is shown. In addition, assuming that the constant L is “1” (L = 1), the weighting factor C _Gk, for the combination of steel types k and k ′, which are product steels, when the actual number NP _{k, k ′} is 0 (zero) _{. k'is} equal to the weighting factor C _G2 for the non-product steel material _{(C Gk, k'= C G2} ).

As described above, the weighting coefficient C _{Gk, k} 'for the combination of steel types k and k' serving as product steels takes smaller values as the actual number NP _{k, k '} increases, and thus, the set of steel types for product steel Among them, a cast candidate j including a large number of combinations of steel types having a large number of achievements NP _{k, k ′} is easily selected as a cast candidate included in an optimal combination of cast candidates. Therefore, it can approach cast organization which a planner performs.

The weighting factors C _G1 and C _G2 respectively determine the minimum value and the maximum value of the weighting factor C _G with respect to the number g of different steel types, and are set in advance. The constant L is for adjusting the weighting factor C _{Gk, k '} for the number g _{k, k'} of different steel types that become product steel when the actual number of times NP _{k, k '} is 0 (zero). , Preset.

The cast knitting apparatus 200 can identify manufacturing performance data NP _{k, k} 'where the molten steel of the steel type k' is continuously cast continuously for a certain period (for example, one year) in the past after continuous casting of the steel type k. , Acquired and stored in advance for all steel types k manufactured by the continuous casting machine. In addition, the cast knitting device 200 acquires and stores in advance a combination of steel types to be a product steel and a combination of steel types to be a non-product steel. For example, the cast formation device 200 performs the operation by the operator on the cast formation device 200, receives slab information transmitted from an external device, or reads slab information stored in a portable storage medium (see FIG. It is possible to acquire manufacturing result data capable of specifying the number of actual results NP _{k, k ′} , a set of steel types to be product steels, and a set of steel types to be non-product steels. For example, the cast knitting apparatus 200 is a product steel number _{k, k} 'for the combination of steel types k and k' to be product steel, from two such steel records k, k different from each other continuously cast from the manufacturing results data It can obtain by deriving the group of steel types k and k 'which become product steel materials among'. In addition, the cast knitting apparatus 200 is a product number NP _{k, k} 'for combinations of steel types k and k' which are non-product steel materials, and two cast steel types k and k different from each other continuously cast from such manufacturing result data. It can obtain by totaling the number of sets of steel types k and k 'which become non-product steel materials among'.

When deriving the evaluation value c _j of the cast candidate j, the optimization unit 205 uses the equations (8) to (10) instead of the equation (7) described in the second embodiment. At this time, based on the steel type of the slab included in the cast candidate j, the optimization unit 205 determines the number of sets of steel types k and k 'as product steels and k and k' pairs of steel types as non-product steels. Derive the total number. Note that, as described above, the number g _{k, k '} of different steel types which are product steels is "1" when the slabs included in the cast candidate j include the steel types k, k', otherwise Becomes "0". And the optimization part 205 makes each of the number of sets of steel types k and k 'which become product steel materials different number of steels kind continuous number g _{k and k'} which becomes product steel, and k and k of steel types which become non-product steel materials The total number of the set of 'is given to the equation (9) as a number g ₂ of different steel types which are non-product steel materials. Moreover, the optimization unit 205 gives a weighting factor C _G2 for the non-product steel material in (9). Further, the optimization unit 205 gives the number of actual results NP _{k, k '} and the weighting factors C _G1 and C _G2 to the equation (10), and the weighting factor C _Gk for the combination of steel types k and k' to be product steels. _{, k ′} are derived. As described above, the evaluation value c _j of each cast candidate _j is derived.
The weighting factors C _N , C _y , C _G (C _{G k, k ′} , C ₂ ), C _p , and C _D are preset according to how much the respective evaluation items are to be emphasized. It represents the balance of evaluation among evaluation items. One or more of the weighting factors C _N , C _y , C _G (C _{G k, k ′} , C ₂ ), C _p , and C _D may be “1”.

(Example of calculation)
Next, a calculation example will be described.
In this calculation example, the cast plan is created on the assumption that the steel type included in the cast candidate j is any of steel types A, B, C, D, and E.
FIG. 20 is a table showing the actual number of times NP _{k, k '} used in this calculation example. In FIG. 20, the steel type shown in the row element is a front steel type k, and the steel type shown in the column element is a rear steel type k ′. The rear steel type k 'is a steel type which becomes a product steel material with respect to the front steel type k and is a steel type k' different from the front steel type k (when the molten steel of the rear steel type k is cast subsequently to the molten steel of the front steel type k) The slab of the mixing portion of the molten steel becomes the product steel). In addition, when the front steel type k and the rear steel type k 'are the same, the weighting factor C _G for different numbers of different steel types continuous g (weighting factor C _{Gk, k} ' for the combination of steel types k and k that become product steels and non products The weighting factor C _G2 ) for steel is assumed to be 0 (zero). Since the actual number of times NP _{k, k ′ in} such a case becomes unnecessary, the corresponding element is indicated as “−” in FIG.

In this calculation example, the weighting factor C _G1 for the product steel material is “10”, and the weighting factor C _G2 for the non-product steel material is “100”. Further, the constant L is set to "1.1". Then, according to equation (10), C _{Gk, k '} for a combination of steel types k and k' which are product steels is as shown in FIG. 21, the weighting factor C _G of the inter steels communicating s number g in the present calculation example illustrates in tabular form. In FIG. 21, combinations of steel types for which the weighting coefficient C _G with respect to the number of different steel types continuous number g is “0 (zero)” are combinations consisting of the same steel types (ie, front steel type k and rear steel type k ′ are the same) is there). Further, steels combination weighting factors C _G of the inter steels communicating s number g is "100" is a combination of steel types to be non-product steel material. Also, combinations of steels of the weighting factor C _G of the inter steels communicating s number g is a value other than "0 (zero)" or "100" is a combination of steel species as a product steel material.

FIG. 22 is a diagram showing the numbers of different steel types in the cast candidate j derived by the cast knitting device 200 in the form of a table for each combination of steel types. FIG. 22 (a) shows a comparative example, and FIG. 22 (b) shows an invention example. In the invention examples, to derive the (8) to (10) evaluation values for different grades communicating s number g using ((8) .SIGMA.C _G × g for formula). On the other hand, in the comparative example, the weight coefficient C _G2 for non-product steel materials is set to "100", and the weight coefficients C _{Gk, k} 'for the combination of steel types k and k' which become product steels are all weight coefficients C _G1 for product steel materials (= 10)

As shown in FIG. 22 (b), as in the present embodiment, the weighting factor C _G1 over to the product steel material, in a range of less than the weighting factor C _G2 for the non-product steel material, the steel type k, a combination of k'as a product steel The total number of different steel types is reduced by making the weighting coefficient C _{Gk, k '} for the value smaller as the actual number of times NP _{k, k'} increases, and the total number of different types of steels decreases, and A combination of steel types k and k ′ is selected in which the weight coefficient CG k, k _′ decreases with respect to the combination of k and k ′. That is, since a combination of steel types k and k _′ having many actual numbers NP _{k and k ′} is selected, the cast plan derived by the cast knitting device 200 can be brought closer to the cast plan intended by the planner. On the other hand, when the weighting coefficient _{CGk, k} 'for the combination of steel types k and k' which are product steels is constant, as shown in FIG. 22A, the total number of different steel types will increase. Therefore, the cast plan intended by the planner can not be obtained. In the example of the invention shown in FIG. 22 (b), the evaluation value (ΣC _G × g in the equation (8)) with respect to the number g of different steel types is “122.7 (= 100 + 22.7)”. On the other hand, the evaluation value for the number g of different steel types in the comparative example shown in FIG. 22A is “296.7 (= 12.8 + 91.8 + 21.1 + 100 + 18.1 + 52.9)”. Therefore, it is understood that the evaluation value for the number g of different steel types is significantly improved in the invention example as compared with the comparative example.

(Summary)
As described above, in the present embodiment, the number g of different steel types is divided into the number g _{k, k '} of different types of steel used as product steels and the number g ₂ of different steel types continuously manufactured as non-product steels. As coefficients, weighting coefficients C _{Gk, k} 'for combinations of steel types k and k' for product steels and weighting coefficients C _G2 for combinations of steel types k and k _' for non-product steels are respectively used. Here, steels k as a non-product steel material, the weight coefficient C _G2 is a fixed value for the combination of k', steels k as the product steel material, the weight coefficient C _Gk for the combination of _k', the _k', weight for product steel factor C _G1 over, in a range of less than the weighting factor C _G2 for the non-product steel material, the smaller value result count NP _{k, k'often.} Therefore, as a combination of steel types k and k 'included in the optimum cast candidate j, a combination of steel types k and k' as product steels is more likely to be included than a combination of steel types k and k 'as non-product steels. . In addition, for combinations of steel types k and k 'which are product steels, combinations of steel types k and k _' having many actual numbers NP _{k and k '} are included in combinations of steel types k and k' included in the optimum cast candidate j. It becomes easy to It is not realistic for the planner to manually set the weighting factors C _{Gk, k '} and C _G2 for this purpose. As described above, in the present embodiment, in addition to the effects described in the first embodiment and the second embodiment, the cast plan derived by the cast knitting device 200 is brought closer to the cast plan intended by the planner. The effect is that it can be realized without much effort.

(Modification)
<Modification 13>
In this embodiment, the number g of different steel types is divided into the number g _{k, k '} of different types of steel used as product steels, and the number g ₂ of different steel types continuously formed as non-product steels. The case where the weighting coefficient C _{Gk, k} 'for the combination of steel types k and k' used as steel materials and the weighting coefficient C _G2 for the combination of steel types k and k _'used as non-product steel materials are used is described as an example. However, the classification destinations of the number g of different steel types are not limited to two.

For example, the product steel may be further classified into high quality product steel and low quality product steel. In this case, the number g of different steel types is divided into the number of different steel types which become high quality product steels, the number of different steel types which become low quality product steels, and the number of different steel types which become non-product steels. being classified. Therefore, as a weighting factor for these, for example, a weighting factor C _{GHk, k} 'for a combination of steel types k and k' for high quality product steel and a weighting factor for a combination of steel types k and k _' for low quality product steel C _{GLk, k',} steels k as a non-product steel material, the weight coefficient C _G2 for the combination of k'can be used respectively. The weighting factor C _{GLk, k} 'for the combination of steel types k and k' for low quality product steel and the weighting factor C _{GHk, k} 'for the combination of steel types k and k _' for high quality product steel are, for example, It is represented by the following equation (11) and equation (12).

C _{GL k, k '} = C _G3 + (C _G2- C _G3 ) / (NP _{k, k'} + L1) (11)
C _{GH k, k '} = C _G4 + (C _G3- C _G4 ) / (NP _{k, k'} + L2) (12)
Here, C _G3 is a weight factor for the product steel lower quality, a value less than the weighting factor C _G2 for the non-product steel material (C _G3 <C _G2). C _G4 is a weight factor for the product steel quality, a value less than the weighting factor C _G3 for the product steel lower quality (C _G4 <C _G3). The weighting factor C _G3 for low quality product steel and the weighting factor C _G4 for high quality product steel are preset. The constants L1 and L2 respectively have values (L1, L2> 1) exceeding “1”, and are set in advance. Constants L1 and L2 are weighting factors C _{GLk, k '} for high quality steels with a combination of steel types k and k' which are low quality product steels when the actual number of times NP _{k, k '} is 0 (zero). The weighting factor C _GH k, k 'for the combination of steel types k and k' which are the product steels of the present invention is to be adjusted.

<Modification 14>
In this embodiment, the case where the evaluation index classified according to the classification condition represented using the steel type k is the number g of different steel types continuously is described as an example. However, the evaluation index classified according to the classification condition represented using the steel type k is not limited to the number g of different steel types continuously.
For example, the evaluation index classified according to the classification condition represented using the steel type k may be a total y of the amount of excess material. For example, the total sum y of the amount of excess material may be classified for each steel type k, and a weighting factor may be derived for each amount of excess material for each steel type k. In this case, the weighting factor is derived for each steel type k. The weighting factor for each steel type k is represented, for example, by a function in which the value decreases as the amount of excess material increases, within the range between the maximum value and the minimum value set in advance. The cast knitting device 200 acquires the total for each steel type k of (the actual results of) the amount of remaining material in a predetermined fixed period (for example, one year) in the past, and the weighting factor corresponding to the total of the acquired remaining material amount of steel type k The above-mentioned function is used as a weighting factor for the amount of excess material of the steel type k. The planner determines that the steel type k for which the amount of excess material has increased in the past may be included in the cast. Therefore, in the above manner, the cast plan derived by the cast formation device 200 can be made closer to the cast plan intended by the planner. The evaluation value for the evaluation index in this case (corresponding to CC _G × g in equation (8)) is the sum of the products of the weighting factor and the amount of remaining material for all steel types k. .

Moreover, the evaluation index classified according to the classification condition represented using the steel type k may be the manufacturing cost generated at the time of operation. For example, the manufacturing costs generated at the time of operation may be classified by steel type k, and a weighting factor may be derived for each of the manufacturing costs for each steel type k. In this case, the weighting factor is derived for each steel type. The weighting factor for each steel type k is represented by, for example, a function in which the value decreases as the manufacturing cost increases, within the range between the maximum value and the minimum value set in advance. The cast forming apparatus 200 acquires the sum of (the actual results of) manufacturing costs in a certain period in the past (for example, one year) for each steel type k, and the weighting factor corresponding to the total of the acquired manufacturing costs for steel type k is Is derived as a weighting factor for the production cost of the steel type k using the above function. The planner determines that the steel type k whose production cost has increased in the past may be included in the cast. Therefore, in the above manner, the cast plan derived by the cast formation device 200 can be made closer to the cast plan intended by the planner. Note that the evaluation value for the evaluation index in this case (corresponding to CC _G × g in equation (8)) is the sum of products of the weighting factor and the manufacturing cost for all steel types k. In this case, the slab information 300 can include information indicating the manufacturing cost as the manufacturing condition.

Moreover, the evaluation index classified according to the classification condition represented using the steel type k may be the difference D between the average value of the desired hot-rolling day of the slab and the earliest date. In the following description, the difference D between the average value of the desired hot-rolling day of the slab and the earliest date is referred to as the delivery difference D as necessary. For example, the due date difference D may be classified by steel type k, and a weighting factor may be derived for each of the due date difference D for each steel type k. In this case, the weighting factor is derived for each steel type. The weighting factor for each steel type k is represented by, for example, a function in which the value decreases as the due date difference D increases, within the range between the maximum value and the minimum value set in advance. The cast knitting device 200 acquires an average value for each steel type k in (the actual results of) the delivery date difference D during a predetermined period (for example, one year) in the past, and a weight corresponding to the average value of the acquired delivery date difference D of the steel type k. The factor is derived as a weighting factor for the average value of the due date difference of the steel type k using the function described above. The planner determines that the steel type k whose difference in delivery date has become large in the past may be included in the cast. Therefore, in the above manner, the cast plan derived by the cast formation device 200 can be made closer to the cast plan intended by the planner. Note that the evaluation value for the evaluation index in this case (corresponding to CC _G × g in equation (8)) is the sum of products of weighting factors and delivery date differences for all steel types k.
Also, two or more evaluation indexes (for example, the number g of different steel types and the total sum y of the amount of excess material) may be classified.

<Modification 15>
In the present embodiment, the evaluation index has been described by way of example where classification is performed according to the classification condition represented using steel type k. However, the evaluation index may be classified according to classification conditions represented using manufacturing conditions other than steel type k.
For example, when steel products (parts of slabs) manufactured as surplus materials are linked to a new order and made into product steel products, there is a tendency that there are more orders for wider steels than orders for narrow steels In the case, it is desirable to make and leave a wide steel material as a surplus material. In such a case, the total sum y of the amount of excess material may be classified for each width range of the steel material (a range of width set in advance), and a weighting factor may be derived for each amount of excess material for each width range of steel material . In addition to this, the total sum y of the amount of excess material may be classified for each thickness region of the steel material (predetermined thickness range) or for each weight of the steel material (predetermined weight range). In this case, the present embodiment can be applied to the first embodiment without assuming the second embodiment. In addition, the evaluation index may be classified by combining two or more manufacturing conditions (for example, steel type k and width range). The fact that the manufacturing cost may be included in the manufacturing conditions is as shown in <Modification 14>.

<Modification 16>
In the present embodiment, the weighting factor C _{Gk, k} 'for the combination of steel types k and k _' which are product steels is within the range of weighting factor C _G1 or more for product steel and more than weighting factor C _G2 for non-product steel The case where the value is smaller as NP _{k, k '} is larger has been described as an example. However, if the weighting factor for the evaluation index classified according to the classification condition represented using a certain manufacturing condition is changed according to the manufacturing performance value derived from the manufacturing performance data classified according to the classification condition , It does not necessarily change according to the number of times of performance NP _{k, k '} , for example, as described in <Modification 14>, the amount of remaining material (performance), manufacturing cost (performance), delivery date It may be changed according to the difference D (actual value).

In addition to this, for example, the weighting factor for the evaluation index may be changed according to the value indicating the quality of the product which is an example of the above-mentioned manufacturing result value. In this case, for example, the number of pieces is totaled for each manufacturing condition (for example, steel type) for the manufactured slab, and the weighting factor for the evaluation index is within the range between the preset maximum value and the minimum value. The smaller the number, the smaller the number. Taking an example where the evaluation index is several grams of different steel types in succession, explanation will be given on the case of a slab manufactured by continuously casting molten steel of steel type k (consecutively continuous casting of different steel types) following the molten steel of steel type k. the number of ND _k, When _k', the weighting factor C _G of the inter steels communicating s number g is, for example, represented by the following equation (13).

C _G = Σ (C _G1 + C _ND × ND _k , _{k ′} ) (13)
In the equation (13), Σ represents that integration is performed for a combination of steel types k and k ′ to be a product steel material. Further, in equation (13), C _ND is flaws number of slabs ND _j, a weighting factor for _j', are set in advance. Flaws number ND _j slab, the weight coefficient C _ND for _j'are flaws number ND _j slabs is determined depending on how much importance to assess _j'.
In the example shown in the equation (13), the value in the parentheses in the equation (13) becomes smaller as the combination of the steel types k and k 'with a smaller number of slabs becomes smaller, so the minimum as shown in the equation (1) In the conversion problem, such a combination of steel types k and k 'tends to be included in the optimum cast candidate.

<Modification 17>
The method of the present embodiment has been described by taking the case of minimizing the value of the objective function J as an example, but the method of the present embodiment can be applied to the case of maximizing the value of the objective function J. In this case, for example, an objective function J is obtained by multiplying the right side of the equation (1) by (-1). Further, (rather than C _G1) a weighting factor for the product steel as C _G5, the weighting factor C _G5 for the product steel material, a value exceeding the weight coefficient C _G2 for the non-product steel material (C _G5> C _G2). Then, steels k as the product steel material, the weight coefficient C _Gk for the combination of _k', the _k', exceeds the weight coefficient C _G2 for the non-product steel material, the weight coefficient C _G5 following range for the product steel material, result count NP _{k , k '} are larger values.

<Other Modifications>
The embodiment of the present invention described above can be realized by a computer executing a program. In addition, a computer readable recording medium recording the program and a computer program product such as the program can be applied as an embodiment of the present invention. As the recording medium, for example, a flexible disk, a hard disk, an optical disk, a magneto-optical disk, a CD-ROM, a magnetic tape, a non-volatile memory card, a ROM or the like can be used.
In addition, any of the embodiments of the present invention described above is merely an example of implementation for carrying out the present invention, and the technical scope of the present invention should not be interpreted limitedly by these. It is a thing. That is, the present invention can be implemented in various forms without departing from the technical idea or the main features thereof.

(Relationship with claim)
The relationship between the matters described in the present embodiment and the claims will be listed below. The present invention is not limited to the following, as described in the modification and the like.
<Claim 1>
The plan creation device corresponds to, for example, the cast formation device 200.
The acquisition unit is realized by using the slab information acquisition unit 201, for example.
The product information corresponds to, for example, the slab information 300.
The selection means is realized by using the slab group selection unit 203, for example.
The lot candidate derivation unit is realized, for example, by using the cast candidate derivation unit 204.
The first lot inclusion enable condition is realized, for example, by the constraints (A2) to (E2) (see also step S1702 in FIG. 17).
The optimization means is realized, for example, by using the optimization unit 205.
The determination unit is realized by using the determination unit 206, for example.
The redefinition means is realized by using the slab group redefinition unit 207, for example.
The output unit is realized, for example, by using the output unit 208.
The objective function is realized by, for example, equation (1).
The first lot candidate derivation evaluation index is, for example, the cast number (“1 (= decision variable x _j ) multiplied by the weighting coefficient C _N ) of the equations (4), (7), and (8). To be realized.
The second lot candidate derivation evaluation index is realized by using, for example, the total sum y of the amount of excess material in the cast candidate j, the number g of different steel types, and the like. The surplus amount is realized, for example, by using the total sum y of the surplus material amounts in the cast candidate j (see also <Modification 14> and the like).
The classification condition represented using the manufacturing conditions of the product is, for example, a condition that classification is made into each combination of steel types k and k 'as product steels and combination of steel types k and k' as non-product steels. Corresponds to
The weighting factor for each of the second lot candidate derived evaluation indexes classified according to the classification conditions represented using the manufacturing conditions of the product is, for example, a weighting factor C for a combination of steel types k and k 'which are product steels. _{Gk, k',} for non-product steel material is achieved by using a weighting coefficient C _G2.
The constraint equation is realized by, for example, equations (2) and (3).
The manufacturing result data of the product, which is classified according to the classification condition represented by using the manufacturing condition of the product, is, for example, a combination of steel types k and k 'which become the product steel material. actual number NP k of the _respective and manufacturing performance data that can identify the _k', steels k as a non-product steel material, result count NP k in combination _k', manufacturing performance data that can identify the _k' And can be obtained by
<Claim 2>
The relational expression is realized, for example, by using the equation (10) (see also <Modification 13> and the like). The manufacturing performance value derived based on the manufacturing performance data classified according to the classification condition expressed using the manufacturing condition of the product is, for example, the number of performance times in each of the combinations of steel types k and k 'which are product steels. It corresponds to the actual number of times NP _{k, k} 'in the combination of NP _{k, k'} and steel types k, k 'as non-product steel materials.
<Claim 3>
The weighting factor for the second lot candidate derivation evaluation index classified according to the classification condition represented using the manufacturing condition of the product, for the second lot candidate derivation evaluation index classified in a predetermined classification destination weighting factors, it is a constant value, for example, as shown in FIG. 19, steels k as a non-product steel material, the weight coefficient C _G2 for the combination of k'is realized by a constant value.
<Claim 4>
In the second lot candidate derivation evaluation index classified according to the classification condition represented using the manufacturing condition of the product, taking a weighting factor for the second lot candidate derivation index classified as a predetermined classification destination For example, as shown in equation (10), the weighting factor C _{Gk, k} 'for the combination of steel types k and k _' as product steels is more than the weighting factor C _G1 for product steels and the weight for non-product steel materials. It corresponds to becoming a range less than the coefficient _CG2 .
<Claim 5>
The group creation unit is realized by using the slab group creation unit 202, for example.
The product group corresponds to the slab group.
<Claim 6>
The product size, weight, delivery time, and cost are realized by, for example, slab width and slab thickness, slab weight, desired hot rolling date, and manufacturing cost (see <Modification 14>).
The evaluation index for evaluating at least one of the size, weight, delivery date, and cost of the product is, for example, the difference W between the maximum value and the minimum value of the width of the slab (slab width) included in the cast candidate j, The difference T between the maximum value and the minimum value of the slab thickness (slab thickness) included in the cast candidate j, the number S of slabs included in the cast candidate j, the average value of the desired hot rolling dates of slabs included in the cast candidate j This is realized by using the difference D between the date and the earliest date D, and the sum of the manufacturing cost (see <Modification 14>).
For example, at least one of the second lot candidate derivation metrics is classified according to classification conditions represented using at least one of the size, weight, delivery date, and manufacturing cost of the product, for example, This corresponds to the description of <Modification 14>.
<Claim 7>
The conditions under which the products can be included in the same lot are realized, for example, by the constraints (B2) to (E2).
<Claim 8>
The manufacturing condition selection evaluation index is realized, for example, by the evaluation indexes ((2) to (4) of the right side of the expression (6)) of (A3) to (D3) (see also steps S1708 to S1712 in FIG. 17).
The second lot inclusion enable condition is realized, for example, by the constraint condition (F2) (see also step S1714 in FIG. 17).
<Claim 9>
The material, size, weight, delivery date, and cost of the product are realized by, for example, material, slab width and slab thickness, slab weight, desired hot rolling date, and manufacturing cost.
The evaluation index for evaluating at least one of the material, size, weight, delivery date, and cost of the product is, for example, the total number n of steel types of slabs included in cast candidate j, width of slab included in cast candidate j The difference W between the maximum value and the minimum value of (slab width), the difference T between the maximum value and the minimum value of the slab thickness (slab thickness) included in the cast candidate j, the number S of slabs included in the cast candidate j, It is realized by using the total sum y of the amount of surplus material in the cast candidate j, the difference D between the mean value of the desired hot rolling of slabs included in the cast candidate j and the earliest date, and the sum of the manufacturing cost.
For example, at least one of the second lot candidate derivation evaluation indexes is classified according to a classification condition represented using at least one of the material, size, weight, delivery date, and cost of the product, for example, Classifying the number g of different steel types in a row into several g _{k, k '} of different steel types as product steels and the number g ₂ of different steel types as non-product steels according to the combination of steel types k and k' , <Modification 15> corresponding to the description.
<Claim 10>
Of the weighting factors for the second lot candidate derivation evaluation index classified according to the classification condition represented using the material of the product, the weighting for the second lot candidate derivation evaluation index classified as a predetermined classification destination factor, it is a constant value, for example, as shown in FIG. 19, steels k as a non-product steel material, is multiplied by a weighting factor C _G2 (the product steel material different steel grades communicating s number g ₂ for the combination of k' Is realized by the constant weighting factor C _G2 ).
<Claim 11>
The first lot inclusion enable condition is realized, for example, by the constraints (A2) to (E2) (see also steps S1702 to S1713 in FIG. 17).
The second lot inclusion enable condition is realized, for example, by the constraint condition (F2) (see also step S1714 in FIG. 17).

  200: cast knitting device, 201: slab information acquisition unit, 202: slab group creation unit, 203: slab group selection unit, 204: cast candidate derivation unit, 205: optimization unit, 206: determination unit, 207: slab group re- Definition part 208: Output part 300: Slab information 400: Slab information after rearrangement 500: Slab group information 600: Slab group information after rearrangement 900: Slab group information after redefinition

Claims (13)

A planning device that creates a plan for collectively producing and processing multiple products in units of lots,
Acquisition means for acquiring product information including information on the plurality of products including manufacturing conditions of the products;
Selection means for selecting a part of the plurality of products;
Among the subsets of the products selected by the selection means, the subsets satisfying the first lot inclusion enabling condition expressed using the manufacturing conditions of the products as the conditions that the products can be included in the same lot Lot candidate derivation means for deriving as a lot candidate;
An optimization means for deriving an optimal lot candidate from the lot candidate derived by the lot candidate deriving means by performing an optimization calculation to maximize or minimize the value of an objective function within a range satisfying a constraint expression;
A determination unit that determines whether the result of the optimization calculation has converged;
Redefining means for redefining the product included in the optimal lot candidate derived by the optimizing means as one product when it is determined by the determining means that the result of the optimization calculation is not converged When,
When the determination means determines that the result of the optimization calculation has converged, the optimal lot candidate derived by the optimization means is regarded as an optimal lot, and any suitable product is included in the optimal lot. And output means for outputting information indicative of
The objective function is a product of a first lot candidate derivation evaluation index including the number of lots, a weighting factor for the first lot candidate derivation evaluation index, and a second lot candidate derivation evaluation index including a surplus amount An objective function including a product of the second lot candidate derivation evaluation index and a weighting factor,
The surplus amount is an amount of a portion which becomes surplus in the lot when one or more of the products are put together in the lot,
At least one of the second lot candidate derivation metrics is classified according to classification conditions represented using manufacturing conditions of the product,
The weighting factor for the second lot candidate derivation evaluation index includes a weighting factor for each of the second lot candidate derivation evaluation indices classified according to the classification conditions represented using the manufacturing conditions of the product,
The constraint equation is that the number of products included in the optimal lot candidate is the same as the number of products selected by the selection unit, and the same product is not included in different lot candidates. Contains the formula formulated
The optimization means is a production performance data of the product, wherein a weighting factor for the second lot candidate derived evaluation index classified according to the classification condition represented using the manufacturing condition of the product is the product performance of the product Derived based on manufacturing performance data classified according to classification conditions expressed using manufacturing conditions,
The selection means selects all of the products redefined by the redefinition means and some products of the unselected products when the products are redefined by the redefinition means;
The selection of the product by the selection unit, the derivation of the lot candidate by the lot candidate derivation unit, and the optimization by the optimization unit until the determination unit determines that the result of the optimization calculation has converged A plan creation device characterized in that   The optimization means is classified according to the second lot candidate derivation evaluation index classified according to the classification condition represented using the manufacturing condition of the product, and the classification condition represented using the manufacturing condition of the product The second lot candidate derivation evaluation classified according to the classification condition represented using the manufacturing condition of the product using the relational expression showing the relationship with the manufacturing actual value derived based on the manufacturing result data. The plan creation device according to claim 1, wherein a weighting factor for the index is derived.   The weighting factor for the second lot candidate derivation evaluation index classified according to the classification condition represented using the manufacturing condition of the product, for the second lot candidate derivation evaluation index classified in a predetermined classification destination The plan creation device according to claim 1 or 2, wherein the weighting factor is a constant value.   The optimization unit is configured to derive the second lot candidate classified as a predetermined classification destination among the second lot candidate derivation evaluation index classified according to the classification condition represented using the manufacturing condition of the product. The plan creation device according to any one of claims 1 to 3, wherein a possible range of a weighting factor for an index is set based on information input to the plan creation device. It further comprises group creation means for creating a plurality of product groups by grouping the plurality of products based on the manufacturing conditions of the products,
The selection means selects a part of the product group created by the group creation means,
The lot candidate derivation unit derives, as a lot candidate, a subset satisfying the condition that the product group can be included in the same lot among the subsets of the product group selected by the selection unit.
The constraint equation includes the same number of product groups included in the optimum lot candidate and the number of product groups selected by the selection unit, and the same product group is included in different lot candidates. Containing formulas formulated to not
When the determination means determines that the result of the optimization calculation does not converge by the determination means, the product group included in the optimal lot candidate derived by the optimization means is a product of one product Redefined as a group,
Selection of the product group by the selection unit, derivation of the lot candidate by the lot candidate derivation unit, and selection of the lot candidate by the selection unit until the determination unit determines that the result of the optimization calculation has converged The plan creation device according to any one of claims 1 to 4, wherein the optimization calculation is performed. The manufacturing conditions of the product include at least one of the size, weight, delivery time, and cost of the product,
The second lot candidate derivation evaluation index further includes an evaluation index for evaluating at least one of the size, weight, delivery date, and cost of the product,
At least one of the second lot candidate derivation metrics is classified according to classification conditions represented using at least one of the size, weight, delivery date, and cost of the product. The plan creation device according to any one of Items 1 to 5. The product is a slab cast by a continuous casting machine,
The lot is a cast, which is a group of multiple charges cast in succession.
The plan is a cast plan that indicates the cast to which the slab belongs,
The conditions under which the products can be included in the same lot are that the total weight of the slab does not exceed the upper limit specified in advance, the sum of the lengths of the coils manufactured by rolling the slab is previously The specified upper limit value is not exceeded, the difference in width of the slabs which precede and follow each other in the rolling order of the slabs is equal to or less than the upper limit value specified in advance, and the difference in width is a constant value Including at least any one of the following number of slabs being less than or equal to a predetermined upper limit value,
The excess is the amount of excess material which is the excess of the total weight of slabs of the same steel type in one cast divided by the weight of one charge,
The plan generation device according to any one of claims 1 to 6, wherein the steel type is specified in advance based on a component of molten steel. The lot candidate deriving means has freedom in the selection of the products having the manufacturing condition having freedom in selection among the products included in the subset of products selected by the selecting means. The manufacturing condition selection evaluation index is derived based on the manufacturing condition of the product included in the subset of the products selected by the selection unit for each of a plurality of different manufacturing condition options, and the derived manufacturing condition is derived First manufacturing condition selecting means for selecting a manufacturing condition having the degree of freedom of selection based on a selection evaluation index;
The subset of products selected by the selection means includes the products in the same lot, assuming that the selection of the manufacturing conditions having the freedom of selection is determined by the first manufacturing condition selection means. It is determined whether or not the second lot inclusion enabling condition is expressed using the manufacturing condition as the condition that can be performed, and based on the result of the determination, the subset of the products selected by the selecting means And second production condition selecting means for deriving the lot candidate from among the above.
The manufacturing condition selection evaluation index is a manufacturing condition of the product included in the subset of the products selected by the selection means, and is selected from the manufacturing condition options having the degree of freedom of selection Evaluation indices concerning the number of different options, Evaluation indices concerning the amount of excess material produced when producing the product included in the subset of products selected by the selecting means, and the products selected by the selecting means At least one of an evaluation index on manufacturing cost when producing the product included in the subset, and an evaluation index on improvement of violation against the second lot inclusion possible condition realized by selection of the manufacturing condition; The plan creation device according to any one of claims 1 to 7, comprising. The manufacturing conditions of the product include at least one of the material, size, weight, delivery date, and cost of the product,
The second lot candidate derivation evaluation index further includes an evaluation index for evaluating at least one of material, size, weight, delivery date, and cost of the product,
At least one of the second lot candidate derivation evaluation indexes is classified according to a classification condition represented using at least one of material, size, weight, delivery date, and cost of the product. The plan creation device according to claim 8.   Of the weighting factors for the second lot candidate derivation evaluation index classified according to the classification condition represented using the material of the product, the weighting for the second lot candidate derivation evaluation index classified as a predetermined classification destination The plan creation device according to claim 9, wherein the coefficient is a constant value. The product is a slab cast by a continuous casting machine,
The lot is a cast, which is a group of multiple charges cast in succession.
The plan is a cast plan that indicates the cast to which the slab belongs,
The manufacturing conditions with the above-mentioned freedom of choice are the steel types specified in advance based on the composition of the molten steel,
The first lot inclusion condition includes constraints in manufacturing the slab, which is determined independently of the steel type,
The second lot inclusion possible conditions include constraints in manufacturing the slab, which are determined depending on the steel type,
The first manufacturing condition selecting means in the lot candidate derivation means extracts a subset of the products satisfying the first lot inclusion possible condition from the subset of the products selected by the selecting means, Among the products included in the extracted subset of products, for the product having freedom in selection of the steel type, the extraction is performed for each of a plurality of different options of manufacturing conditions having freedom in the selection. The manufacturing condition selection evaluation index is derived based on the manufacturing conditions of the products included in the subset of products, and one of the steel types is determined based on the derived manufacturing condition selection evaluation index ,
The second production condition selection means in the lot candidate derivation means includes the second lot including the subset of the extracted product, with the steel type having the freedom of selection as the determined steel type. It is determined whether a possible condition is satisfied, and a subset satisfying the second lot inclusion possible condition among the extracted subsets of the product is derived as the lot candidate.
The excess amount is characterized in that it is an amount of excess material which is the total weight of the slabs of the same steel type in one cast divided by the weight of one charge. The plan creation device according to any one of the above. A planning method for creating a plan for collectively producing and processing a plurality of products in units of lots,
An acquisition step of acquiring product information including information on the plurality of products including manufacturing conditions of the products;
Selecting a part of the plurality of products;
Among the subsets of the products selected by the selection step, the subsets satisfying the first lot inclusion enabling condition represented using the production conditions of the products as the conditions that the products can be included in the same lot A lot candidate derivation step of deriving as a lot candidate;
An optimization step of deriving an optimal lot candidate from the lot candidate derived in the lot candidate derivation step by performing an optimization calculation that maximizes or minimizes the value of an objective function within a range that satisfies a constraint expression;
A determination step of determining whether or not the result of the optimization calculation has converged;
When it is determined that the result of the optimization calculation is not converged by the determination step, a redefinition step of redefining the products included in the optimal lot candidate derived by the optimization step as one product When,
When the determination step determines that the result of the optimization calculation has converged, the optimal lot candidate derived by the optimization step is determined as an optimal lot, and the optimal lot includes which of the products. Outputting an information indicating information of
The objective function is a product of a first lot candidate derivation evaluation index including the number of lots, a weighting factor for the first lot candidate derivation evaluation index, and a second lot candidate derivation evaluation index including a surplus amount An objective function including a product of the second lot candidate derivation evaluation index and a weighting factor,
The surplus amount is an amount of a portion which becomes surplus in the lot when one or more of the products are put together in the lot,
At least one of the second lot candidate derivation metrics is classified according to classification conditions represented using manufacturing conditions of the product,
The weighting factor for the second lot candidate derivation evaluation index includes a weighting factor for each of the second lot candidate derivation evaluation indices classified according to the classification conditions represented using the manufacturing conditions of the product,
The constraint equation is that the number of products included in the optimal lot candidate is the same as the number of products selected in the selection step, and the same product is not included in different lot candidates. Contains the formula formulated
The optimization step is a process result data of the product regarding a weighting factor for the second lot candidate derivation evaluation index classified according to the classification condition represented using the manufacturing condition of the product, Derived based on manufacturing performance data classified according to classification conditions expressed using manufacturing conditions,
The selection step selects all of the products redefined by the redefinition step and some of the products not selected when the products are redefined by the redefinition step;
The selection of the product by the selection step, the derivation of the lot candidate by the lot candidate derivation step, and the optimization by the optimization step until the determination step determines that the result of the optimization calculation has converged A plan creation method characterized in that   The program for functioning a computer as each means of the plan creation apparatus in any one of Claims 1-11.

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