CN109358581B - Two-stream different-width batch planning optimization method for steelmaking-continuous casting process - Google Patents
Two-stream different-width batch planning optimization method for steelmaking-continuous casting process Download PDFInfo
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- 238000005457 optimization Methods 0.000 title claims abstract description 22
- 238000005266 casting Methods 0.000 claims abstract description 75
- 238000002922 simulated annealing Methods 0.000 claims abstract description 30
- 238000004519 manufacturing process Methods 0.000 claims abstract description 28
- 230000007717 exclusion Effects 0.000 claims description 20
- 229910000831 Steel Inorganic materials 0.000 claims description 19
- 239000010959 steel Substances 0.000 claims description 19
- 239000002436 steel type Substances 0.000 claims description 15
- 239000000463 material Substances 0.000 claims description 13
- 238000007670 refining Methods 0.000 claims description 9
- 238000010079 rubber tapping Methods 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 4
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Abstract
The invention provides a two-stream different-width batch planning optimization method for a steelmaking-continuous casting process, which comprises the following steps of: s1, establishing a heat-casting batch planning model of steel-making-continuous casting production scheduling; s2, optimizing the model in the step S1 by adopting a variable neighborhood combined simulated annealing algorithm. The method for optimizing the pouring batch plan of the steelmaking-continuous casting furnace group with two different widths is convenient and simple, has high calculation speed, and effectively overcomes the defect that the conventional method cannot solve the input of slabs with multiple varieties, multiple specifications and small batches.
Description
Technical Field
The invention relates to the field of production, in particular to a steelmaking-continuous casting process batch planning optimization method based on combination of simulated annealing and variable neighborhood searching.
Background
With the development of the steel industry, the orders of steel enterprises increasingly present the characteristics of multiple varieties, small batch and customization.
When the number of input contracts or slabs is small and specifications are numerous, the width values of two-flow crystallizers in the same heat are set to be the same numerical value in the existing batch plan optimization method, when the number of slabs is small, the width between heats is broken, the calculated residual material rate or the number of packages causes high cost, the profit is small, and satisfactory effects cannot be discharged.
Disclosure of Invention
According to the existing batch planning optimization method, the cost is high, the profit is low, and the satisfactory technical problem cannot be often discharged, so that the two-stream different-width batch planning optimization method for the steelmaking-continuous casting process is provided. The invention mainly utilizes a heat-casting batch planning model of steel-making-continuous casting production scheduling to design a Variable neighbor domain (VNS) combined Simulated Annealing (SA) algorithm for solving the model, thereby effectively solving the defect that the prior method can not solve the input of slabs with multiple varieties, multiple specifications and small batches.
The technical means adopted by the invention are as follows:
a two-stream different-width steelmaking-continuous casting process batch planning optimization method comprises the following steps:
s1, establishing a heat-casting batch planning model of steel-making-continuous casting production scheduling; s2, optimizing the model in the step S1 by adopting a variable neighborhood combined simulated annealing algorithm.
Further, in step S1, a half heat schedule is generated first, and then a half tundish schedule of the same steel type is generated, and a heat schedule is determined by the half tundish schedule, and after the heat schedule is determined, a casting schedule of continuous casting of different steel types and hot roll material constraint is considered, and the specific process is as follows:
s11, processing data;
dividing the work orders into different furnace-group work order mutual exclusion sets according to the unit, the refining path, the thickness and the tapping marks, and arranging the work orders in a descending order according to the allowable production width in the sets so as to make steel and furnace group;
s12, grouping a half-heat process;
s121, taking a first work order set of the work order mutual exclusion set, and performing the next step S122 for each work order;
s122, judging whether the work order set is empty or not, if the work order set is empty, deleting the work order set, and then performing the step S121, otherwise performing the next step S123;
s123, taking one work order in the work order set, and performing the next step S124;
s124, judging whether the heat set is empty or not, if so, establishing a half heat set, and generating a new half heat; according to a furnace combination rule, traversing a furnace set, judging whether the work order can be added into the current half furnace, if so, adding the work order into the current furnace and deleting the work order from the work order set, if not, newly building a half furnace, adding the work order into the half furnace, adding the half furnace into the furnace set, deleting the work order from the work order set, and then turning to the step S125;
s125, judging whether the work order mutex set is empty, if so, turning to the step S13, otherwise, turning to the step S121;
s13, grouping a pre-tundish process;
s131, taking a first half heat of the heat set, and performing the step S132 aiming at the first half heat;
s132, judging whether the pre-tundish set is empty, if so, establishing a pre-tundish set and generating a new pre-tundish; traversing a pre-tundish set according to a set of pre-tundish rules, judging whether the half heat can be added to the current pre-tundish, if so, adding the work order to the current pre-tundish and deleting the half heat from the half heat set, if not, newly building a pre-tundish, adding the half heat to the pre-tundish and deleting the half heat from the heat set; go to step S133;
s133, judging whether the half heat set is empty, wherein the step is an idling step S14, and otherwise, turning to the step S131;
s14, grouping a tundish process;
s141, dividing the pre-tundish into different group casting mutual exclusion sets according to the unit, the refining path, the thickness and the tapping mark, and arranging the pre-tundish in the sets in a descending order according to the number of half furnaces;
s142, judging whether the tundish set is empty, if the tundish set is empty, establishing a tundish set, taking the first two pre-tundishes in the pre-tundish set, judging whether the half-heat quantity is equal, if the half-heat quantity is equal, combining the tundish sets, putting the tundish sets into the tundish set, if the half-heat quantity is not equal, cutting off the difference of the half-heat of the tundish with large quantity, putting the cut part into the pre-tundish set, combining the remaining two pre-tundishes to form a tundish, putting the tundish set into the tundish set, and turning to the step S143;
s143, judging whether the pre-tundish set is empty, if the pre-tundish set is empty, deleting the pre-tundish set, and taking the next pre-tundish set in the mutual exclusion set, if the group of tundish mutual exclusion sets is empty, turning to the step S15, otherwise, turning to the step S142;
s15, determining the heat;
s151, traversing the tundish set, taking the tundish in sequence, and turning to the step S152;
s152, traversing the tundish, taking a half heat at a position corresponding to the left and right flows in the same tundish to form a heat, and setting the heat attribute of the half heat;
s153, judging whether the traversal of the tundish set is completed, and turning to the step S16 if the traversal of the tundish set is completed, or turning to the step S151 if the traversal of the tundish set is not completed;
s16, considering continuous casting of different steel types, and determining a casting time plan;
s161, according to the continuous casting group casting rule, dividing the casting times which are not combined with the optimal economic furnace number in the casting time set into different mutually exclusive continuous casting sets, and turning to the step S162;
s162, taking one continuous casting set, traversing casting times, and judging whether the widths of the two casting times can be connected or not, so that the two casting times are combined into one continuous casting; if the two watering times can be combined, combining the two watering times, and if the two watering times cannot be combined, taking the next watering time for judgment until the watering time combination is completed; go to step S163;
and S163, judging whether the traversal of the mutual exclusion continuous casting set is completed, if so, entering an optimization step, and otherwise, turning to the step S162.
Further, after an initial solution is generated according to the specific steps in the step S1, optimization is performed in a step S2 by using a variable neighborhood combined simulated annealing algorithm; taking the heat plan as an example, the following neighborhood structure is defined: n1-exchange slabs; the operation is performed by exchanging slabs of different heats, and if the operation satisfies the constraint and improves the objective function, the exchange is accepted; n2-exchanging slabs by exchanging slabs of different heats, accepting the exchange if the operation satisfies the constraints and improves the objective function; the neighborhood structure of the casting plan is the same as the furnace plan; because the model is extremely constrained, and the neighborhood transformation is accepted in a greedy manner, if the solution falls into the local optimal solution, in order to solve the problem, when the solution falls into the local optimal solution, a new solution is obtained by adopting a simulated annealing algorithm, and then the neighborhood-variable search is carried out in an iterative manner, so that an acceptable solution is obtained.
The algorithm adopted comprises the following steps:
s21, setting the initial solution generated by the process as S0Setting parameters of a simulated annealing algorithm;
s22 solution S0Adopting inner layer simulated annealing to obtain a new solution S';
s221, judging whether i is greater than m, wherein m is the total number of the heat, if so, turning to a step S222, otherwise, turning to a step S22;
s222, on the basis of the current solution, randomly exchanging the heat to obtain a new solution S'; calculating the objective function value of the new solution S, and the objective function difference Delta E ═ S' -S0(ii) a Deciding whether to accept a new solution if Δ E < 0 or E-ΔE/TIf the probability is greater than the random probability p, the solution S' is reserved; i + +;
s223, cooling, and if T is T · Δ T, go to step S23;
s23 solution S0Performing Variable Neighborhood Search (VNS), and setting an initial parameter k to be 1; go to step S231;
s231, judging whether k is smaller than 4, and if so, using a neighborhood structure NkSearching until trapping in the local optimal solution S*If S is*Is superior to S0Then order S0＝S*K is 0; otherwise k + +; if k > ═ 4, go to step S24;
s24, performing step 2 on solution S 'to obtain solution S'*(ii) a Judging whether the solution is better than S*If yes, accepting and assigning value to S*；
S25, judging whether T is less than 40, if yes, outputting a solution, otherwise, turning to the step S23.
Further, there are performance indicators: the steel grade difference of the production work orders grouped into each heat is as small as possible; the difference of the same flow width of the production work orders grouped to each heat is as small as possible; the grade difference of steel grades between adjacent furnaces of the same tundish is as small as possible; the width difference between adjacent heats grouped to the same tundish is as small as possible.
Compared with the prior art, the two-flow different-width steelmaking-continuous casting process batch planning optimization method mainly comprises two processes, wherein a heuristic algorithm is adopted to construct an initial solution, and then a variable neighborhood combined simulated annealing algorithm is adopted to optimize the initial solution. The initial solution process can be divided into a furnace grouping process and a casting grouping process, the effectiveness of the model and the algorithm is verified and tested by utilizing the actual production cases of enterprises, and the result shows that the steelmaking-continuous casting batch planning model and the VNS are effective in solving the problems by combining the SA algorithm.
The width processing in the batch plan optimization algorithm plays a decisive role in the optimization algorithm result, aiming at small batch contract orders, the field planning usually considers the widths of the two flows of the continuous casting machine separately, the widths of the two-flow crystallizer are set according to the number of the plate blanks, so that the plate blanks with different widths are combined together on the premise of meeting production constraints, the plate blank width difference corresponding to the two flows in the same heat is large, the production can be reasonably arranged, and the number of tundishes is reduced.
Therefore, the situation that the amount of the same product is small but production is needed to be arranged urgently can be solved by adopting the two-flow different-width furnace group pouring. Under the condition of describing the problems of steelmaking-continuous casting batch planning of the steel mill, the invention considers the difference width of two streams, decomposes the heat plan into a half heat plan, macroscopically determines the other half heat corresponding to the half heat when a tundish is assembled to form the heat, and recombines the casting times under the condition of determining the heat. Therefore, the problems that the tundish quantity is large and the tundish cannot be combined due to the fact that the plate blanks are various in variety and small in batch are solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flowchart illustrating step S1 according to the present invention.
FIG. 2 is a flowchart of step S2 according to the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention provides a two-stream different-width batch planning optimization method for a steelmaking-continuous casting process, which comprises the following steps of:
s1, establishing a heat-casting batch planning model of steel-making-continuous casting production scheduling;
firstly, generating a half-furnace plan, further generating a half-tundish plan of the same steel grade, determining the furnace plan according to the half-furnace plan, and considering a casting schedule of continuous casting of different steel grades and hot roll material constraint after determining the furnace plan, wherein the specific process comprises the following steps:
s11, processing data;
dividing the work orders into different furnace-group work order mutual exclusion sets according to the unit, the refining path, the thickness and the tapping marks, and arranging the work orders in a descending order according to the allowable production width in the sets so as to make steel and furnace group;
s12, grouping a half-heat process;
s121, taking a first work order set of the work order mutual exclusion set, and performing the next step S122 for each work order;
s122, judging whether the work order set is empty or not, if the work order set is empty, deleting the work order set, and then performing the step S121, otherwise performing the next step S123;
s123, taking one work order in the work order set, and performing the next step S124;
s124, judging whether the heat set is empty or not, if so, establishing a half heat set, and generating a new half heat; according to a furnace combination rule, traversing a furnace set, judging whether the work order can be added into the current half furnace, if so, adding the work order into the current furnace and deleting the work order from the work order set, if not, newly building a half furnace, adding the work order into the half furnace, adding the half furnace into the furnace set, deleting the work order from the work order set, and then turning to the step S125;
s125, judging whether the work order mutex set is empty, if so, turning to the step S13, otherwise, turning to the step S121;
s13, grouping a pre-tundish process;
s131, taking a first half heat of the heat set, and performing the step S132 aiming at the first half heat;
s132, judging whether the pre-tundish set is empty, if so, establishing a pre-tundish set and generating a new pre-tundish; traversing a pre-tundish set according to a set of pre-tundish rules, judging whether the half heat can be added to the current pre-tundish, if so, adding the work order to the current pre-tundish and deleting the half heat from the half heat set, if not, newly building a pre-tundish, adding the half heat to the pre-tundish and deleting the half heat from the heat set; go to step S133;
s133, judging whether the half heat set is empty, wherein the step is an idling step S14, and otherwise, turning to the step S131;
s14, grouping a tundish process;
s141, dividing the pre-tundish into different group casting mutual exclusion sets according to the unit, the refining path, the thickness and the tapping mark, and arranging the pre-tundish in the sets in a descending order according to the number of half furnaces;
s142, judging whether the tundish set is empty, if the tundish set is empty, establishing a tundish set, taking the first two pre-tundishes in the pre-tundish set, judging whether the half-heat quantity is equal, if the half-heat quantity is equal, combining the tundish sets, putting the tundish sets into the tundish set, if the half-heat quantity is not equal, cutting off the difference of the half-heat of the tundish with large quantity, putting the cut part into the pre-tundish set, combining the remaining two pre-tundishes to form a tundish, putting the tundish set into the tundish set, and turning to the step S143;
s143, judging whether the pre-tundish set is empty, if the pre-tundish set is empty, deleting the pre-tundish set, and taking the next pre-tundish set in the mutual exclusion set, if the group of tundish mutual exclusion sets is empty, turning to the step S15, otherwise, turning to the step S142;
s15, determining the heat;
s151, traversing the tundish set, taking the tundish in sequence, and turning to the step S152;
s152, traversing the tundish, taking a half heat at a position corresponding to the left and right flows in the same tundish to form a heat, and setting the heat attribute of the half heat;
s153, judging whether the traversal of the tundish set is completed, and turning to the step S16 if the traversal of the tundish set is completed, or turning to the step S151 if the traversal of the tundish set is not completed;
s16, considering continuous casting of different steel types, and determining a casting time plan;
s161, according to the continuous casting group casting rule, dividing the casting times which are not combined with the optimal economic furnace number in the casting time set into different mutually exclusive continuous casting sets, and turning to the step S162;
s162, taking one continuous casting set, traversing casting times, and judging whether the widths of the two casting times can be connected or not, so that the two casting times are combined into one continuous casting; if the two watering times can be combined, combining the two watering times, and if the two watering times cannot be combined, taking the next watering time for judgment until the watering time combination is completed; go to step S163;
and S163, judging whether the traversal of the mutual exclusion continuous casting set is completed, if so, entering an optimization step, and otherwise, turning to the step S162.
S2, optimizing the model in the step S1 by adopting Variable Neighbor (VNS) and combining Simulated Annealing (SA) algorithm.
After generating the initial solution according to the specific steps in step S1, in step S2, optimization is performed using a varied neighbor domain (VNS) in combination with a Simulated Annealing (SA) algorithm; taking the heat plan as an example, the following neighborhood structure is defined: n1-exchange slabs; the operation is performed by exchanging slabs of different heats, and if the operation satisfies the constraint and improves the objective function, the exchange is accepted; n2-exchanging the slabs, the operation accepting the exchange by exchanging slabs of different heats if the operation satisfies the constraints and improves the objective function.
The neighborhood structure of the casting plan is the same as the furnace plan; because the model is extremely constrained, and the neighborhood transformation is accepted in a greedy manner, if the local optimal solution is trapped, in order to solve the problem, when the local optimal solution is trapped, a new solution is obtained by adopting a Simulated Annealing (SA) algorithm, and then the variable neighborhood search is iterated, so that an acceptable solution is obtained;
the algorithm adopted comprises the following steps:
s21, setting the initial solution generated by the process as S0Setting parameters of a simulated annealing algorithm;
s22 solution S0A new solution S' is obtained using inner layer Simulated Annealing (SA).
S221, judging whether i is greater than m, wherein m is the total number of the heat, if so, turning to a step S222, otherwise, turning to a step S22;
s222, on the basis of the current solution, randomly exchanging the heat to obtain a new solution S'; calculating the objective function value of the new solution S, and the objective function difference Delta E ═ S' -S0(ii) a Deciding whether to accept a new solution if Δ E < 0 or E-ΔE/TIf the probability is greater than the random probability p, the solution S' is reserved; i + +;
s223, cooling, and if T is T · Δ T, go to step S23;
s23 solution S0Performing Variable Neighborhood Search (VNS), and setting an initial parameter k to be 1; go to step S231;
s231, judging whether k is smaller than 4, and if so, using a neighborhood structure NkSearching until trapping in the local optimal solution S*If S is*Is superior to S0Then order S0＝S*K is 0; otherwise k + +; if k > ═ 4, go to step S24;
s24, performing step 2 on solution S 'to obtain solution S'*(ii) a Judging whether the solution is better than S*If yes, accepting and assigning value to S*；
S25, judging whether T is less than 40, if yes, outputting a solution, otherwise, turning to the step S23.
Has the performance indexes that: the steel grade difference of the production work orders grouped into each heat is as small as possible; the difference of the same flow width of the production work orders grouped to each heat is as small as possible; the grade difference of steel grades between adjacent furnaces of the same tundish is as small as possible; the width difference between adjacent heats grouped to the same tundish is as small as possible.
The invention relates to a batch planning optimization method for a steelmaking-continuous casting process with two flows of different widths, which combines the background that an order contract in actual production is mostly small batch and customized, fully considers the condition that two-flow crystallizers of a double-flow continuous casting machine can have different widths, establishes a furnace-group-casting batch planning model meeting steelmaking continuous casting process constraints, designs a heuristic algorithm to solve the problem, and optimizes the result by using a variable neighborhood search algorithm. Simulation verification is carried out on the actual production data of a certain steel mill in one day. From simulation results, the widths of the left flow and the right flow in the tundish are inconsistent, the widths of the furnaces are smoothly connected, and compared with the actual scheduling result on the same day, the excess material rate is reduced by 5.5%, and the total weight of the excess material is reduced by 980 t. The number of the furnace times is reduced by 4 furnaces, and the number of the tundish is reduced by 3. Obviously superior to manual operation and brings considerable economic benefit for enterprises. Compared with manual planning, the method reduces planning time and improves planning quality.
The invention relates to a method for optimizing a batch plan in a steelmaking-continuous casting process with two flows different in width, which comprises a furnace grouping process and a casting grouping process, wherein the problems can be divided into a furnace frequency problem and a casting frequency problem, and the two flows different in width need to be considered, so that a half furnace frequency plan is firstly generated, a half tundish plan and a tundish plan of the same steel type are further generated, the furnace frequency plan is determined, and after the furnace frequency plan is determined, the casting frequency plan of different steel type continuous casting and hot roll material constraint is considered, so that the method is the method for optimizing the batch plan in the steelmaking-continuous casting process with two flows different in width.
Example 1
In the examples, the letter symbols used and their meanings are shown in the following table:
decision variables
In order to facilitate the establishment and the solution of a mathematical model for the casting problem of a single group of production furnaces, firstly, the performance index of the problem is transformed, and then, the key elements of modeling such as the target, the constraint condition, the decision variable and the like of the problem are described.
The performance indexes are respectively as follows:
the steel grade differences of the production orders grouped into the individual heats are as small as possible.
The difference of the same flow width of the production work orders grouped to each heat is as small as possible.
The grade difference between adjacent heats grouped to the same tundish is as small as possible.
The width difference between adjacent heats grouped to the same tundish is as small as possible.
Wherein the constraint conditions are as follows:
all work orders are scheduled, i.e., each work order is grouped in a furnace plan.
All heats are scheduled, i.e., it is ensured that the heats are scheduled in a certain pour schedule.
The furnace combination rule is that the total weight of work orders in the same furnace is smaller than the maximum standard of the furnace weight and larger than the minimum standard, the machine set, the thickness, the refining path and the tapping marks of the work orders are required to be the same and have similar widths, the difference of the widths in the same furnace is smaller than the width adjusting range, and the work orders in different sets are not allowed to be in the same furnace according to data preprocessing.
The group casting rule is that the number of furnaces in the same casting time is smaller than the optimal economic number of furnaces, the units, the thickness and the refining path of the same casting time are the same, mixed casting can be carried out if the steel types of the same casting time are different, and work orders in different sets are not allowed to be in the same casting time according to data preprocessing.
The weight of the heat is not less than the minimum weight of the furnace of the unit and not more than the maximum weight of the furnace.
The maximum continuous casting furnace number of the casting times is the minimum value of the maximum continuous casting furnace numbers corresponding to all the furnace steel types in the casting times.
The width difference of adjacent slabs in the same heat is smaller than the maximum value allowed by width adjustment.
The width difference of the slabs connected with the adjacent furnaces in the same casting time is smaller than the maximum value allowed by width adjustment.
The weight of the plate blank of the inner ironing roller material in the casting time is less than the maximum weight of the ironing roller material allowed to be produced.
In order to facilitate the establishment and the solution of the mathematical model, data preprocessing is required, and the method mainly comprises the following two points:
(1) and classifying the work orders. Classifying the worksheet set I according to the machine set, the steel type and the refining path, arranging the worksheets produced in the same machine set and of the same steel type into the same category, and totally classifying the worksheets into K categories, namely
I＝G1∪G2∪...∪GK。
(2) A set of mutual exclusion rules. Because of the requirement of the same steel type and the organic group in the furnace combination rule, the mutual exclusion set of the work group furnaces is established according to the requirement, and the work orders which can not be combined in the same furnace are listed in the set, namely
S1＝{(i，j)|i∈Gk1，j∈Gk2，k1≠k2}
The group casting rules have the same specification, the continuous casting can be carried out only when the steel species can be continuously cast, and the group casting exclusive set can be obtained in the same way, namely
S2＝{(i，j)|i∈Gk3，j∈Gk4，k3≠k4}
Based on the above analysis, a group furnace-group casting mathematical model is designed as follows:
the method comprises the following steps of (1) obtaining a casting time, wherein the formula (1) is an objective function, the first term is the minimum casting time quantity, the second term is the penalty of the width difference of adjacent slabs in the furnace time, and the third term is the penalty value of the width of the adjacent furnace time in the casting time. Constraint (2) indicates that all work orders are scheduled, i.e., each work order is grouped in a particular furnace plan. Equation (3) indicates that all heats are scheduled, i.e., ensuring that the heats are scheduled in a particular pour schedule. And (4) the formula (4) is a furnace grouping rule, and work orders in different sets are not allowed to be in the same heat according to data preprocessing. And (5) the formula (5) is a group casting rule, and work orders in different sets are not allowed to be in the same casting time according to data preprocessing. The formula (6) represents the weight of the heat, the weight is related to the unit to which the slab is distributed, and the weight of the furnace is not less than the minimum weight of the unit and not more than the maximum weight of the furnace. And (4) the formula (7) represents the maximum continuous casting furnace number, and the maximum continuous casting furnace number of the casting times is the minimum value of the maximum continuous casting furnace numbers corresponding to all the furnace steel types in the casting times. The expression (8) shows that the width difference of adjacent slabs in the same heat is smaller than the maximum value allowed by width adjustment. And (9) the width difference of the slabs connected with the adjacent furnaces in the same casting time is smaller than the maximum value allowed by width adjustment. The formula (10) shows that the weight of the blank of the inner ironing roller material of the casting time is less than the maximum weight of the ironing roller material allowed to be produced.
The problem of large-scale actual production cannot be solved due to the complex solving of the mathematical model and low effectiveness, and the corresponding relation between the plate blank in the virtual furnace and the left and right crystallizers of the continuous casting machine is difficult to express by mathematical symbols, so that the problem is solved by adopting a heuristic method.
And (4) combining simulated annealing and variable neighborhood search.
This embodiment proposes a three-stage furnace group casting strategy: the method comprises the steps of data preprocessing, initial solution construction of a group furnace group casting plan, and optimization of the group furnace group casting plan based on simulated annealing and variable neighborhood searching. Wherein, the construction of the initial solution of the group furnace group casting plan comprises the following steps: the production worker single group is half a stove, half stove is merged into half and half is poured, half is poured and combined into the tundish, the tundish is split the stove four parts. The optimization process of the group furnace group casting plan based on simulated annealing and variable neighborhood search comprises the following steps: constructing a variable neighborhood search algorithm neighborhood, and simulating annealing search local optimum to obtain a perturbation solution. The group furnace group casting plan meets a plurality of performance indexes that the number of the tundishes is minimum, the width difference between adjacent furnace ranks in the same tundish is as small as possible, the grade difference of steel grades is as small as possible, the width difference between adjacent plate blanks in the same flow in the furnace is as small as possible, the residual material rate is as small as possible and the like, and the performance indexes reach better solutions as far as possible. And the requirements of the next process on the hot roller material in the tundish are met, and the production process constraint is met.
And (5) simulation results and analysis.
In order to verify the effect of a multi-target model for furnace number-casting number planning and VNS combined SA algorithm for solving the problem of different widths of left and right flows in a casting number, the actual data of a certain steel production enterprise is adopted for carrying out simulation experiments. Taking 1 day contract day plan data, containing 732 slabs of A1, A2 and A9 units, wherein the standard furnace weights of the 3 units are 150t, 250t and 150t respectively, the left and right flows in the furnace are allowed to be widened once respectively, and the widening range is 150 mm. The parameters are set as follows:
the SA algorithm part T1 is 100, T2 is 60, and T is 0.8. And solving by using the model to obtain the heat and casting time plan of the current day. Only a portion of the results are listed due to the large amount of data.
Table 1 shows a partial pour schedule, and table 2 shows a 106-pour partial sub-furnace schedule.
TABLE 1 casting plan (part)
Table 2106 casting heat plan (part)
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (2)
1. A two-stream different-width batch planning optimization method for a steelmaking-continuous casting process is characterized by comprising the following steps of:
s1, establishing a heat-casting batch planning model of steel-making-continuous casting production scheduling;
s2, optimizing the model in the step S1 by adopting a variable neighborhood combined simulated annealing algorithm;
in step S1, a half-heat plan is generated first, and then a half-tundish plan and a tundish plan of the same steel type are generated, so as to determine the heat plan, and after the heat plan is determined, a casting plan of continuous casting of different steel types and constraint of hot roll materials is considered, and the specific process is as follows:
s11, processing data;
dividing the work orders into different furnace-group work order mutual exclusion sets according to the unit, the refining path, the thickness and the tapping marks, and arranging the work orders in a descending order according to the allowable production width in the sets so as to make steel and furnace group;
s12, grouping a half-heat process;
s121, taking a first work order set of the work order mutual exclusion set, and performing the next step S122 for each work order;
s122, judging whether the work order set is empty or not, if the work order set is empty, deleting the work order set, and then performing the step S121, otherwise performing the next step S123;
s123, taking one work order in the work order set, and performing the next step S124;
s124, judging whether the heat set is empty or not, if so, establishing a half heat set, and generating a new half heat; according to a furnace combination rule, traversing a furnace set, judging whether the work order can be added into the current half furnace, if so, adding the work order into the current furnace and deleting the work order from the work order set, if not, newly building a half furnace, adding the work order into the half furnace, adding the half furnace into the furnace set, deleting the work order from the work order set, and then turning to the step S125;
s125, judging whether the work order mutex set is empty, if so, turning to the step S13, otherwise, turning to the step S121;
s13, grouping a pre-tundish process;
s131, taking a first half heat of the heat set, and performing the step S132 aiming at the first half heat;
s132, judging whether the pre-tundish set is empty, if so, establishing a pre-tundish set and generating a new pre-tundish; traversing a pre-tundish set according to a set of pre-tundish rules, judging whether the half heat can be added to the current pre-tundish, if so, adding the work order to the current pre-tundish and deleting the half heat from the half heat set, if not, newly building a pre-tundish, adding the half heat to the pre-tundish and deleting the half heat from the heat set; go to step S133;
s133, judging whether the half heat set is empty, wherein the step is an idling step S14, and otherwise, turning to the step S131;
s14, grouping a tundish process;
s141, dividing the pre-tundish into different group casting mutual exclusion sets according to the unit, the refining path, the thickness and the tapping mark, and arranging the pre-tundish in the sets in a descending order according to the number of half furnaces;
s142, judging whether the tundish set is empty, if the tundish set is empty, establishing a tundish set, taking the first two pre-tundishes in the pre-tundish set, judging whether the half-heat quantity is equal, if the half-heat quantity is equal, combining the tundish sets, putting the tundish sets into the tundish set, if the half-heat quantity is not equal, cutting off the difference of the half-heat of the tundish with large quantity, putting the cut part into the pre-tundish set, combining the remaining two pre-tundishes to form a tundish, putting the tundish set into the tundish set, and turning to the step S143;
s143, judging whether the pre-tundish set is empty, if the pre-tundish set is empty, deleting the pre-tundish set, and taking the next pre-tundish set in the mutual exclusion set, if the group of tundish mutual exclusion sets is empty, turning to the step S15, otherwise, turning to the step S142;
s15, determining the heat;
s151, traversing the tundish set, taking the tundish in sequence, and turning to the step S152;
s152, traversing the tundish, taking a half heat at a position corresponding to the left and right flows in the same tundish to form a heat, and setting the heat attribute of the half heat;
s153, judging whether the traversal of the tundish set is completed, and turning to the step S16 if the traversal of the tundish set is completed, or turning to the step S151 if the traversal of the tundish set is not completed;
s16, considering continuous casting of different steel types, and determining a casting time plan;
s161, according to the continuous casting group casting rule, dividing the casting times which are not combined with the optimal economic furnace number in the casting time set into different mutually exclusive continuous casting sets, and turning to the step S162;
s162, taking one continuous casting set, traversing casting times, and judging whether the widths of the two casting times can be connected or not, so that the two casting times are combined into one continuous casting; if the two watering times can be combined, combining the two watering times, and if the two watering times cannot be combined, taking the next watering time for judgment until the watering time combination is completed; go to step S163;
s163, judging whether the traversal of the mutual exclusion continuous casting set is completed, if so, entering an optimization step, otherwise, turning to the step S162;
generating an initial solution according to the specific steps in the step S1, and then optimizing by using a variable neighborhood combined simulated annealing algorithm in the step S2;
taking the heat plan as an example, the following neighborhood structure is defined: n1-exchange slabs;
the operation is performed by exchanging slabs of different heats, and if the operation satisfies the constraint and improves the objective function, the exchange is accepted;
n2-exchanging slabs by exchanging slabs of different heats, accepting the exchange if the operation satisfies the constraints and improves the objective function;
the neighborhood structure of the casting plan is the same as the furnace plan;
because the model is extremely constrained, and the neighborhood transformation is accepted in a greedy manner, if the solution falls into the local optimal solution, in order to solve the problem, when the solution falls into the local optimal solution, a new solution is obtained by adopting a simulated annealing algorithm, and then the neighborhood-variable search is carried out in an iterative manner, so that an acceptable solution is obtained;
the algorithm adopted comprises the following steps:
s21, setting the initial solution generated by the process as S0Setting parameters of a simulated annealing algorithm;
s22 solution S0Adopting inner layer simulated annealing to obtain a new solution S';
s221, judging whether i is greater than m, wherein m is the total number of the heat, if so, turning to a step S222, otherwise, turning to a step S22;
s222, on the basis of the current solution, randomly exchanging the heat to obtain a new solution S'; calculating the objective function value of the new solution S, and the objective function difference Delta E ═ S' -S0(ii) a Deciding whether to accept a new solution, if Δ E<0 or e-ΔE/TIf the probability is greater than the random probability p, the solution S' is reserved; i + +;
s223, cooling, and if T is T · Δ T, go to step S23;
s23 solution S0Performing Variable Neighborhood Search (VNS), and setting an initial parameter k to be 1; step S is transferred231；
S231, judging whether k is smaller than 4, and if so, using a neighborhood structure NkSearching until trapping in the local optimal solution S*If S is*Is superior to S0Then order S0＝S*K is 0; otherwise k + +; if k is>If yes, go to step S24;
s24 solution S′Step S2 is performed to obtain solution S′*(ii) a Judging whether the solution is better than S*If yes, accepting and assigning value to S*；
S25, judging whether T is less than 40, if yes, outputting a solution, otherwise, turning to the step S23.
2. The method of claim 1, wherein the two-stream differencing steelmaking-continuous casting process lot plan optimization,
has the performance indexes that: the steel grade difference of the production work orders grouped into each heat is as small as possible; the difference of the same flow width of the production work orders grouped to each heat is as small as possible; the grade difference of steel grades between adjacent furnaces of the same tundish is as small as possible; the width difference between adjacent heats grouped to the same tundish is as small as possible.
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