JP2013122733A - Production planning program and production planning device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a production planning device or the like giving flexibility to an operating range of production planning.SOLUTION: A production planning device 1 includes a CPU 17. The CPU 17 acquires plan data including items identifying components of product and a phase for manufacturing the components, and master data including production capability of a production line of the components. Also, the CPU 17 defines a three-dimensional element whose elements are the items related to the components, the line and the phase. The CPU 17 selects a unique constraint to the components or the line according to a predetermined operation, and generates a mathematical model of integer programming which includes the selected unique constraint and a predetermined common constraint and allocates an optimum amount of production of the item for each three-dimensional element. Further, the CPU 17 executes each process allocating the optimum amount of production of the item for each three-dimensional element satisfying the unique constraint and the common constraint by executing the mathematical model.

Description

  The present invention relates to a production planning program and a production planning apparatus.

  For example, in a manufacturing factory that manufactures various types of products in a plurality of processes, a production plan for the product is made. In production planning, it is necessary to efficiently produce each part in a product within the planned time using limited resources such as production lines (hereinafter simply referred to as lines), personnel and materials. The question is when to allocate resources, which parts, and which lines.

  Therefore, the “load pile / climbing method” is mainly used in a production plan prepared by a person in charge of process management at a manufacturing plant. In this method, the production start time is provisionally determined by calculating back the production lead time from the scheduled completion date of the product. Then, the working time of the manufacturing process is accumulated for each temporarily determined day, and the working time is compared with the production capacity of the manufacturing process for one day. And the production start date is advanced ahead for the amount exceeding the production capacity. Then, this work is applied to all schedules, for example, usually for one month, to all manufacturing processes and all products to level the load. As a result, an optimal production result can be planned.

  However, in the “load pile / collapse method”, as the production scale increases, the number of competing parts increases in the manufacturing process, so that adjustment takes a long time. In addition, as the production scale increases, the production load tends to fluctuate and the number of extra factory inventory increases. Therefore, it is conceivable to implement the “load pile / collapse method” in a general-purpose programming language. However, it is necessary to repeatedly perform load pile calculation and load balance of the load collapse. As a result, the number of steps in the program becomes as large as several thousand steps, and it takes a long time to develop the program. Moreover, the number of cases where the program is corrected every time the configuration of the manufacturing process is changed.

Further, when viewed from the theoretical plane, for assigning assignments of n order to the order of the input line to the m lines, the method is (n!) ^M street. This is an NP (Non-deterministic Polynomial) complete problem in which the total number of combinations increases exponentially with respect to the number of lines. Therefore, a huge amount of calculation is required to obtain the allocation solution. Therefore, optimization solutions devised to minimize the calculation time have been studied.

  As an optimization solution method, there is a mathematical programming method in which an optimal solution is obtained by modeling with a mathematical expression composed of an objective function and constraint conditions. Moreover, a mathematical model of integer programming is known as a method for obtaining a discrete optimal solution of production planning in mathematical programming.

JP 2005-92726 A

Kenji Muramatsu, “Simultaneous Optimization of Lot Size and Order”, Japan Society for Management Engineering, Vol. 52, No. 5, December 2001 Takayoshi Tamura and Katsuhito Hitomi, "Study on lot scheduling of multi-product / multi-period based on minimizing the number of setup changes", Japan Society of Mechanical Engineers, 1978 Nobuo Watanabe, Shun Anjo and Hidesaku Hiraki, "Mathematical planning approach to the pull-type production instruction method", Japan Society for Management Engineering, Vol.44, No.6, pp.478-486 February 1994

  However, the mathematical model of the integer programming method for obtaining the optimal solution of the production plan has a fixed constraint condition, so that the production plan of the applicable manufacturing process is also limited. As a result, the application range of the mathematical model becomes limited, and the operation range of the production plan is limited.

  In one aspect, an object of the present invention is to provide a production planning program and a production planning apparatus that can give flexibility to the operation range of a production plan.

  An aspect of the disclosure obtains plan data including an item for identifying a part of a product and a period for manufacturing the part, and master data including a production capacity of a line for manufacturing the part, and the item related to the part, A three-dimensional element having the line and the period as elements is defined. According to a disclosed aspect, the three-dimensional element includes a constraint condition specific to the part or the line selected according to a predetermined operation, and the selected unique constraint condition and a predetermined common constraint condition Each time, a mathematical model of integer programming that allocates the optimum production amount of the item is generated. Further, according to an aspect of the disclosure, by executing the mathematical model, each process of assigning an optimum production amount of the item for each of the three-dimensional elements satisfying the unique constraint condition and the common constraint condition is performed by a computer. To run.

  In the disclosed aspect, the operation range of the production plan can be given flexibility.

FIG. 1 is an explanatory diagram illustrating an example of the configuration of the production planning apparatus according to the present embodiment. FIG. 2 is an explanatory diagram showing the flow from the order of the product to the shipment and the positioning of the production planning apparatus of the present embodiment. FIG. 3 is an explanatory diagram illustrating an example of a component configuration of a product. FIG. 4 is an explanatory diagram showing an example of the flow of the manufacturing process. FIG. 5 is an explanatory diagram showing an example of a plan order in the plan database. FIG. 6 is an explanatory diagram showing an example of data in the mounting time table. FIG. 7 is an explanatory diagram showing an example of data in the piece number table. FIG. 8 is an explanatory diagram illustrating an example of a mathematical model setting screen. FIG. 9 is a flowchart illustrating an example of the processing operation of the CPU related to the production plan process. FIG. 10 is a flowchart illustrating an example of the processing operation of the CPU related to the model file generation process. FIG. 11 is an explanatory diagram showing an example of a high priority plan order. FIG. 12 is an explanatory diagram illustrating an example of data in the line-item-specific production amount table. FIG. 13 is an explanatory diagram showing an example of data in the line-item-specific production time table. FIG. 14 is an explanatory diagram of an example of the previously assigned table. FIG. 15 is an explanatory diagram showing an example of a low priority plan order. FIG. 16 is an explanatory diagram showing an example of data in the production quantity table by line-item. FIG. 17 is an explanatory diagram of an example of data in the line-item-specific production time table. FIG. 18 is an explanatory diagram showing an example of a Gantt chart for maximizing the equipment operation rate.

  Embodiments of a production planning program and a production planning apparatus disclosed in the present application will be described below in detail with reference to the drawings. The disclosed technology is not limited by the present embodiment.

  FIG. 1 is an explanatory diagram illustrating an example of the configuration of the production planning apparatus according to the present embodiment. A production planning apparatus 1 shown in FIG. 1 devises a production plan that instructs when and in which manufacturing process each product is manufactured in a manufacturing factory that manufactures various types of products in a plurality of manufacturing processes. The production planning apparatus 1 is constituted by, for example, a personal computer (hereinafter simply referred to as a personal computer).

  1 includes a keyboard 11, a display 12, a communication interface 13, an external medium drive device 14, an HDD (Hard Disk Drive) 15, a main memory 16, and a CPU (Central Processing Unit). 17 and a system bus 18. The keyboard 11 inputs various commands and data. The display 12 displays various information on the screen. The communication interface 13 is connected to an external network (not shown) and communicates with the external network. The external medium driving device 14 drives an external storage medium such as a CD-ROM (Compact Disk-Read Only Memory) or a USB (Universal Serial Bus) memory. The HDD 15 stores various information. The main memory 16 is a memory area, such as a RAM (Random Access Memory), for developing a program executed by the CPU 17. The CPU 17 controls the production planning apparatus 1 as a whole.

  For example, the external medium driving device 14 reads a production plan program stored in a CD-ROM, and expands the read production plan program in the main memory 16. The CPU 17 executes production plan processing based on the production plan program developed in the main memory 16.

  Next, an overall mechanism to which the production planning apparatus 1 of the present embodiment is applied will be described. FIG. 2 is an explanatory diagram showing the flow from product ordering to shipment and the positioning of the production planning apparatus 1 of the present embodiment. The mechanism shown in FIG. 2 can be broadly classified into a supply chain, a related system, and a manufacturing system. The supply chain continues from customer 101 to sales 102, production 103, purchasing 104, manufacturing 105, and shipping 106. In sales 102, preparation information is input to the production management system 201 based on the order of the product from the customer 101 and the expected order.

  In the production management system 201, based on the preparation information input in the sales 102, the demand amount and ordering time of parts necessary for product production from the technical information (BOM: Bill of Material) 202 and the inventory information in the warehouse 205 Develop MRP (Material Requirements Planning). In the production management system 201, an external work order to be ordered from an external contractor or an internal work order to be ordered in-house is created according to the demand amount of parts and the ordering timing.

  The purchase acceptance system 203 instructs the purchase 104 to purchase a product based on the external work order from the production management system 201. The manufacturing management system 204 creates a planned order to which a completion delivery date is given for each product based on the in-house order from the production management system 201, and inputs the planned order to the production planning apparatus 1 described above.

  The production planning device 1 allocates an optimum production amount for each item, line, and period three-dimensional element in the product according to the planned order. Then, the production planning apparatus 1 instructs the manufacturing process 206 on the optimum production amount for each three-dimensional element. The production planning device 1 assigns an optimum production amount for each item, line, and period of the part from the planned order in consideration of the priority for preferential production of parts, line capability, and other manufacturing conditions. The item identifies a product part. A line is a facility for manufacturing parts. The period is a time span obtained by subdividing the production planning period by input unit.

  FIG. 3 is an explanatory diagram illustrating an example of a component configuration of a product. The finished product is a mobile phone, for example. A finished product 300 shown in FIG. 3 includes a printed board 301 inside the movable part 300A on which the display is mounted, and a printed board 302 inside the fixed part 300B on which the key is mounted. For example, the printed circuit board 301 of the movable part 300A is for mounting electronic components only on the surface side. It is assumed that four pieces of printed circuit boards 301 are obtained with one board sheet. Moreover, the printed circuit board 302 of the fixing | fixed part 300B mounts an electronic component on the both surfaces (front surface and back surface) side of a board | substrate, for example. It is assumed that four printed circuit boards 302 are obtained with one substrate sheet.

  FIG. 4 is an explanatory diagram showing an example of the flow of the manufacturing process. In the manufacturing process shown in FIG. 4, for example, N SMT (Surface Mount Technology) lines 401, M total assembly lines 402, M test lines 403, and M packing lines 404 are used. Each SMT line 401 is a line for producing a component by mounting a predetermined electronic component on the printed circuit board using the printed circuit board and the electronic component conveyed from the component warehouse 405. Then, the parts produced on the SMT line 401 are transported into the intermediate warehouse 406.

  Each total assembly line 402 is a line for assembling parts conveyed from the intermediate warehouse 406 and producing a finished product of the assembled parts. Each test line 403 is a line for executing an appropriateness test of a finished product. Each packing line 404 is a line for packing a finished product. Then, the finished product packed in the packing line 404 is transported into the shipping warehouse 407 waiting to be shipped.

  In the production planning apparatus 1 according to the present embodiment, a production plan for producing parts on the SMT line 401, for example, among the manufacturing steps shown in FIG.

  The production planning device 1 uses the factory operation day calendar, the line capacity for each SMT line 401, the mounting time for each part on the line, the setup time for the line, the number of individual parts, etc. as master data in the HDD 15. Is stored. The line capacity is the maximum operation time that can be operated in a unit period, for example, one day, regardless of the parts to be manufactured. The setup time is an operation time required for switching a new component or switching the back surface of the substrate. The setup time differs depending on the parts.

  The mounting time is the time required for assembling the parts. The mounting time varies depending on the parts. FIG. 6 is an explanatory diagram showing an example of data in the mounting time table. The mounting time table 30 shown in FIG. 6 manages a mounting time 33 that is an assembly time (in seconds) per printed circuit board for each line 32 for each item 31. For example, when assembling “item 1” using “line 1”, the CPU 17 can recognize that “35.5 seconds” are required per printed circuit board. Further, the CPU 17 can recognize that “line 6” cannot be used for “item 1” because the mounting time 33 of “item 1” using “line 6” is “999999”, for example.

  Moreover, the number of pieces is the number of pieces of the printed circuit board obtained from one board sheet. FIG. 7 is an explanatory diagram showing an example of data in the piece number table. The piece number table 40 shown in FIG. 7 manages the number of printed boards obtained from one board sheet for each item 41, that is, the number of pieces 42. The CPU 17 can refer to the piece number table 40 and recognize, for example, that the number of pieces 42 per sheet of “item 1” is “4”.

  FIG. 5 is an explanatory diagram showing an example of a plan order in the plan database. The planned order shown in FIG. 5 is an input plan in which the planned production amount for each model, command, piece, and delivery date generated by the manufacturing management system 204 is planned. The planned order 20 includes a part model 21, a command 22 and the number of pieces 23, a delivery date 24 for manufacturing the part, and a planned production amount 25 for each delivery date 24. The CPU 17 identifies the item of the part in the master data with the model 21, the command 22, and the number of pieces 23. The planned order is for managing the delivery date 24 and the planned production volume 25 in association with each model 21, command 22, and number of pieces 23. It is assumed that the planned order is stored in a plan database (not shown) in the production management system 201 connected to the external network, for example.

  In addition, the production planning apparatus 1 executes the mathematical model of the integer programming method of the production planning program, so that the objective function can be calculated based on the optimum production amount allocated to each three-dimensional element of the item, line, and period from the planned order. An optimal solution is obtained. In the mathematical model, for example, the number of demands, the line capacity time, the mounting time of each item for each line, the number of pieces, and the like are input values. In addition, the mathematical model uses, for example, item production quantities by line and period as output values. The mathematical model includes an objective function and constraint conditions. The execution of the mathematical model is to calculate an output value that maximizes or minimizes the objective function under a constraint condition.

  The CPU 17 of the production planning apparatus 1 defines three-dimensional elements of items, lines, and periods. Further, the CPU 17 generates a mathematical model for integer programming including common constraints, constraints unique to the manufacturing process, and the like. Common constraint conditions include an inventory constraint condition, a delivery date constraint condition, a capacity constraint condition, a demand constraint condition, an input constraint condition, and the like.

The demand constraint condition is a constraint condition that guarantees that the total production amount of a certain item for the entire period is equal to or greater than the total production requirement amount (demand amount). For example, the demand constraint condition is expressed by Σ production number _{item, line, period} ≧ Σ demand number (production required amount) _{item, period} .

The capacity constraint condition is a constraint condition that guarantees that the production load of the line assigned to a certain item (the total production time in each period) is equal to or less than the line capacity (maximum operation time). For example, the capacity constraint condition is expressed as Σ production time _{item, line, period} ≦ equipment capacity _{line, period} .

The inventory constraint condition is a constraint condition that guarantees that the inventory will be carried forward and forward before and after the period. For example, Σ current inventory _{item, line, term} = Σ carry forward inventory _{item, line, term} + Σ production _{item, line, term} -demand number (shipment number) _{item, term} .

The delivery date constraint condition is a constraint condition that guarantees that a delay in delivery date due to out-of-stock does not occur because the current inventory quantity is positive. For example, the delivery date constraint condition is expressed as Σcurrent inventory quantity _{item, line, period} ≧ 0.

The input restriction condition is a restriction condition for guaranteeing that only one type of item is input within one period on one line. For example, the input constraint condition is expressed as Σ input line _{item, line, period} ≦ 1.

  FIG. 8 is an explanatory diagram illustrating an example of a mathematical model setting screen. The mathematical model setting screen 50 shown in FIG. 8 includes an objective function selection unit 51, a unique constraint selection unit 52, a start date selection unit 53, a period selection unit 54, a time width selection unit 55, and an order number selection. A unit 56, a line number selection unit 57, a setting unit 58, and a cancellation unit 59. The objective function selection unit 51, the unique constraint condition selection unit 52, the start date selection unit 53, the period selection unit 54, the time width selection unit 55, the order number selection unit 56, the line number selection unit 57, the setting unit 58, and the cancellation unit. 59 is for performing a selection operation using, for example, the keyboard 11 or a mouse.

  The objective function selection unit 51 selects an objective function prepared in advance. The objective function includes, for example, minimizing setup time (minimizing the number of setup changes), maximizing line operating time (line operating rate), and minimizing the number of in-process inventory.

  The unique constraint condition selection unit 52 selects a unique constraint condition prepared in advance. The inherent constraint conditions are conditions unique to the manufacturing process such as an item or a line, for example, grouping of lines, item priority, multistage process, and the like. Note that the unique constraint conditions can be changed as appropriate.

  The start date selection unit 53 selects the date on which the production of the item is started. The period selection unit 54 selects a period from the production start date. The period includes, for example, one month, two weeks, and one week. The time width selection unit 55 selects the time width of the period. The time width includes, for example, 4 hours, 8 hours, 16 hours and 24 hours.

  The order number selection unit 56 selects the number of orders. The line number selection unit 57 selects the number of lines. The setting unit 58 determines the selection content on the setting screen 50. The cancel unit 59 cancels the selection content on the setting screen 50.

  Next, operation | movement of the production planning apparatus 1 of a present Example is demonstrated. FIG. 9 is a flowchart showing an example of the processing operation of the CPU 17 related to the production plan process. In FIG. 9, the CPU 17 generates initial data based on the planned order and master data (step S11). The CPU 17 acquires a plan order from a plan database (not shown) through an external network, and acquires master data stored in the HDD 15. The CPU 17 acquires the delivery date 24 and the planned production amount 25 for each model 21, command 22, and number of pieces 23 from the planned order. Further, the CPU 17 acquires line capability, mounting time, number of pieces and the like from the master data.

  The CPU 17 sets the selection content on the setting screen 50 (step S12). The user selects an objective function, a specific constraint condition, a start date, a period, a time width, the number of orders, and the number of lines in accordance with the setting operation on the mathematical model setting screen 50 shown in FIG. is there.

  In accordance with the setting operation on the setting screen 50, the CPU 17 converts the model 21, command 22, and number of pieces 23 in the planned order of the initial data into items in the master data (step S13). Further, when the priority of the item is selected as the unique constraint condition, the CPU 17 determines whether or not the three-dimensional element of the priority item is data that can be optimized (step S14).

  When the three-dimensional element of the priority item is data that can be optimized (Yes at Step S14), the CPU 17 generates a data file that is an input of the mathematical model based on the planned order and master data of the priority item (Step S14). S15). Note that the CPU 17 extracts a planned order for a high priority item having a higher priority among unassigned priority items. For example, as shown in FIG. 11, the CPU 17 plans for each delivery date 24 of “item 2”, “item 3”, “item 5”, and “item 6” among “item 1” to “item 6”. A planned order including the production amount 25 is extracted.

  The CPU 17 executes a model file generation process shown in FIG. 10 in order to generate a model file of a mathematical model based on common constraint conditions (step S16). The CPU 17 executes an integer programming mathematical model based on the data file and the model file (step S17). In addition, the execution of the mathematical model is to calculate the item production quantity for each line period that minimizes the setup time under a given constraint if the objective function is the minimization of the setup time. . In addition, if the objective function is to maximize the line operation time, the mathematical model is to calculate the item production quantity for each line period that maximizes the line operation time under the given constraints. . In addition, if the objective function is the minimization of the number of in-process inventory, the mathematical model is to calculate the item production quantity for each line period that minimizes the number of in-process inventory under the given constraints. .

  The CPU 17 optimizes the production amount allocated for each three-dimensional element of the priority item based on the mathematical model (step S18). Note that the CPU 17 stores the production amount of the optimized priority item for each three-dimensional element in the line-item production amount table in the HDD 15 (see FIGS. 12 and 16). Further, the CPU 17 stores the production time corresponding to the production amount of each optimized priority item for each three-dimensional element in the production time table for each line and item in the HDD 15 (see FIGS. 13 and 17).

  After optimizing the production amount allocated for each three-dimensional element of the priority item, the CPU 17 determines whether there is a three-dimensional element of the unassigned priority item (step S19). When there is a three-dimensional element of an unassigned priority item (Yes in step S19), the CPU 17 sets the production amount for each assigned three-dimensional element as the assigned production amount, and assigns the previous assigned table (see FIG. 14) (step S20).

  Then, the CPU 17 shifts to a processing operation after step S14 based on the assigned production amount of the previously assigned table. That is, when assigning the optimum production amount for each three-dimensional element of the low-priority item among the unassigned priority items, the CPU 17 has already assigned the previously assigned table in addition to the planned order of the low-priority item. Reset common constraints based on production volume. As a result, the model file of the mathematical model is regenerated.

  If the priority item data is not data that can be optimized (No at Step S14), the CPU 17 proceeds to Step S19 to determine whether or not there is an unassigned priority item.

  If there is no unassigned priority item (No in step S19), the CPU 17 stores the production amount assigned for each three-dimensional element optimized in step S18 in the production amount table (not shown) in the HDD 15 as an optimization result. (Step S21). The CPU 17 stores the production time obtained by time-converting the production amount assigned to each three-dimensional element as an optimum result in a production time table (not shown) in the HDD 15. Further, the CPU 17 calculates an optimal solution of the objective function selected on the setting screen 50 based on the table contents of the production volume table or the production time table (step S22). The production volume table stores a line-item production volume table for all priority items. The production time table stores a line-item production time table for all priority items.

  The CPU 17 outputs an optimal solution corresponding to the objective function (step S23) and ends the processing operation shown in FIG. Note that the CPU 17 displays on the display 12 a Gantt chart (see FIG. 18) indicating the production time of items for each line and period as an optimal solution.

  In the production planning process shown in FIG. 9, an integer programming mathematical model including common constraint conditions and unique constraint conditions is generated, and the mathematical model is executed. Preferentially allocate production. Then, the production planning process preferentially assigns the optimum production quantity for each three-dimensional element of the next priority item, reflecting the assigned previous production quantity in the mathematical model. As a result, since an optimal production amount is assigned to each three-dimensional element according to the priority item, the input line, the production amount, and the input period can be solved simultaneously as a production plan. Furthermore, it is possible to give flexibility to the operation range of the production plan by considering not only common constraint conditions but also inherent constraint conditions that can be appropriately selected.

  FIG. 10 is a flowchart illustrating an example of the processing operation of the CPU 17 related to the model file generation process. In FIG. 10, the CPU 17 sets a variable that is an input value in the mathematical model (step S31). The CPU 17 extracts items (model, command and number of pieces), delivery date, and planned production amount according to the target priority from the planned order. CPU17 extracts the master data regarding the extracted item. The master data includes the factory operating day calendar corresponding to the planned period, line capability, installation time, setup time, number of multi-chamfer pieces, and presence / absence of front / back pair.

The CPU 17 defines a set of items, lines, and periods. For example, item ∈ (item ₁ , item ₂ ... Item _N ), line ∈ (line ₁ , line ₂ ... Line _L ) and period ∈ (period ₁ , period ₂ ... Period _T ). That is, the CPU 17 defines three-dimensional elements of items, lines, and periods. The CPU 17 defines the number of demands, the number of inventory, the number of production, the presence of production (0-1 integer variable), the absolute value conversion variable (0-1 integer variable), the mounting time, the number of pieces, and the number of assigned as variables To do. Furthermore, the CPU 17 defines, as constants, an upper limit value of continuous variables, a line capability, and a second → time conversion coefficient in integer programming.

  The CPU 17 assigns the optimum production amount for each high-priority item, line, and period three-dimensional element, and then assigns the production amount assigned for each high-priority item three-dimensional element to the main memory 16 in the previous allocation. Update and register in the table. The CPU 17 reflects the assigned production amount in the previously assigned table in the capacity constraint condition when assigning the optimum production amount for each three-dimensional element of the next priority item. That is, the CPU 17 adjusts the allocation of the current production amount so that the production time corresponding to the previous production amount + the production time corresponding to the current production amount does not exceed the maximum operating time of the line.

The CPU 17 sets inventory constraint conditions within the common constraint conditions (step S32). The inventory constraints are Σ current inventory _{item, line, period} = Σ carry forward inventory _{item, line, period} + Σ production _{item, line, period} -demand (shipment) _{item, period} constraint is there. The CPU 17 sets a delivery date constraint within the common constraints (step S33). Note that the delivery date constraint condition is a constraint condition of Σ current inventory quantity _{item, line, period} ≧ 0.

The CPU 17 sets capability constraint conditions within the common constraint conditions (step S34). Note that the capacity constraint conditions are Σ production time _{item, line, period} ≦ line capacity _{line, period} constraint condition. CPU17 sets the demand constraint condition in a common constraint condition (step S35). The demand constraint conditions are the Σ production number _{item, line, period} ≧ Σ demand number _{item, period} constraint condition.

Further, the CPU 17 sets the input restriction condition within the common restriction condition (step S36). In addition, the input constraint condition is a constraint condition of Σ number of production _{items, line, period} ≦ 1. The number of items existing in one line and in one term shall be one item or less.

  Furthermore, the CPU 17 sets a unique constraint condition (step S37). The unique constraint condition is a unique constraint condition selected by the unique constraint condition selection unit 52 on the setting screen 50.

Further, the CPU 17 sets a 0-1 integer variable in accordance with the presence or absence of production among the defined three-dimensional elements (step S38). The CPU 17 sets “1” when there is production and “0” when there is no production for each item, line, and period three-dimensional element. CPU17, X _{item, line, period} (X = 0 (no production) or X = 1 (production)), that is, production based on “0” or “1” for X, production in the relevant line and period Determine the presence or absence. Then, Y = A * X is formulated as a mixed integer programming method for determining the Y _{item, line, and period} production.

  The CPU 17 sets absolute value conversion (step S39). The absolute value conversion is for converting the absolute value of the solution obtained by the mathematical model. Further, the CPU 17 sets the selected objective function (step S40). The objective function is for setting the objective function selected on the setting screen 50. Then, the CPU 17 generates a model file of a mathematical model including the setting contents of steps S31 to S40 (step S41), and ends the processing operation shown in FIG.

  In the model file generation process shown in FIG. 10, an arrangement plan including common constraint conditions such as inventory constraint conditions, delivery date constraint conditions, demand constraint conditions, and capacity constraint conditions, and unique constraint conditions such as item priority, for example. Generate a mathematical model of the modulo. It is possible to generate a mathematical model of a flexible production plan by adjusting the application range of the mathematical model by appropriately selecting unique constraint conditions.

  In the model file generation process, “0” or “1” indicating whether or not production is set is set for each three-dimensional element. Therefore, among the three-dimensional elements of items, lines, and periods, combinations that do not execute the production of the item can be easily performed. Can be identified.

  Next, for example, the operation of the production planning apparatus 1 that makes a production plan of “item 1” to “item 6” from the planned order will be described. The CPU 17 of the production planning device 1 extracts the planned production volume from the first order to the sixth day of the items 2, 3, 5 and 6 of the high priority group from the planned order shown in FIG. FIG. 11 is an explanatory diagram showing an example of a high priority plan order. The CPU 17 stores the extracted high-priority group item 61 and the planned production volume 63 from the first day to the sixth day of the schedule 62 in the main memory 16 as the high-priority planned order 60A shown in FIG. To do. The CPU 17 can recognize that, for example, the number of “item 2” input on the first day is 5100 with reference to the high priority plan order 60A of FIG.

  The CPU 17 generates a data file based on the high priority plan order and the master data, and assigns an optimum production amount for each three-dimensional element of the high priority item. The CPU 17 assigns the optimum production amount for each three-dimensional element of the high priority item, and then assigns the production amount assigned for each three-dimensional element to the line-item production amount table 70A shown in FIG. Store. FIG. 12 is an explanatory diagram showing an example of the line-item-specific production amount table 70A. The line-item production amount table 70A shown in FIG. 12 manages the production amount 74 obtained by the optimization calculation result for each of the three-dimensional elements of the item 71, the line 72, and the period 73. The CPU 17 can recognize the production amount of “item 1”, “line 4”, and “period 1” as 2839 units with reference to the line-item production amount table 70A of FIG.

  Further, the CPU 17 calculates a time-converted production time based on the optimum production amount assigned to each three-dimensional element of the high priority item, and then calculates the production time for each three-dimensional element of the high priority item in the HDD 15. Are stored in the line-item production time table 80A shown in FIG. FIG. 13 is an explanatory diagram showing an example of the production time table 80A for each line and item. The line-item production time table 80A shown in FIG. 13 manages the assigned production time 84 that is the calculation result of optimization for each of the three-dimensional elements of the item 81, the line 82, and the period 83. The CPU 17 can recognize, for example, the allocated production time of “item 1”, “line 4”, and “period 1” as 7.0 hours with reference to the line-item production time table 80A of FIG.

  Further, the CPU 17 determines whether or not there is an unassigned priority item after storing the optimum production amount assigned for each three-dimensional element of the high priority item in the line-item production amount table 70A. . When there is an unassigned priority item, for example, a low priority item, the CPU 17 updates the assigned production amount for each three-dimensional element in the previously assigned table 90 </ b> A shown in FIG. 14 in the main memory 16. FIG. 14 is an explanatory diagram showing an example of the previously assigned table 90A. The previously assigned table 90A shown in FIG. 14 manages the assigned production amount 94 assigned when the optimization of the high priority item is completed for each three-dimensional element of the item 91, the line 92, and the period 93. . In the period shown in FIG. 14, since the time width selected by the time width selection unit 55 on the setting screen 50 is 8H, 24 hours a day is divided into three periods. The CPU 17 can recognize the allocated amount 94 of “period 4” of “line 2” of “item 2” as 2563 units with reference to the previously allocated table 90A of FIG.

  The CPU 17 extracts the planned production volume from the first day to the sixth day of “item 1” and “item 4” of the low priority items from the planned order. FIG. 15 is an explanatory diagram showing an example of a low priority plan order. The CPU 17 stores the extracted low-priority item 61 and the planned production volume 63 from the first day to the sixth day of the schedule 62 in the main memory 16 as the low-priority planned order 60B shown in FIG. To do. Note that the CPU 17 can recognize that the number of items input on the first day of “item 1” is 8400, for example, with reference to the low priority plan order 60B of FIG.

  The CPU 17 generates a data file based on the low priority plan order and the master data, and assigns an optimum production amount for each three-dimensional element of the low priority item. The CPU 17 assigns an optimum production amount for each three-dimensional element of the low priority item, and then stores the production amount for each three-dimensional element in the production amount table 70B for each line-item shown in FIG. FIG. 16 is an explanatory diagram illustrating an example of the production amount table 70B for each line and item. The line-item production amount table 70B shown in FIG. 16 manages the production amount 74 obtained as a result of optimization calculation for each of the three-dimensional elements of the item 71B, the line 72B, and the period 73. For example, the CPU 17 can recognize that the production amount of “item 3”, “line 1”, and “period 1” is 2563 units.

  Further, the CPU 17 calculates the time-converted production time based on the optimum production amount assigned to each three-dimensional element of the low priority item, and the assigned production time for each three-dimensional element is shown in FIG. Stored in the line-item production time table 80B. FIG. 17 is an explanatory diagram showing an example of the production time table 80B for each line and item. The line-item production time table 80B shown in FIG. 17 manages the production time 84 that is the calculation result of optimization for each of the three-dimensional elements of the item 81B, the line 82B, and the period 83. The CPU 17 can recognize the production time of “item 3”, “line 1”, and “period 1” as 7.0 hours with reference to the line-item production time table 80B of FIG.

  Further, the CPU 17 determines whether or not there is an unassigned priority item after storing the optimum production amount of the three-dimensional element of the low priority item in the line-item production amount table 70A. When there is an unassigned priority item, the CPU 17 updates the production amount for each three-dimensional element in the previously assigned table 90 </ b> A in the main memory 16. As a result, in the next allocation, optimization is performed using the production amount allocated this time and the order of the current priority item. By executing this for all items, the final allocation result is obtained.

  Further, when there is no unassigned priority item, the CPU 17 calculates an optimum solution corresponding to the objective function based on the contents of the production volume table or the production time table, and based on the optimum solution, for example, the Gantt shown in FIG. The chart is displayed on the screen of the display 12. As a result, the user can recognize the input period and production time of the item for each line by looking at the Gantt chart. Furthermore, the CPU 17 instructs the manufacturing process on the optimum production time assigned to each three-dimensional element.

  That is, the CPU 17 assigns an optimal production amount for each three-dimensional element of the priority item, determines whether there is a three-dimensional element of the unassigned priority item, and if there is an unassigned three-dimensional element, Incorporate the assigned 3D element production into the constraint. Then, the optimum production amount allocation is repeatedly executed according to the priority.

  In this example, “0” or “1” is used to define production for each item, line, and period, and a mathematical model incorporating common constraints and selectable unique constraints is executed. By doing so, an optimal production amount is assigned to each three-dimensional element. As a result, the optimal input line, the optimal input period, and the optimal production amount of items can be solved simultaneously. Furthermore, it is possible to give flexibility to the operation range of the production plan by determining the framework of the production plan with common constraints, enabling the selection of site-specific constraints, and adjusting the scope of application of the mathematical model. .

  In this embodiment, priority is assigned to each item, and the optimum production amount is sequentially assigned from the item with high priority, so that the optimum production amount can be reliably assigned from the item with high priority. As a result, it is possible to preferentially assign the production amount of the item with high priority, and embed the production amount of the item with low priority in the empty area, thereby enabling assignment close to human judgment.

  In the present embodiment, the stock constraint condition, the delivery date constraint condition, the demand constraint condition, and the capacity constraint condition are set as a common constraint condition, so that the production planning framework can be determined.

  In this embodiment, the presence / absence of production indicated by “0” or “1” is defined for each three-dimensional element, so that the presence / absence of production for each three-dimensional element can be easily identified.

  In the above-described embodiment, the stock constraint condition, the delivery time constraint condition, the demand constraint condition, the capacity constraint condition, and the input constraint condition are included as common constraint conditions, but the input constraint condition may not be included.

  In addition, although two types of priority items, high priority and low priority, are used, the priority is not limited to two types, and may be three or more types.

Further, the constraint condition of the high priority item may be set strictly, and the constraint condition may be set loosely for the low priority item. In this case, the optimization can be easily converged by adjusting the constraint condition according to the priority. For example, in the case of low-priority items, the delivery date constraint condition may be loosened and changed to Σ current inventory quantity _{item, line, period} ≧ −300, and the like. In the case of low priority items, the input restriction condition may be relaxed and changed to Σ input line _{item, line, period} ≦ 2, etc.

  In addition, each component of each part illustrated does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each part is not limited to the one shown in the figure, and all or a part thereof may be functionally or physically distributed / integrated in arbitrary units according to various loads and usage conditions. Can be configured.

  Furthermore, various processing functions performed in each device are performed on a CPU (Central Processing Unit) (or a microcomputer such as an MPU (Micro Processing Unit), MCU (Micro Controller Unit), etc.) in whole or in part. You may make it perform. Various processing functions may be executed entirely or arbitrarily on a program that is analyzed and executed by a CPU (or a microcomputer such as an MPU or MCU) or hardware based on wired logic. Needless to say.

  As described above, the following supplementary notes are further disclosed regarding the embodiment including the present example.

(Appendix 1) Obtaining plan data including an item for identifying a part of a product and a period for manufacturing the part, and master data including a production capacity of a line for manufacturing the part,
Define a three-dimensional element having the item, the line and the period related to the part as elements,
A specific constraint condition for the part or the line is selected according to a predetermined operation, and the item is included for each of the three-dimensional elements including the selected unique constraint condition and a predetermined common constraint condition. Generate an integer programming mathematical model that allocates the optimal output of
By executing the mathematical model, each of the three-dimensional elements satisfying the specific constraint condition and the common constraint condition is caused to cause a computer to execute each process of assigning an optimal production amount of the item. Production planning program.

(Appendix 2) For each item, a priority for preferentially assigning the optimum production amount is given,
The production planning program according to appendix 1, wherein the computer is caused to execute each process of sequentially assigning the optimum production amount for each of the three-dimensional elements from the item having a high priority.

(Supplementary note 3) After assigning the optimum production amount from the three-dimensional element of the item with high priority, when assigning the optimum production amount of the item to the three-dimensional element of the next item, the assigned The production planning program according to appendix 2, wherein the computer is caused to execute a process of generating the mathematical model reflecting a production amount in the constraint condition.

(Appendix 4) The common constraint condition is
An inventory constraint condition for calculating a production amount of the part, which indicates a transition of the inventory quantity of the part for the current period and the inventory quantity of the previous period immediately before the period,
A delivery date constraint where the inventory quantity of the part for the period is positive;
Demand constraint condition that the production number of the parts over the whole period is more than the demand number,
The production planning program according to any one of appendices 1 to 3, further comprising: a capacity restriction condition in which a production load of the line for manufacturing the part is equal to or less than a production capacity of the line.

(Supplementary Note 5) The inventory constraint condition is
Appendix 4 characterized in that it is an equation for calculating the optimum production amount to be allocated to the part based on the inventory quantity of the part for the current period, the inventory quantity of the part for the previous period, and the demand quantity of the part for the current period. Production planning program described in

(Additional remark 6) When defining the said three-dimensional element, it makes the said computer perform the process which attaches the presence or absence of the production of the said part for every said three-dimensional element. The production planning program described.

(Supplementary note 7) A production planning apparatus having a processor,
The processor is
Obtaining plan data including an item for identifying a part of the product and a period for manufacturing the part, and master data including a production capacity of a line for manufacturing the part;
Define a three-dimensional element having the item, the line and the period related to the part as elements,
A specific constraint condition for the part or the line is selected according to a predetermined operation, and the item is included for each of the three-dimensional elements including the selected unique constraint condition and a predetermined common constraint condition. Generate an integer programming mathematical model that allocates the optimal output of
A production characterized in that, by executing the mathematical model, each process for allocating an optimal production amount of the item is performed for each of the three-dimensional elements satisfying the specific constraint condition and the common constraint condition. Planning equipment.

1 Production planning device 15 HDD
16 Main memory 17 CPU

Claims (6)

Obtaining plan data including an item for identifying a part of the product and a period for manufacturing the part, and master data including a production capacity of a line for manufacturing the part;
Define a three-dimensional element having the item, the line and the period related to the part as elements,
A specific constraint condition for the part or the line is selected according to a predetermined operation, and the item is included for each of the three-dimensional elements including the selected unique constraint condition and a predetermined common constraint condition. Generate an integer programming mathematical model that allocates the optimal output of
By executing the mathematical model, each of the three-dimensional elements satisfying the specific constraint condition and the common constraint condition is caused to cause a computer to execute each process of assigning an optimal production amount of the item. Production planning program. For each item, a priority for preferentially assigning the optimum production amount is given,
The production planning program according to claim 1, wherein the computer is caused to execute each process of sequentially assigning the optimum production amount for each of the three-dimensional elements from the item having a high priority. After allocating the optimum production amount from the three-dimensional element of the item with high priority, when assigning the optimum production amount of the item to the three-dimensional element of the next item, the assigned production amount is The production planning program according to claim 2, wherein the computer is caused to execute a process of generating the mathematical model reflected in the constraint condition. The common constraints are:
An inventory constraint condition for calculating a production amount of the part, which indicates a transition of the inventory quantity of the part for the current period and the inventory quantity of the previous period immediately before the period,
A delivery date constraint where the inventory quantity of the part for the period is positive;
Demand constraint condition that the production number of the parts over the whole period is more than the demand number,
The production planning program according to any one of claims 1 to 3, further comprising: a capacity restriction condition in which a production load of the line for manufacturing the part is equal to or less than a production capacity of the line. The production according to any one of claims 1 to 4, wherein when the three-dimensional element is defined, the computer is caused to execute a process for adding the presence or absence of production of the part for each three-dimensional element. Planning program. A production planning device having a processor,
The processor is
Obtaining plan data including an item for identifying a part of the product and a period for manufacturing the part, and master data including a production capacity of a line for manufacturing the part;
Define a three-dimensional element having the item, the line and the period related to the part as elements,
A specific constraint condition for the part or the line is selected according to a predetermined operation, and the item is included for each of the three-dimensional elements including the selected unique constraint condition and a predetermined common constraint condition. Generate an integer programming mathematical model that allocates the optimal output of
A production characterized in that, by executing the mathematical model, each process for allocating an optimal production amount of the item is performed for each of the three-dimensional elements satisfying the specific constraint condition and the common constraint condition. Planning equipment.

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