JP6389817B2 - Production plan optimization system and production plan optimization method - Google Patents

Description

  The present invention relates to a production plan optimization system and a production plan optimization method.

  It is known to create a production plan in order to systematically execute production of an order-made product.

  As a related technique, Patent Document 1 describes a build-to-order production plan creation method. In the method described in Patent Document 1, a production plan is created by executing a production leveling process for each element in the order of N shift, N + 1 shift, N + 2 shift, and N + 3 shift. In the created production plan, the amount of production in each confirmed shift is compared with the upper limit production amount, and if the difference between the production amount and the upper limit production amount is greater than or equal to the predetermined amount, the upper limit production amount Correct.

Japanese Patent Laid-Open No. 2003-280718

  In the method described in Patent Document 1, the production level for each element (part) of the ordered product is leveled. However, the relationship between the production plan for parts and the assembly plan for ordered products is not shown. Accordingly, an object of the present invention is to provide a production plan optimization system and a production plan optimization method for optimizing both an assembly plan for an ordered product and a procurement plan for parts required for the assembly of the ordered product. It is in.

  These objects and other objects and benefits of the present invention can be easily confirmed by the following description and the accompanying drawings.

  Hereinafter, means for solving the problem will be described using the numbers and symbols used in the embodiments for carrying out the invention. These numbers and symbols are added with parentheses for reference in order to show an example of the correspondence between the description of the claims and the mode for carrying out the invention. Accordingly, the claims should not be construed as limiting due to the bracketed description.

  In some embodiments, a production plan optimization system outputs an assembly plan for an ordered product and a computing device for creating a parts procurement plan for parts of the ordered product, and the generated assembly plan and the parts procurement plan. Output device (60). The computing device (20) is based on first data indicating the type of the ordered product, second data indicating the delivery date of the ordered product, and a plurality of assembly conditions obtained by changing assembly parameters. A plurality of assembly plan candidates and an assembly plan candidate evaluation value for each of the plurality of assembly plan candidates are calculated. The arithmetic unit (20) determines an assembly plan candidate having the best assembly plan candidate evaluation value among the plurality of assembly plan candidates as the assembly plan. The computing device (20), based on the determined assembly plan, BOM data (130), and a plurality of parts procurement conditions obtained by changing procurement parameters, a plurality of parts procurement plan candidates, and A part procurement plan candidate evaluation value of each of the plurality of part procurement plan candidates is calculated. The arithmetic unit determines a component procurement plan candidate having the best component procurement plan candidate evaluation value as the component procurement plan among the plurality of component procurement plan candidates. By using the production plan optimization system, it is possible to optimize both an assembly plan for an ordered product and a procurement plan for parts necessary for the assembly of the ordered product.

  In the production plan optimization system, the assembly plan candidate evaluation value may include a total of assembly penalty values. The parts procurement plan candidate evaluation value may include a total of procurement penalty values. By using the production plan optimization system, an assembly plan is determined based on the total assembly penalty value, and a parts procurement plan is determined based on the total procurement penalty value. As a result, it is possible to optimize both the assembly plan for ordered products and the procurement plan for parts required for assembling ordered products, taking into account multiple assembly conditions or multiple parts procurement conditions. Become.

  In the production plan optimization system, each of the plurality of assembly plan candidates may include an assembly process. The sum of the assembly penalty values may include a penalty value that is added when the computing device (20) selects an assembly condition different from the standard assembly condition for the assembly process. By using the production plan optimization system, priority is given to the selection of standard assembly conditions. As a result, it is possible to suppress unnecessary selection of assembly conditions different from the standard assembly conditions. The standard assembly condition is preferably an assembly condition that does not cause an excessive load on resources.

  In the production plan optimization system, each of the plurality of assembly plan candidates may include an assembly process. In the total of the assembly penalty values, the arithmetic device (20) adds an assembly mode penalty value added when the assembly process selects an assembly mode different from the standard assembly mode, or the arithmetic device (20 ) May include an assembly lead time reduction penalty value that is added when a lead time shorter than the standard lead time is selected for the assembly process. By using the production plan optimization system, priority is given to the selection of the standard assembly mode or the standard lead time. As a result, unnecessary selection of an assembly mode or lead time different from the standard assembly mode or standard lead time is suppressed. For example, by suppressing the selection of a lead time different from the standard lead time, an assembly plan with a short lead time can be easily created. As a result, load leveling is realized, worker overtime is reduced, or the operating rate of the assembly equipment is leveled.

  In the production plan optimization system, the computing device (20) may calculate assembly resources used in the assembly process. The assembly plan may be determined under a constraint that a load of the assembly resource does not exceed an absolute load upper limit value. By using the production plan optimization system, occurrence of a situation where the load of the assembly resource exceeds the absolute load upper limit value is avoided.

  In the production plan optimization system, the sum of the assembly penalty values may include a reference upper limit excess penalty value that is added when the load of the assembly resource exceeds the reference load upper limit value. By using the production plan optimization system, occurrence of a situation where the load of the assembly resource exceeds the reference load upper limit value is suppressed. As a result, load leveling is realized, worker overtime is reduced, or the operating rate of the assembly equipment is leveled.

  In the production plan optimizing system, the assembly plan may be determined under a constraint condition that a load fluctuation of the assembly resource does not exceed a fluctuation upper limit and does not fall below a fluctuation lower limit. By using the production plan optimization system, an assembly plan with a small load variation of assembly resources (a variation in load acting on an operator or an assembly facility) can be created.

  In the production plan optimization system, each of the plurality of parts procurement plan candidates may include a parts procurement process. The total of the procurement penalty values may include a penalty value that is added when the computing device (20) selects a component procurement condition different from the standard procurement condition for the component procurement process. By using the production plan optimization system, priority is given to the selection of standard procurement conditions. As a result, unnecessarily selecting parts procurement conditions different from the standard procurement conditions are suppressed. Note that the standard procurement conditions are preferably parts procurement conditions that do not cause an excessive load on resources.

  In the production plan optimization system, each of the plurality of parts procurement plan candidates may include a parts procurement process. In the total of the procurement penalty values, the computing device (20) adds a procurement mode penalty value added when the procurement mode different from the standard procurement mode is selected for the component procurement process, or the computing device ( 20) may include a procurement lead time reduction penalty value added when a lead time shorter than the standard lead time is selected for the component procurement process. By using the production plan optimization system, priority is given to the selection of the standard procurement mode or the standard lead time. As a result, the procurement mode or lead time different from the standard procurement mode or standard lead time is unnecessarily suppressed. For example, by suppressing the selection of a lead time different from the standard lead time, it becomes easier to create a parts procurement plan with a short lead time. As a result, load leveling is realized, worker overtime is reduced, or equipment operation rate is leveled.

  In the production plan optimizing system, the total of the procurement penalty values may include an inventory penalty value indicating a magnitude of deviation of the number of parts in stock with respect to the number of safety stocks. By using the production plan optimization system, it is easy to create a parts procurement plan with a small deviation in the number of parts stock from the number of safety stocks. As a result, the risk of excess inventory is reduced. In addition, it is easy to create a parts procurement plan with a low risk of missing parts.

  In the production plan optimization system, the total of the inventory penalty values may include a total of the safety stock excess penalty value and a total of the safety stock interruption penalty value. The arithmetic unit (20) calculates the total of the safety stock excess penalty value based on the excess number of the parts stock quantity with respect to the safety stock quantity for the parts, and also calculates the parts stock quantity with respect to the safety stock quantity. A total of the safety stock interruption penalty values may be calculated based on the shortage number. By using the production plan optimization system, it is easy to create a parts procurement plan with a small deviation in the number of parts stock from the number of safety stocks. As a result, the risk of excess inventory is reduced. In addition, it is easy to create a parts procurement plan with a low risk of missing parts.

  In the production plan optimization system, the procurement parameter may include the number of parts procured or the delivery date of the parts. Changing the procurement parameters may include changing the number of parts procured or the delivery date of the parts. In the production plan optimization system, the number of parts procured or the delivery date of parts can be changed. In the production plan optimization system, a parts procurement plan can be created more flexibly, and the load acting on resources can be further leveled.

  The production plan optimizing method in some embodiments includes an assembly plan creation step in which the arithmetic unit (20) creates an assembly plan, and a part procurement plan creation step in which the arithmetic unit (20) creates a part procurement plan. And the output device (60) includes a plan output step for outputting the created assembly plan and the parts procurement plan. The assembly plan creation step includes a plurality of items based on first data indicating the type of the ordered product, second data indicating the delivery date of the ordered product, and a plurality of assembly conditions obtained by changing assembly parameters. An assembly plan candidate, a step of calculating an assembly plan candidate evaluation value of each of the plurality of assembly plan candidates, and an assembly plan candidate having the best assembly plan candidate evaluation value among the plurality of assembly plan candidates. And a step of determining as an assembly plan. The parts procurement plan creation step includes a plurality of parts procurement plan candidates and a plurality of parts procurement plans based on a plurality of parts procurement conditions obtained by changing the determined assembly plan, BOM data, and procurement parameters. The step of calculating each part procurement plan candidate evaluation value of each part procurement plan candidate, and the part procurement plan candidate having the best part procurement plan candidate evaluation value among the plurality of part procurement plan candidates as the part procurement plan Determining. By using the production plan optimization method, it is possible to optimize both the assembly plan of the ordered product and the procurement plan of the parts necessary for the assembly of the ordered product.

  The production plan optimization method may further include a step of preparing the storage device (50). The storage device (50) includes second association data associated with a product ID, an assembly process ID, a standard assembly condition, and an assembly penalty value added when the assembly condition is changed from the standard assembly condition. The procurement condition is changed from the fourth association data in which the product ID, the part ID, and the assembly process ID are associated, the part ID, the procurement process ID, the standard procurement condition, and the standard procurement condition. You may memorize | store the 3rd correlation data linked | related with the procurement penalty value added in the case. The assembly plan creation step calculates the plurality of assembly plan candidates and the respective assembly plan candidate evaluation values based on the first data, the second data, and the second association data. May be included. The component procurement plan creation step includes the plurality of component procurement plan candidates and each of the component procurement plan candidates based on the determined assembly plan, the third association data, and the fourth association data. It may include calculating an evaluation value. In the production plan optimization method, it is possible to efficiently calculate the assembly plan candidate evaluation value and the parts procurement plan candidate evaluation value based on the data stored in the storage device.

  According to the present invention, it is possible to provide a production plan optimization system and a production plan optimization method for optimizing both an assembly plan for an ordered product and a procurement plan for parts necessary for assembling the ordered product.

FIG. 1 is a table showing order data. FIG. 2 is a functional block diagram illustrating an example of a production plan optimization system in the embodiment. FIG. 3 is a conceptual diagram illustrating an example of an outline of a production plan optimization method in the embodiment. FIG. 4 is a table conceptually showing the contents of the order data stored in the storage device. FIG. 5A is a table schematically showing the “order unit” leveling method. FIG. 5B is a table schematically showing a method for leveling the “assembly process unit”. FIG. 6 is a table conceptually showing the contents of the process master data stored in the storage device. FIG. 7 is a diagram illustrating an example of how man-hours are allocated to resources. FIG. 8 is a table conceptually showing the contents of another process master data stored in the storage device. FIG. 9 is a table conceptually showing the contents of BOM data stored in the storage device. FIG. 10 is a table conceptually showing the contents of the parts master data stored in the storage device. FIG. 11 is a table conceptually showing the contents of resource master data stored in the storage device. FIG. 12 is a table conceptually showing the contents of the load fluctuation setting data stored in the storage device. FIG. 13 is a table conceptually showing the contents of the transport master data stored in the storage device. FIG. 14A is a flowchart illustrating an example of the production plan optimization method according to the embodiment. FIG. 14B is a flowchart illustrating an example of the production plan optimization method according to the embodiment. FIG. 15 is a diagram illustrating an example of an assembly plan candidate. FIG. 16 is a diagram illustrating an example of an assembly plan candidate. FIG. 17 is a diagram illustrating an example of an assembly plan candidate. FIG. 18 is a diagram illustrating an example of an assembly plan candidate. FIG. 19 is a diagram illustrating an example of a part procurement plan candidate. FIG. 20 is a diagram illustrating an example of a part procurement plan candidate. FIG. 21 is a diagram illustrating an example of a part procurement plan candidate. FIG. 22 shows an example of an assembly plan output to the output device. FIG. 23 shows an example of a parts procurement plan output to the output device. FIG. 24 is a conceptual diagram illustrating the assembly resource load penalty value. FIG. 25 is a diagram illustrating the relationship between the transition of the number of parts stock and the number of safety stocks. FIG. 26 is a conceptual diagram illustrating the inventory penalty value.

  Hereinafter, a production plan optimization system and a production plan optimization method will be described with reference to the accompanying drawings.

(Definition of terms)
In the present specification, the “assembly parameter” means a parameter that constitutes assembly conditions and can be changed in a product assembly process. The assembly parameter includes an assembly mode, an assembly lead time, and the like.
In this specification, the “procurement parameter” means a parameter that configures component procurement conditions and can be changed in the component procurement process. The procurement parameters include the procurement mode, the procurement lead time, the number of parts procured, the parts delivery date, and the like.
In the present specification, the “backward method” means a method in which the execution schedule of each process is determined in the order of the process executed first to the process executed first. That is, in the “backward method”, the execution schedule of the process executed later is determined earlier.

(Matters recognized by the inventor)
Referring to FIG. 1, it is assumed that a production plan is created for a product whose order ID is “A001” and product ID indicating the type of product is “C001”. The delivery date of the product with the product ID “C001” is September 30, 2015. The process of assembling the product with the product ID “C001” includes four processes “E001” to “E004”, and the lead time of each process is “2 days” and “3 days”, respectively. , “1 day”, “4 days”. In this specification, “lead time” means “required period”, for example, “required days”. When the production plan is determined by the backward method, the start dates of the process “E001”, the process “E002”, the process “E003”, and the process “E004” are “September 21” and “September 23”, respectively. "September 26" and "September 27". For this reason, for example, parts used in the process “E001” need to be procured by September 20, and parts used in the process “E002” need to be procured by September 22. . Conventionally, parts have been procured in consideration of procuring parts used in the processes by the start date of each process. For this reason, the leveling of the work load generated when parts are procured may be insufficient. In this specification, “procurement” means that parts are manufactured and procured at a factory, parts are transported and procured from other factories or warehouses, and parts are purchased and procured from a distributor. Is included.

(Embodiment)
A production plan optimization system and a production plan optimization method according to the embodiment will be described with reference to FIGS. FIG. 2 is a functional block diagram illustrating an example of the production plan optimization system 1 according to the embodiment. The production plan optimization system 1 includes an input device 10, a calculation device 20, a storage device 50, and an output device 60. The input device 10 is a device for inputting data to the arithmetic device 20. The input device 10 is a keyboard, a mouse, or another computer. When input data is already stored in the storage device 50, the storage device 50 may function as an input device that inputs the input data to the arithmetic device 20.

  The arithmetic device 20 includes a hardware processor such as a CPU. The computing device 20 is connected to each of the input device 10, the storage device 50, and the output device 60 so that information can be transmitted. The computing device 20 creates an assembly plan and a parts procurement plan based on the data received from the input device 10 and the data stored in the storage device 50. The arithmetic unit 20 functions as an assembly plan creation unit 22 and a parts procurement plan creation unit 24 by executing a program stored in the storage device 50. The program may be installed in the storage device 50 via an arbitrary recording medium 70. The storage device 50 stores the above-described program and various data necessary for creating an assembly plan and a parts procurement plan.

  The output device 60 is a display device such as a display, for example. The output device 60 receives the assembly plan and the parts procurement plan created by the arithmetic device 20 from the arithmetic device 20, and outputs (for example, displays) the received assembly plan and the parts procurement plan.

  FIG. 3 is a conceptual diagram illustrating an example of an outline of a production plan optimization method in the embodiment. Note that the case where some of the elements shown in FIG. 3 are omitted or the case where elements not shown in FIG. 3 are added are also included in the production plan optimization method in the embodiment.

  With reference to FIG. 3, it is understood that an assembly plan is first created and then a parts procurement plan is created. In the present specification, “component” includes “material” in addition to “component”.

  When creating the assembly plan, first data indicating the type of the ordered product and second data indicating the delivery date (delivery date) of the ordered product are transmitted from the input device 10 to the computing device 20. When there are a plurality of ordered products, the first data indicating the type of each ordered product and the second data indicating the delivery date of each ordered product are transmitted from the input device 10 to the computing device 20.

  Further, when creating an assembly plan, data such as assembly conditions and assembly penalty values stored in the storage device 50 are transmitted from the storage device 50 to the computing device 20. The assembly condition is constituted by at least one assembly parameter. Also, the total assembly penalty value changes as the assembly parameters (assembly conditions) change.

  The computing device 20 (assembly plan creation means 22) creates an assembly plan candidate based on the first data, the second data, and the assembly conditions, and the first data and the second data and other assembly conditions (that is, , Another assembly plan candidate is created based on the other assembly conditions obtained by changing the assembly parameters. In other words, the arithmetic unit 20 (assembly plan creation means 22) creates a plurality of assembly plan candidates based on the first data and the second data and a plurality of assembly conditions obtained by changing the assembly parameters. To do. The arithmetic unit 20 (assembly plan creation means 22) calculates an assembly plan candidate evaluation value for each assembly plan candidate. The assembly plan candidate evaluation value is the sum of the assembly penalty values. The arithmetic unit 20 (assembly plan creation means 22) determines an assembly plan to be adopted that has the best assembly plan candidate evaluation value from among a plurality of assembly plan candidates.

  When creating a parts procurement plan, data such as parts procurement conditions and procurement penalty values stored in the storage device 50 are transmitted from the storage device 50 to the computing device 20. The parts procurement conditions are configured by at least one procurement parameter. Further, the total procurement penalty value changes as the procurement parameters (part procurement conditions) change.

  The computing device 20 (parts procurement plan creation means 24) includes the determined assembly plan (more specifically, the start time of each assembly process included in the determined assembly plan), BOM data, and part procurement conditions. Based on the above, a part procurement plan candidate is created, and another part procurement plan candidate is created based on the determined assembly plan, BOM data, and another part procurement condition (in addition, another part procurement condition Is another part procurement condition obtained by changing the procurement parameters). In other words, the arithmetic unit 20 (parts procurement plan creation means 24) can determine a plurality of parts based on the determined assembly plan, BOM data, and a plurality of parts procurement conditions obtained by changing the procurement parameters. Create procurement plan candidates. In this specification, the BOM data means data that associates an article (for example, a product), a part necessary for manufacturing the article, and a process necessary for manufacturing the article.

  The arithmetic unit 20 (part procurement plan creation means 24) calculates a part procurement plan candidate evaluation value for each part procurement plan candidate. The part procurement plan candidate evaluation value is the sum of the procurement penalty values. The total procurement penalty value may include an inventory penalty value calculated based on the inventory data of the part and the number of safety stocks of the part. The inventory penalty value is one of the procurement penalty values. The arithmetic unit 20 (parts procurement plan creation means 24) determines a part procurement plan having the best part procurement plan candidate evaluation value to be adopted from among a plurality of part procurement plan candidates.

  The output device 60 outputs the determined assembly plan and parts procurement plan. The output assembly plan may be an assembly schedule plan and / or an assembly load plan. Further, the output part procurement plan may be at least one of a parts procurement schedule plan, a parts procurement load plan, or a parts inventory plan.

  Next, data (or a file) stored in the storage device 50 will be described. The storage device 50 stores, for example, order data 110, process master data 120 and 125, BOM data 130, parts master data 140, resource master data 150, load fluctuation setting data 160, transportation master data 170, and the like, which will be described later. .

(Order data 110)
FIG. 4 is a table conceptually showing the contents of the order data 110 stored in the storage device 50. The order data 110 may be data in an order file. Referring to FIG. 4, the order data 110 includes an order ID, a delivery destination ID, a product ID, product version data, quantity data, a base ID, accuracy data, a work start possible date, a delivery date, and the like. , Data associated with the leveling method (hereinafter referred to as “first association data”). The first association data may be data in which any two or more items among the items described in the first row of FIG. 4 are associated. For example, the first association data may be data in which an order ID, a product ID, and a delivery date are associated. Alternatively, in the first association data, the order ID, product ID, delivery date, and any other one or more items (items described in the first row in FIG. 4) are associated. May be the data.

  The order ID is an ID given to each order. The delivery destination ID is an ID indicating the delivery destination of the ordered product corresponding to the order ID. The product ID is an ID indicating the type of the ordered product corresponding to the received order ID. One order ID and a plurality of product IDs may be associated (that is, one order may include a plurality of types of products). The product version data is data indicating the version of the ordered product. When multiple product versions (a product version includes product models, product options, product specifications, etc.) are prepared for a certain type of product, the product version data is used to identify the version of the ordered product. It is data. The quantity data is data indicating the order quantity of products corresponding to the product ID (or both product ID and version data). The base data is an ID for specifying a base (factory or the like) where the product is assembled. The accuracy data indicates the accuracy of the order. For example, the accuracy A may indicate an already confirmed order, the accuracy B may indicate an order for which an order is obtained, and the accuracy C may indicate an order currently under negotiation. The work start possible date indicates a date when the assembly work can be started (in principle, it is impossible to start the assembly work before the work start possible date). The delivery date indicates the date when the ordered product is delivered to the delivery destination.

  The leveling method indicates a leveling method that is used when leveling a work load by changing assembly conditions (more specifically, an assembly lead time, which is one of the assembly parameters). The “order unit” leveling method is a leveling method for changing the lead time of the entire order. Although not shown in FIG. 4, instead of the “order unit” leveling method, for example, an “assembly process unit” leveling method may be employed. The leveling method for each assembly process unit is a leveling method for changing only the lead time of some assembly processes among a plurality of assembly processes necessary for assembling the ordered product corresponding to the order.

  FIG. 5A is a table schematically showing the “order unit” leveling method. In the example shown in FIG. 5A, the assembly process includes an assembly process A with a lead time of 2 days and an assembly process B with a lead term of 2 days. It is assumed that the lead time of the entire assembly process is shortened by “order unit”. In the example shown in FIG. 5A, the lead time of the entire assembly process is reduced by half. As a result, the lead time of the assembly process A is 1 day, and the lead time of the assembly process B is 1 day. When the lead time of the entire assembly process is shortened by “order unit”, the lead time of each assembly process may be shortened by multiplying the lead time of each assembly process by the same lead time reduction coefficient. In the example described in FIG. 5A, the lead time reduction coefficient is “0.5”. In the example shown in FIG. 5A, the work load per unit time (unit number of days) in each assembly process is doubled by halving the lead time of each assembly process.

  FIG. 5B is a table schematically showing a method for leveling the “assembly process unit”. In the example shown in FIG. 5B, the assembly process includes an assembly process A having a lead time of 2 days and an assembly process B having a lead term of 2 days. Assume that the lead time of the entire assembly process is shortened by “unit of assembly process”. In the example shown in FIG. 5B, only the lead time of the assembly process A is shortened by half, and the lead time of the assembly process B is maintained. As a result, the lead time of assembly process A is 1 day, and the lead time of assembly process B is 2 days. When shortening the lead time of the entire assembly process by “unit of assembly process”, the lead time of the assembly process to be shortened can be shortened by multiplying the lead time of the assembly process to be shortened by a lead time reduction coefficient. . In the example described in FIG. 5B, the lead time reduction coefficient is “0.5”. In the example shown in FIG. 5B, the work load per unit time (unit number of days) in the assembly process A is doubled because the lead time of the assembly process A is halved. On the other hand, the work load per unit time (unit number of days) in the assembly process B is maintained by maintaining the lead time of the assembly process B.

(Process master data 120)
FIG. 6 is a table conceptually showing the contents of the process master data 120 stored in the storage device 50. The process master data 120 may be data in a process master file. The process master data 120 includes an article ID (product ID), an article name (product name), an article version data (product version data), a process ID, a process name, process order data, a mode ID, and a mode. The penalty value, standard lead time (standard LT), shortest lead time, setup lead time, lead time reduction penalty value, resource ID, standard man-hour, setup man-hour, and load pattern are associated with each other. Data (hereinafter referred to as “second association data”). Note that the second association data may be data in which any two or more items among the items described in the first row of FIG. 6 are associated. For example, the second association data includes an article ID (product ID), a process ID, process order data, a mode ID and / or standard lead time, a mode penalty value and / or an LT shortened penalty value. May be the data. Alternatively, the second association data includes the item ID (product ID), the process ID, the process order data, the mode ID and / or the standard lead time, the mode penalty value and / or the LT shortening penalty value, Data associated with any other one or more items (items described in the first row of FIG. 6) may be used.

  In the process master data 120, the product ID and product version data are the same as the product ID and product version data, respectively. The article name is the name of the article corresponding to the article ID. The process ID is an assembly process ID indicating a process necessary for assembling (manufacturing) an article corresponding to the article ID. A plurality of process IDs may be associated with one article ID. In the example illustrated in FIG. 6, three process IDs “E001, E002, E003” are associated with the article ID “C001”. The process name is the name of the process corresponding to the process ID.

  The process order data is data indicating the order in which each process is executed. For example, in the example illustrated in FIG. 6, when the product A is assembled, the process A is executed first, then the process B is executed, and then the process C is executed.

  The mode ID is an assembly mode ID indicating a mode that can be selected when a process corresponding to the process ID is executed. In the example illustrated in FIG. 6, the arithmetic unit 20 selects one mode from among the mode with the mode ID “F001” and the mode ID “F002” as the assembly mode for executing the process C. Can be selected. Note that the process ID, the mode ID, and the resource ID are preferably stored in association with each other. When the process ID, the mode ID, and the resource ID are stored in association with each other, the arithmetic unit 20 executes the process corresponding to the process ID based on both the process ID and the mode ID. It becomes possible to acquire the resource ID of the resource to be used (for example, assembly equipment, assembly work team, etc.). For example, when the mode with the mode ID “F001” is selected as the mode for executing the process C, the resource with the resource ID “RES4” is used, and the mode with the mode ID “F002” is selected. In this case, a resource with a resource ID “RES5” is used.

  Note that one of the selectable assembly modes may be a standard assembly mode corresponding to standard assembly conditions. In the example described in FIG. 6, among the selectable assembly modes, the mode located on the upper side in the table of FIG. 6 is the standard assembly mode corresponding to the standard assembly condition. For example, among the plurality of assembly modes corresponding to the process C, the assembly mode positioned at the upper side (the mode ID is “F001”) is the standard assembly mode. The mode penalty value is a penalty value (assembly mode penalty value) added when an assembly mode other than the standard assembly mode corresponding to the standard assembly condition is selected. The assembly mode penalty value is one of the assembly penalty values. In the example shown in FIG. 6, when the mode with the mode ID “F002” is selected as the mode for executing the process C, a penalty value of “1000” is added as the assembly mode penalty value. The In other words, the sum of the assembly penalty values includes “1000” which is an assembly mode penalty value. In the example shown in FIG. 6, when the mode selected by the arithmetic unit 20 is changed, the standard lead time and the like are also changed.

  The standard lead time indicates a required period (number of required days) required for executing an assembly process under standard assembly conditions. In the example shown in FIG. 6, it is understood that, when the process A is executed, it takes 4 days as a standard. The shortest lead time indicates the minimum required period (required number of days) required for executing the assembly process. In the example illustrated in FIG. 6, it is understood that the minimum required period for executing the process A is 2 days. That is, the arithmetic unit 20 can select a period of two days or more, which is the shortest lead time, as the lead time of the process A. More specifically, 2 days, 3 days, 4 days, 5 days, 6 days, or the like can be selected as the lead time of step A. Note that the arithmetic unit 20 cannot select a lead time shorter than the shortest lead time corresponding to the assembly process for the assembly process.

  The lead time shortening penalty value (LT shortening penalty value) is added when a lead time other than the standard lead time corresponding to the standard assembly condition (more specifically, a lead time shorter than the standard lead time) is selected. This is a penalty value (assembly lead time reduction penalty value). When selecting a lead time longer than the standard lead time as the lead time for the assembly process, the penalty value for reducing the assembly lead time is not added, and a lead time shorter than the standard lead time is selected as the lead time for the assembly process. When doing so, an assembly lead time reduction penalty value may be added.

  When a lead time shorter than the standard lead time corresponding to the process is selected as the lead time of the process for a certain process, every time the predetermined lead time is shorter than the standard lead time (for example, every day shorter) ), An assembly lead time reduction penalty value may be added. In the example illustrated in FIG. 6, when 2 days is selected as the lead time for the process A, the total assembly lead time reduction penalty value to be applied is “200 = (4-2) × 100”. . Alternatively, an assembly lead time reduction penalty value may be added each time the standard lead time is shortened by a predetermined ratio (for example, 10%). In the example illustrated in FIG. 6, when the lead time of the process A is “4” × 90% = “3.6”, the total assembly lead time reduction penalty value to be applied is “100”. In other words, the arithmetic unit 20 sets the value obtained by multiplying the deviation between the standard lead time and the selected lead time by the assembly lead time reduction penalty value as the sum of the assembly lead time reduction penalty values. The assembly lead time reduction penalty value is one of the assembly penalty values.

  The resource ID is an assembly resource ID indicating a resource used when executing each process. The resource ID is stored in association with the process ID (or both the process ID and the mode ID). When a plurality of resources are used in one process, the plurality of resources are stored in association with one process. In the example illustrated in FIG. 6, when the process A is executed, a resource with a resource ID “RES1” and a resource with a resource ID “RES2” are used. The resource corresponding to the resource ID may be, for example, an assembly facility or a work team. When there are a plurality of resources of the same type, one resource may be stored as a standard resource and the remaining resources may be stored as selectable resources. In this case, the storage device 50 stores a penalty value (assembly resource penalty value) added when a resource other than the standard resource is selected. When the computing device 20 selects a resource other than the standard resource, the assembly resource penalty value is added to the assembly plan candidate evaluation value (that is, the total assembly penalty). The assembly resource penalty value is one of the assembly penalty values.

  The standard man-hour is an index indicating the load acting on the corresponding resource when executing the corresponding process. For example, the standard man-hour may indicate the standard time (the time when the corresponding resource is occupied when executing the corresponding process), or the standard total work time (the operation when the corresponding process is executed). It may indicate the amount of work (number of people x time) performed by the team. The setup man-hour is an index indicating a load acting on the corresponding resource when preparing the corresponding process. For example, the setup man-hour may indicate the setup time (the time when the corresponding resource is occupied when preparing the corresponding process), or the total setup work time (when preparing the corresponding process) In addition, the amount of preparation work (number of people × time) performed by the work group may be indicated.

  The load pattern indicates how to allocate man-hours (for example, the sum of standard man-hours and setup man-hours) to resources. For example, it is assumed that a task with a man-hour of 40 is allocated to a resource within a period in which the lead time is 4 days. FIG. 7 illustrates five types of allocation methods.

  In “(1) Equal” in FIG. 7, the man-hours are allocated equally within the period. In “(2) first half” in FIG. 7, the man-hours are assigned to the first half in the period. In “(3) second half” in FIG. 7, the man-hours are allocated in the second half of the period. In “(4) first day” in FIG. 7, the man-hours are allocated on the first day in the period. In “(5) Last day” in FIG. 7, the man-hours are assigned to the last day in the period. In FIG. 7, the portion where the man-hours are not allocated is a margin time. The computing device 20 selects a load pattern stored in association with the process ID and the resource ID from among a plurality of load patterns.

(Process master data 125)
FIG. 8 is a table conceptually showing the contents of the process master data 125 stored in the storage device 50. The process master data 125 may be data in a process master file.

  The process master data 125 includes an article ID (part ID), an article name (part name), an article version data (part version data), a process ID, a process name, process order data, a mode ID, and a mode. Penalty value, standard lead time (standard LT), shortest lead time, setup lead time, lead time shortening penalty value (LT shortening penalty value), resource ID, standard man-hour, setup man-hour, Data associated with a load pattern (hereinafter referred to as “third association data”). The third association data may be data in which any two or more items among the items described in the first row of FIG. 8 are associated. For example, the third association data includes an article ID (part ID), a process ID, process order data, a mode ID and / or standard lead time, a mode penalty value and / or an LT shortened penalty value. May be the data. Alternatively, the third association data includes an article ID (part ID), a process ID, process order data, a mode ID and / or standard lead time, a mode penalty value and / or an LT shortened penalty value, Data associated with any other one or more items (items described in the first row of FIG. 8) may be used.

  The component ID is an ID indicating the type of component. The component may be a component composed of a plurality of sub-components, a component composed of one piece such as a screw, or a material such as an adhesive. The part version data is data indicating the version of the part. The process ID is a component procurement process ID indicating a process necessary for procurement of an article corresponding to the article ID (part ID). A plurality of process IDs may be associated with one article ID (component ID). The process name is the name of the process corresponding to the process ID.

  The process order data is data indicating the order in which each process is executed. For example, in the example illustrated in FIG. 8, when the part A is procured, the process D is executed first, and then the process E is executed.

  The mode ID is a procurement mode ID indicating a selectable mode when executing a process corresponding to the process ID. In the example illustrated in FIG. 8, the computing device 20 selects one mode from among the mode with the mode ID “J001” and the mode ID “J002” as the procurement mode for executing the process E. Can be selected. The process ID, mode ID, and resource ID are preferably stored in association with each other. When the process ID, the mode ID, and the resource ID are stored in association with each other, the arithmetic unit 20 executes the process corresponding to the process ID based on both the process ID and the mode ID. It becomes possible to acquire the resource ID of the resource to be used (for example, parts production equipment, parts transportation equipment, etc.). For example, when the mode with the mode ID “J001” is selected as the mode for executing the process E, the resource with the resource ID “RES8” is used, and the mode with the mode ID “J002” is selected. In this case, the resource with the resource ID “RES9” is used.

  Note that one of the selectable modes may be a standard procurement mode corresponding to standard procurement conditions. The mode penalty value is a penalty value (procurement mode penalty value) to be added when the computing device 20 selects a mode other than the standard procurement mode corresponding to the standard procurement condition. The procurement mode penalty value is one of the procurement penalty values. In the example shown in FIG. 8, when the mode with the mode ID “J002” is selected as the mode for executing the process E, a penalty value of “100” is added as the procurement mode penalty value. The The mode with the mode ID “J002” may be, for example, a mode corresponding to purchasing and procuring the part A from the outside. In the example shown in FIG. 8, when the mode selected by the arithmetic unit 20 is changed, the standard lead time and the like are also changed.

  The standard lead time indicates a required period (the required number of days) required for executing the parts procurement process under the standard procurement conditions. In the example shown in FIG. 8, it takes 4 days as a standard when performing the process D. The shortest lead time indicates the minimum required period (the required number of days) required for executing the parts procurement process. In the example illustrated in FIG. 8, the minimum required period for executing the process D is 2 days. Note that the arithmetic unit 20 cannot select a lead time shorter than the shortest lead time corresponding to the assembly process for the assembly process.

  The lead time shortening penalty value (LT shortening penalty value) is added when a lead time other than the standard lead time corresponding to the standard procurement conditions (more specifically, a lead time shorter than the standard lead time) is selected. This is the penalty value (the procurement lead time reduction penalty value). When selecting a lead time longer than the standard lead time as the lead time for the parts procurement process, the procurement lead time reduction penalty value is not added and the lead time shorter than the standard lead time is used as the lead time for each process. When selecting, a procurement lead time reduction penalty value may be added.

  When a lead time shorter than the standard lead time corresponding to the process is selected as the lead time of the process for a certain process, every time the predetermined lead time is shorter than the standard lead time (for example, every day shorter) ) A procurement lead time reduction penalty value may be added. Alternatively, a procurement lead time reduction penalty value may be added each time the standard lead time is shortened by a predetermined ratio (for example, 10%). In other words, the arithmetic unit 20 sets the value obtained by multiplying the deviation between the standard lead time and the selected lead time by the procurement lead time reduction penalty value as the total of the procurement lead time reduction penalty values. The procurement lead time reduction penalty value is one of the procurement penalty values.

  Resource ID is procurement resource ID which shows the resource used when performing each process. When a plurality of resources are used in one process, the plurality of resources are stored in association with one process. In the example illustrated in FIG. 8, when the process D is executed, a resource with a resource ID “RES6” and a resource with a resource ID “RES7” are used. The resource corresponding to the resource ID may be, for example, a part production facility, a transportation facility or a transportation facility, or a work team. When there are a plurality of resources of the same type, one resource may be stored as a standard resource and the remaining resources may be stored as selectable resources. In this case, the storage device 50 stores a penalty value (procurement resource penalty value) that is added when a resource other than the standard resource is selected. When the computing device 20 selects a resource other than the standard resource, the procurement resource penalty value is added to the part procurement plan candidate evaluation value (that is, the total procurement penalty). The procurement resource penalty value is one of the procurement penalty values.

  Since standard man-hours, setup man-hours, and load patterns have already been explained, repeated explanations are omitted.

  Note that the process master data 120 illustrated in FIG. 6 and the process master data 125 illustrated in FIG. 8 may be included in one process master file.

(BOM data 130)
FIG. 9 is a table conceptually showing the contents of the BOM data 130 stored in the storage device 50. “BOM” is an abbreviation for “Bill Of Materials”. The BOM data 130 may be data in a BOM file. The BOM data requires that a part corresponding to the part ID is necessary to produce an article corresponding to the article ID, and that the part is necessary in a production process (for example, an assembly process) of the article corresponding to the process ID. It is data which shows.

  The BOM data 130 includes an item ID (product ID or component ID), an item name, an item version data, a component ID, a required number of components, a component name, a process ID, a process name, and a component start process. This is data (hereinafter referred to as “fourth association data”) associated with data indicating the number of days before the day. Note that the fourth associated data may be data in which any two or more items among the items described in the first row of FIG. 9 are associated. For example, the fourth association data includes an article ID (product ID or part ID), a part ID indicating a part necessary for producing the article corresponding to the article ID, and a process necessary for producing the article corresponding to the article ID. May be data associated with a process ID indicating. Alternatively, the fourth association data includes the item ID (product ID or component ID), the component ID, the process ID, and any other one or more items (described in the first row of FIG. 9). Item) may be associated data.

  As shown in FIG. 9, a plurality of component IDs may be associated with one article ID (or a combination of one article ID and one process ID). In the example illustrated in FIG. 9, two component IDs (“G001” and “G003”) are associated with the combination of the article ID (C001) and the process ID (E003).

  The required number of parts is stored in association with the part ID and the process ID. In the example illustrated in FIG. 9, the required number of parts corresponding to the part A and the process C is “1”. The required number of parts indicates the required number of parts required for the process corresponding to the process ID. The required number of parts may be the required number of parts or the required number of materials (for example, 10 kg, etc.).

(Parts master data 140)
FIG. 10 is a table conceptually showing the contents of the component master data 140 stored in the storage device 50. The component master data 140 may be data in a component master file.

  The part master data 140 includes a part ID, a part name, classification data, a standard production number, a minimum production quantity, a maximum production quantity, a production lot number, a transportation lot number, a weight, a volume, a safety This is data (hereinafter referred to as “fifth association data”) in which the number of stocks, a missing part penalty value, a safety stock interruption penalty value, a safety stock excess penalty value, and a leveling method are associated. Note that the fifth associated data may be data in which any two or more items among the items described in the first row of FIG. 10 are associated. For example, the fifth association data may be data in which a component ID, the number of safety stocks, and a safety stock interruption penalty value are associated. Alternatively, the fifth association data includes the part ID, the number of safety stocks, the safety stock interruption penalty value, and any other one or more items (items described in the first row of FIG. 10). May be associated data.

  Since the component ID and the leveling method have already been described, repeated description will be omitted. The component name is the name of the component corresponding to the component ID. The part name may be a material name. The classification data is the part used in the part procurement process (for example, part production process) of the part of the ordered product, whether the part corresponding to the part ID is the part used in the assembly process of the ordered product. It is data indicating whether or not there is. The standard production number indicates the number of parts to be procured (for example, production quantity or production amount) when parts are procured under standard procurement conditions. For example, the standard number of parts B is “10”. Therefore, when the required number of parts B is three, “10” parts B are procured under the standard procurement conditions, and when the required number of parts B is eleven, under the standard procurement conditions, “20” ”Will be procured.

  The minimum production number indicates the minimum value of the number of parts allowed when parts are procured under conditions other than the standard procurement conditions. That is, the minimum number of manufactured parts is the minimum number of parts manufactured in one order. The maximum production number indicates the maximum value of the allowable number of parts production. In other words, the maximum production number is the maximum number of parts produced in one order. If it is necessary to produce more parts than the maximum quantity, the order is divided. The production lot number indicates the production unit of parts, that is, the number of parts (or the amount of materials) produced at one time. For example, when the minimum production number is “10”, the maximum production number is “20”, and the production lot number is “5”, the production quantities of parts included in one order are “10”, “ 15 "or" 20 ". The number of transportation lots indicates the transportation unit of parts. For example, when the number of transportation lots is “N”, the number of parts transported is an integral multiple of “N”. The weight indicates the weight per transportation lot, and the volume indicates the volume per transportation lot. The safety stock number means the number of parts stock that is desirably set and maintained for each part. For example, if the number of parts in stock is equal to or greater than the number of safety stocks, the risk of missing parts is small even when a defective part is found and the defective part is discarded.

  The missing part penalty value is a penalty value added when a part procurement plan candidate includes a state where a part is missing. The missing part penalty value is one of the procurement penalty values. For example, the missing part penalty value may be added each time the number of missing parts increases by one. In other words, when the number of necessary parts exceeds the number of parts in stock, the arithmetic unit 20 calculates the number of missing parts by subtracting the number of parts in stock from the number of necessary parts, and obtains the number of missing parts by multiplying the number of missing parts by the missing part penalty value. The value obtained may be considered as the sum of the missing part penalty values.

  The safety stock interruption penalty value is a penalty value that is added when a part procurement plan candidate includes a state in which there is a part whose number of parts is less than the number of safety stocks. The safety stock interruption penalty value is one of the procurement penalty values. For example, the safety stock interruption penalty value may be added every time the number of parts stock is less than the safety stock number.

  The safety stock excess penalty value is a penalty value that is added when a part procurement plan candidate includes a state in which there is a part whose number of parts exceeds the number of safety stocks. The safety stock excess penalty value is one of the procurement penalty values. For example, the safety stock excess penalty value may be added each time the number of parts in stock exceeds the safety stock number by one. In general, storage costs and the like increase as the number of inventory increases. For this reason, it is not preferable that the stock quantity greatly exceeds the safety stock quantity. For this reason, in the example shown in FIG. 10, a safety stock excess penalty value is set.

(Parts inventory data)
The storage device 50 stores component inventory data for each component. When the part is a part that can be purchased from the outside or a part that can be transported from the outside, the part inventory data of the part includes the current part inventory data, the date of arrival of the part, and the quantity received. It may be. In this case, the number of parts stock can be expressed as a function of time (date).

(Resource master data 150)
FIG. 11 is a table conceptually showing the contents of the resource master data 150 stored in the storage device 50. The resource master data 150 may be data in a resource master file.

  The resource master data 150 includes a resource ID, resource name, duplication availability data, load setting period, absolute load lower limit value, reference load lower limit value, reference load upper limit value, absolute load upper limit value, and reference lower limit value. Data associated with an interrupt penalty value and a reference upper limit excess penalty value (hereinafter referred to as “sixth association data”). Note that the sixth associated data may be data in which any two or more items among the items described in the first row of FIG. 11 are associated. For example, the sixth associated data may be data in which a resource ID, a reference load upper limit value, an absolute load upper limit value, and a reference upper limit excess penalty value are associated with each other. Alternatively, the sixth association data includes the resource ID, the reference load upper limit value, the absolute load upper limit value, the reference upper limit excess penalty value, and any other one or more items (first line in FIG. 11). The item described in the eye) may be associated data.

  The resource name is the name of the resource corresponding to the resource ID. The “duplication availability data” is data indicating whether or not the resource may be allocated to a plurality of operations (for example, an operation for manufacturing the part A and an operation for manufacturing the part B) in the same period. In the example illustrated in FIG. 11, for resource A, a plurality of operations may be allocated in the same period, but for resource B, only one operation can be allocated in the same period. The load setting period is a target period for setting a resource load. For example, when the load setting period is “daily”, it means that values such as the absolute load upper limit value shown in FIG. 11 are values applied every day, and the load setting period is “Wednesday”. 11 means that the value such as the absolute load upper limit value shown in FIG. 11 is a value applied every Wednesday, and when the load setting period is “other than Wednesday”, FIG. Means that the value such as the absolute load upper limit value described in FIG. 11 is a value applied every day other than Wednesday, and when the load setting period is “monthly”, the absolute load upper limit value shown in FIG. It means that a value such as a value is a value applied every month.

  The absolute load lower limit value indicates the lower limit of the resource load value that must be observed. In other words, the computing device 20 cannot create a plan (assembly plan or parts procurement plan) in which the load value is less than the absolute load lower limit value for each resource. The absolute load upper limit value indicates the upper limit of the resource load value that must be protected. In other words, the computing device 20 cannot create a plan (assembly plan or parts procurement plan) such that the load value exceeds the absolute load upper limit value for each resource. The load value is an index indicating the load acting on the resource. For example, when the resource is a work team, the load value may be a work time. Further, when the resource is a facility, the load value may be the resource operation time or the number of parts manufactured per unit time by the resource.

  The reference load lower limit value indicates the lower limit of the load value that is desirably protected. In the plan candidate (assembly plan candidate or parts procurement plan candidate) created by the arithmetic unit 20, if the load value of a certain resource is lower than the reference load lower limit value, the reference lower limit interrupt penalty value is added. The reference lower limit interrupt penalty value is one of the assembly penalty values when the resource is a resource used in the product assembly process, and when the resource is a resource used in the parts procurement process. Is one of the procurement penalty values. For example, in the plan candidate created by the arithmetic unit 20, when the load value of the resource A is “5”, the total of the reference lower limit interrupt penalty values is “10 = (reference load lower limit value“ 6 ”−load value“ 5 ") x (lower limit interrupt load penalty value" 10 ")". The total of the reference lower limit interrupt penalty values may be the total of the reference lower limit interrupt penalty values calculated for each resource for each day.

The reference load upper limit value indicates the upper limit of the load value that is desirably protected. When the load value of a certain resource exceeds the reference load upper limit value in the plan candidate (assembly plan candidate or parts procurement plan candidate) created by the arithmetic unit 20, a reference upper limit excess penalty value is added. The reference upper limit excess penalty value is one of the assembly penalty values when the resource is a resource used in the product assembly process, and when the resource is a resource used in the parts procurement process. Is one of the procurement penalty values. For example, in the plan candidate created by the arithmetic unit 20, when the load value of the resource A is “11”, the reference upper limit excess penalty value is “30 = (load value“ 11 ”−reference load upper limit value“ 8 ”. ") X (upper limit load penalty value" 10 ")". The total of the reference upper limit excess penalty values may be the total of the reference upper limit excess penalty values calculated every day for each resource.

(Load variation setting data 160)
FIG. 12 is a table conceptually showing the contents of the load fluctuation setting data 160 stored in the storage device 50. The load fluctuation setting data 160 may be data in the load fluctuation setting file.

  For example, assume that the resource is a work group. It is not preferable that the load acting on the work group fluctuates greatly in a short period of time. For example, when the load increases rapidly, there is a risk that the addition of workers to the work team may not be in time. For this reason, it is preferable to create a plan (assembly plan or parts procurement plan) in which the resource load does not fluctuate greatly in a short period of time.

  The load fluctuation setting data 160 is data in which a resource ID, a resource name, a period for setting load fluctuation (current month, next month, etc.), and fluctuation type data (for example, load fluctuation upper limit or load fluctuation lower limit) are associated with each other. (Seventh association data). The seventh association data may be data in which any two or more items among the items described in the first row of FIG. 12 are associated. For example, the seventh association data may be data in which a resource ID, a period for setting a load variation, and variation type data are associated.

  The computing device 20 selects an assembly plan candidate and a parts procurement plan candidate under the constraint that the load fluctuation of the resource does not exceed the load fluctuation upper limit and / or the constraint that the load fluctuation does not fall below the lower limit of the load fluctuation. You may decide. In the example illustrated in FIG. 12, for example, when the current load of the resource A is “100 / day”, the load of the current month of the resource A is not allowed to exceed 110% of “100 / day”. The load of resource A next month is not allowed to exceed 120% of “100 / day”, and the load after 2 months of resource A is not allowed to exceed 130% of “100 / day”. . Further, when the current load of the resource A is “100 / day”, the load of the current month of the resource A is not allowed to fall below 95% of “100 / day”, and the load of the resource A next month is not allowed. , It is not allowed to fall below 90% of “100 / day”, and the load of resource A after 2 months is not allowed to fall below 85% of “100 / day”.

(Transportation master data 170)
FIG. 13 is a table conceptually showing the contents of the transport master data 170 stored in the storage device 50. The transportation master data 170 may be data in the transportation master file.

  The transport master data 170 is data (hereinafter referred to as “eighth association”) in which a transport source base, a transport destination base, a transport means, a transport lead time, an upper limit weight, an upper limit volume, and a transport cost penalty value are associated. Data ”). Note that the eighth associated data may be data in which any two or more items among the items described in the first row of FIG. 13 are associated. For example, the eighth association data may be data in which a transportation source base, a transportation destination base, a transportation means, a transportation lead time, and a transportation cost penalty value are associated with each other. Alternatively, the eighth association data includes the transportation source base, the transportation destination base, the transportation means, the transportation lead time, the transportation cost penalty value, and any other one or more items (the first item in FIG. 13). The item described in the first line) may be associated data.

The transportation source base indicates a base that is the transportation source of the article. The transportation destination base indicates a base that is a transportation destination of the article. The transport lead time (transport LT) indicates a required period (number of required days) required for transporting an article from a transport source base to a transport destination base. The upper limit weight and the upper limit volume indicate the upper limit value of the article weight and the upper limit value of the article volume for applying the corresponding transportation cost penalty value. In the example illustrated in FIG. 13, for example, when an article of 300 kg and 10 m ³ is transported from the base A to the base B by airplane, the transport cost penalty value is “50000”. The transportation cost penalty value includes a case where a part is transported using a specific transportation means (for example, an airplane or a ship) in a plan candidate (part procurement plan candidate) created by the arithmetic unit 20. The penalty value is added in correspondence with the use of the transportation means. The transportation cost penalty value is one of the procurement penalty values.

  Next, the production plan optimization method will be described in more detail with reference to a flowchart. FIG. 14A and FIG. 14B are flowcharts illustrating an example of the production plan optimization method according to the embodiment.

  In the first step S1, various data are stored in the storage device 50. For example, process master data 120 and 125, BOM data 130, parts master data 140, parts inventory data, resource master data 150, load fluctuation setting data 160, transportation master data 170, and the like are stored in the storage device 50. Note that the resource master data 150, the load fluctuation setting data 160, and the transport master data 170 are optional additional data and may be omitted.

  When process master data 120, 125, BOM data 130, parts master data 140, parts inventory data, resource master data 150, load fluctuation setting data 160, transportation master data 170, etc. are already stored in storage device 50. Alternatively, the first step S1 may be omitted.

  In the second step S2, the arithmetic unit 20 receives from the input device 10 first data indicating the type of the ordered product and second data indicating the delivery date of the ordered product. The first data is, for example, a product ID of an ordered product. Referring to FIG. 4, when an order with an order ID “A001” is received, product ID “C001” is input as first data to arithmetic device 20, and delivery date “2014” is input as second data. August 8 "is entered. For example, when the order data 110 shown in FIG. 4 is already stored in the storage device 50, the arithmetic unit 20 may receive the first data and the second data from the storage device 50. . In this case, the storage device 50 functions as the input device 10.

  Next, the arithmetic unit 20 (assembly plan creation means 22) executes an assembly plan creation process (third step S3 to eighth step S8 described later).

  First, in the third step S3, the arithmetic unit 20 calculates a first assembly plan candidate based on the first data, the second data, and the first assembly condition (for example, standard assembly condition).

  The first assembly condition includes first data, process master data 120 (for example, product ID, process ID, process order data, mode ID and / or standard lead time, mode penalty value and / or LT shortening penalty). Based on the second association data associated with the value). Referring to FIG. 6, the first assembly condition (standard assembly condition) is, for example, that the mode for executing process A is the standard assembly mode “F001”, and the lead time of process A is the standard lead time “4”. The mode for executing the process B is the standard assembly mode “F001”, the lead time for the process B is the standard lead time “3”, the mode for executing the process C is the standard assembly mode “F001”, and the process C Is the assembly condition with the standard lead time “5”.

  When creating an assembly plan candidate by the backward method, the arithmetic unit 20 refers to the process order data included in the process master data 120 and creates an assembly plan candidate such as the first assembly plan candidate.

  FIG. 15 shows an example of a first assembly plan candidate. The allocation period of process C, which is the final process, is determined based on the delivery date. Since the standard lead time of process C is “5 days”, “5 days” is assigned to process C. Next, the allocation period of process B, which is the process immediately before process C, is determined. Since the standard lead time for process B is “3 days”, “3 days” is assigned to process B. Next, the allocation period of the process A, which is one process before the process B (initial process), is determined. Since the standard lead time of process A is “4 days”, “4 days” is assigned to process A. In the third step S3, the arithmetic unit 20 regards the first assembly plan candidate (first assembly plan candidate) as the best assembly plan candidate (hereinafter referred to as “best assembly plan candidate”).

  Next, in FIG. 15, it is assumed that the second day corresponds to “today”. In this case, since the first day corresponds to “yesterday” which is the past day, it is clear that the assembly plan candidate shown in FIG. 15 is not allowed. When the assembly plan candidate includes a process to be performed before today, the arithmetic unit 20 adds a large penalty value (for example, “10000” or infinity) to the total assembly penalty value. . Here, “10000” is added to the total of the assembly penalty values. As a result, the assembly plan candidate evaluation value is “10000”.

  In the fourth step S4, the arithmetic unit 20 calculates the evaluation value of the assembly plan candidate calculated this time. Note that the number of assembly plan candidates calculated at one time may be one, or two or more. When a plurality of assembly plan candidates are calculated, the arithmetic unit 20 calculates an evaluation value (assembly plan candidate evaluation value) for each of the assembly plan candidates. The assembly plan candidate evaluation value is the sum of the assembly penalty values. The total of the assembly penalty values includes a penalty value (for example, an assembly mode penalty value and / or an assembly lead time reduction penalty value) given in accordance with the assembly condition.

  In the fifth step S5, the arithmetic unit 20 evaluates the evaluation value of the assembly plan candidate calculated this time (if a plurality of assembly plan candidates are calculated, out of a plurality of evaluation values corresponding to the plurality of assembly plan candidates). It is determined whether or not at least one evaluation value is smaller than the evaluation value of the best assembly plan candidate. When the evaluation value of the assembly plan candidate calculated this time is smaller than the evaluation value of the best assembly plan candidate (fifth step S5: Yes), the process proceeds to the sixth step S6. On the other hand, when the evaluation value of the assembly plan candidate calculated this time is equal to or higher than the evaluation value of the best assembly plan candidate (fifth step S5: No), the process proceeds to the seventh step S7. In the above example, the evaluation value of the assembly plan candidate calculated this time is “10000”, the evaluation value of the best assembly plan candidate is “10000”, and both evaluation values are equal to each other. In this case, the process proceeds to the seventh step S7.

  In the sixth step S6, the arithmetic unit 20 sets the assembly plan candidate calculated this time (or the one with the smallest evaluation value among the plurality of assembly plan candidates calculated this time) as the best assembly plan candidate.

  In the seventh step S7, the arithmetic unit 20 determines whether or not the end condition is satisfied. The end condition is based on, for example, that the evaluation value of the best assembly plan candidate is equal to or less than the first threshold, or the first data and the second data, and another assembly condition obtained by changing the assembly parameter. The evaluation value of another assembly plan candidate to be calculated is unlikely to be smaller than the evaluation value of the current best assembly plan candidate, or the calculation time for creating the assembly plan exceeds the second threshold Etc. If the end condition is satisfied (seventh step S7: Yes), the process proceeds to the parts procurement plan creation process (from the ninth step). On the other hand, when the end condition is not satisfied (seventh step S7: No), the process proceeds to the eighth step S8.

  In the eighth step S8, the arithmetic unit 20 uses the first data and the second data and another assembly condition obtained by changing the assembly parameters (the assembly obtained by slightly changing the assembly conditions used so far). New assembly plan candidates are calculated on the basis of (condition). That is, calculation of assembly plan candidates is repeated.

  For example, referring to FIG. 6, a case is assumed in which an assembly parameter that is a parameter related to a lead time is changed from a standard lead time to a lead time shorter by one day than the standard lead time. In other words, it is assumed that the assembly condition is changed from the assembly condition in which the standard lead time is used in each process to the assembly condition in which a lead time shorter by one day than the standard lead time is used in each process. . In this case, the new assembly plan candidate is the assembly plan candidate shown in FIG.

  After execution of the eighth step S8, the process returns to the fourth step S4. In 4th step S4, the arithmetic unit 20 calculates the evaluation value (assembly plan candidate evaluation value) of the new assembly plan candidate calculated this time. Since the new assembly plan candidate (see FIG. 16) does not include a process to be executed before today, the arithmetic unit 20 does not add “10000” to the total assembly penalty value. In other words, the evaluation value of the new assembly plan candidate does not include the penalty value “10000”. On the other hand, since a new assembly plan candidate uses a lead time shorter than the standard lead time, the evaluation value of the new assembly plan candidate includes an assembly lead time reduction penalty value (for example, 100 + 100 + 100 = 300). It is.

  In the fifth step S5, the arithmetic unit 20 determines whether or not the evaluation value of the new assembly plan candidate calculated this time is smaller than the evaluation value of the best assembly plan candidate. Since the evaluation value of the new assembly plan candidate calculated this time is “300” and the evaluation value of the best assembly plan candidate is “10000”, the arithmetic unit 20 evaluates the new assembly plan candidate acquired this time. It is determined that the value is smaller than the evaluation value of the best assembly plan candidate. Then, the process proceeds to the sixth step S6. In 6th step S6, the arithmetic unit 20 makes the new assembly plan candidate acquired this time the best assembly plan candidate.

  In the seventh step S7, it is determined whether or not an end condition is satisfied. Here, it is assumed that the termination condition is not satisfied. In this case, the process proceeds to the eighth step S8. In the eighth step S8, the arithmetic unit 20 uses the first data and the second data and another assembly condition obtained by changing the assembly parameters (the assembly obtained by slightly changing the assembly conditions used so far). New assembly plan candidates are calculated on the basis of (condition). That is, the calculation of assembly plan candidates is further repeated.

  For example, in the assembly conditions in which the assembly plan candidate shown in FIG. 15 is calculated, the mode of the process C is changed from the standard assembly mode with the mode ID “F001” to the assembly mode with the mode ID “F002” (FIG. 6). )). In other words, it is assumed that the assembly parameter, which is a parameter related to the assembly mode, is changed from the standard assembly mode to an assembly mode different from the standard assembly mode. In this case, a further new assembly plan candidate is an assembly plan candidate shown in FIG.

  After execution of the eighth step S8, the process returns to the fourth step S4. In the fourth step S4, the arithmetic unit 20 calculates an evaluation value of a further new assembly plan candidate calculated this time. The computing device 20 does not include “10000” as the total assembly penalty value because the new assembly plan candidate does not include a process to be executed before today. In other words, the evaluation value of the new assembly plan candidate does not include the penalty value “10000”. On the other hand, since a new assembly plan candidate uses an assembly mode other than the standard assembly mode, the evaluation value of the new assembly plan candidate includes an assembly mode penalty value (for example, “1000”). (See FIG. 6).

  In the fifth step S5, the arithmetic unit 20 determines whether or not the evaluation value of the further new assembly plan candidate calculated this time is smaller than the evaluation value of the best assembly plan candidate. Since the evaluation value of the further new assembly plan candidate calculated this time is “1000” and the evaluation value of the best assembly plan candidate is “300”, the arithmetic unit 20 calculates the further new assembly plan candidate calculated this time. It is determined that the evaluation value is equal to or higher than the evaluation value of the best assembly plan candidate. Then, the process proceeds to the seventh step S7.

  In the seventh step S7, it is determined whether or not an end condition is satisfied. Here, it is assumed that the termination condition is satisfied. In this case, the arithmetic unit 20 determines an assembly plan corresponding to the best assembly plan candidate as an assembly plan to be adopted. Then, the process proceeds to the ninth step S9.

  In the second step S2 described above, the first data (first data indicating the types of the ordered products) may be data indicating the types of the plurality of ordered products. Further, the second data may include a delivery date corresponding to each of the plurality of ordered products. In this case, the assembly plan candidate includes each assembly plan of a plurality of ordered products. FIG. 18 shows an example of an assembly plan candidate that includes an assembly plan for each of a plurality of ordered products. When an assembly plan candidate includes an assembly plan for each of a plurality of ordered products, the evaluation value of the assembly plan candidate is the total sum of assembly penalty values calculated corresponding to the assembly plans for each ordered product. It becomes.

  When the assembly plan to be adopted (for example, the assembly plan shown in FIG. 16) is determined, the arithmetic unit 20 (parts procurement plan creation unit 24) performs parts procurement plan creation processing (the ninth steps S9 to S14 described later). Step S14) is executed.

  In the ninth step S9, the arithmetic unit 20 determines the determined assembly plan (more specifically, the start time of each assembly process included in the determined assembly plan), BOM data 130 (for example, the type of the ordered product and The data necessary for assembling the ordered product and the assembly process necessary for assembling the ordered product) and the first component procurement conditions (for example, standard procurement conditions) 1 part procurement plan candidate is calculated.

  The first part procurement condition includes the determined assembly plan, BOM data 130, process master data 125 (for example, part ID, process ID, process order data, mode ID and / or standard lead time, Based on the third penalty data associated with the mode penalty value and / or the LT shortening penalty value). Referring to FIG. 9, assembly of product A requires part A and part C. Part A is required one day before the start date of process C and is used in process B. It is understood that the part C is required one day before the start date of the process B, and that the part C used in the process C is required one day before the start date of the process C. Referring to FIG. 8, the first part procurement condition (standard procurement condition) is, for example, that the process D is executed in the procurement of the part A, and the mode of executing the process D is the standard procurement mode “J001”. The parts procurement conditions are such that the time is the standard lead time “4”, the mode for executing the process E is the standard procurement mode “J001”, and the lead time for the process E is the standard lead time “3”. The first part procurement condition (standard procurement condition) is, for example, in the procurement of part C, the mode in which the process G is executed is the standard procurement mode “J001”, and the lead time of the process G is the standard lead time “ 2 ”is a part procurement condition.

  When creating a parts procurement plan candidate by the backward method, the arithmetic unit 20 refers to the process order data included in the process master data 125 and creates a part procurement plan candidate such as the first part procurement plan candidate. To do.

  FIG. 19 shows an example of a first parts procurement plan candidate. For part A, the allocation period of process E, which is the final process, is determined based on the delivery date (one day before the start date of process C). Since the standard lead time of process E is “3 days”, “3 days” is assigned to process E. Next, the allocation period of process D, which is the process (initial process) immediately before process E, is determined. Since the standard lead time of process D is “4 days”, “4 days” is assigned to process D. In addition, for the part C used in the process B, the allocation period of the process G is determined based on the delivery date (one day before the start date of the process B). Since the standard lead time of process G is “2 days”, “2 days” is assigned to process G. Further, for the part C used in the process C, the allocation period of the process G is determined based on the delivery date (one day before the start date of the process C). Since the standard lead time of process G is “2 days”, “2 days” is assigned to process G. In the ninth step S9, the arithmetic unit 20 refers to the first component procurement plan candidate (first component procurement plan candidate) as the best component procurement plan candidate (hereinafter, “best component procurement plan candidate”). ).

  In FIG. 19, the second day corresponds to “today”. In this case, since the first day corresponds to “yesterday” which is the past day, it is clear that the parts procurement plan candidates shown in FIG. 19 are not allowed. The arithmetic unit 20 adds a large penalty value (for example, “10000” or infinity, etc.) to the total procurement penalty value when the parts procurement plan candidate includes a process to be performed before today. To do. Here, “10000” is added to the total procurement penalty value. As a result, the part procurement plan candidate evaluation value is “10000”.

  In the tenth step S10, the arithmetic unit 20 calculates the evaluation value of the part procurement plan candidate calculated this time. Note that the number of parts procurement plan candidates calculated at one time may be one or two or more. When a plurality of component procurement plan candidates are calculated, the arithmetic unit 20 calculates an evaluation value (component procurement plan candidate evaluation value) for each of the component procurement plan candidates. The parts procurement plan candidate evaluation value is the sum of the procurement penalty values. The total of the procurement penalty values includes a penalty value (for example, a procurement mode penalty value and / or a procurement lead time reduction penalty value) given in accordance with the component procurement conditions.

  In the eleventh step S11, the arithmetic unit 20 calculates the evaluation value of the component procurement plan candidate calculated this time (if a plurality of component procurement plan candidates are calculated, a plurality of evaluation values corresponding to the plurality of component procurement plan candidates). It is determined whether at least one of the evaluation values is smaller than the evaluation value of the best parts procurement plan candidate. When the evaluation value of the part procurement plan candidate calculated this time is smaller than the evaluation value of the best part procurement plan candidate (11th step S11: Yes), the process proceeds to the 12th step S12. On the other hand, when the evaluation value of the component procurement plan candidate calculated this time is equal to or higher than the evaluation value of the best component procurement plan candidate (11th step S11: No), the process proceeds to the 13th step S13. In the above example, the evaluation value of the part procurement plan candidate calculated this time is “10000”, the evaluation value of the best parts procurement plan candidate is “10000”, and both evaluation values are equal to each other. In this case, the process proceeds to the thirteenth step S13.

  In the twelfth step S12, the arithmetic unit 20 uses the currently calculated component procurement plan candidate (or the one with the smallest evaluation value among the plurality of component procurement plan candidates calculated this time) as the best component procurement plan candidate. And

  In the thirteenth step S13, the arithmetic unit 20 determines whether or not the end condition is satisfied. The end condition is, for example, that the evaluation value of the best part procurement plan candidate is equal to or less than the third threshold value, or another part procurement condition obtained by changing the determined assembly plan and BOM data 130 and procurement parameters. The evaluation value of another part procurement plan candidate calculated based on the above is unlikely to be smaller than the evaluation value of the current best parts procurement plan candidate, or the calculation time for creating a part procurement plan is the fourth For example, the threshold is exceeded. If the end condition is satisfied (13th step S13: Yes), the process proceeds to an assembly plan and parts procurement plan output process (15th step S15). On the other hand, when the end condition is not satisfied (13th step S13: No), the process proceeds to the 14th step S14.

  In the fourteenth step S14, the arithmetic unit 20 changes the determined assembly plan and BOM data 130 and other parts procurement conditions obtained by changing the procurement parameters (change the parts procurement conditions used so far slightly). New parts procurement plan candidates are calculated on the basis of the parts procurement conditions obtained by (1). That is, calculation of parts procurement plan candidates is repeated.

  For example, referring to FIG. 8, a case is assumed in which a procurement parameter, which is a parameter related to lead time, is changed from a standard lead time to a lead time shorter by one day than the standard lead time. In other words, when the parts procurement conditions are changed from the parts procurement conditions using the standard lead time in each process to the parts procurement conditions using a lead time shorter by one day than the standard lead time in each process. Is assumed. In this case, the new part procurement plan candidate is the part procurement plan candidate shown in FIG.

  After execution of the 14th step S14, the process returns to the 10th step S10. In the tenth step S10, the arithmetic unit 20 calculates an evaluation value (parts procurement plan candidate evaluation value) of the new part procurement plan candidate calculated this time. Since the new component procurement plan candidate (see FIG. 20) does not include a process to be executed before today, the arithmetic unit 20 does not add “10000” to the total procurement penalty value. In other words, the evaluation value of the new parts procurement plan candidate does not include the penalty value “10000”. On the other hand, the evaluation value of the new parts procurement plan candidate includes a procurement lead time reduction penalty value (for example, 100 + 100 = 200) (see FIG. 8).

  In the eleventh step S11, the arithmetic unit 20 determines whether or not the evaluation value of the new parts procurement plan candidate calculated this time is smaller than the evaluation value of the best parts procurement plan candidate. Since the evaluation value of the new parts procurement plan candidate calculated this time is “200” and the evaluation value of the best parts procurement plan candidate is “10000”, the arithmetic unit 20 acquires the new parts procurement plan acquired this time. It is determined that the candidate evaluation value is smaller than the evaluation value of the best parts procurement plan candidate. Then, the process proceeds to the twelfth step S12. In the twelfth step S12, the arithmetic unit 20 sets the new parts procurement plan candidate acquired this time as the best parts procurement plan candidate.

  In a thirteenth step S13, it is determined whether an end condition is satisfied. Here, it is assumed that the termination condition is not satisfied. In this case, the process proceeds to the 14th step S14. In the fourteenth step S14, the arithmetic unit 20 changes the determined assembly plan and BOM data 130 and other parts procurement conditions obtained by changing the procurement parameters (change the parts procurement conditions used so far slightly). Based on the component procurement conditions obtained by the above, a further new component procurement plan candidate is calculated. That is, the calculation of parts procurement plan candidates is further repeated.

  For example, in the assembly condition for which the assembly plan candidate shown in FIG. 19 is calculated, the mode of the process E is changed from the standard procurement mode with the mode ID “J001” to the procurement mode with the mode ID “J002”. This is assumed (see FIG. 8). In other words, it is assumed that the procurement parameter, which is a parameter related to the procurement mode, is changed from the standard procurement mode to a procurement mode different from the standard procurement mode. In this case, a further new parts procurement plan candidate is a parts procurement plan candidate shown in FIG.

  After execution of the 14th step S14, the process returns to the 10th step S10. In the tenth step S10, the arithmetic unit 20 calculates an evaluation value of a further new parts procurement plan candidate calculated this time. Since the new component procurement plan candidate does not include a process to be executed before today, the computing device 20 does not add “10000” to the total procurement penalty value. In other words, the penalty value “10000” is not included in the evaluation value of the new part procurement plan candidate. On the other hand, a procurement mode penalty value (for example, “100”) is added or added to the evaluation value of a new part procurement plan candidate (see FIG. 8).

  In the eleventh step S11, the arithmetic unit 20 determines whether or not the evaluation value of the further new parts procurement plan candidate calculated this time is smaller than the evaluation value of the best parts procurement plan candidate. Since the evaluation value of the further new parts procurement plan candidate calculated this time is “100” and the evaluation value of the best parts procurement plan candidate is “200”, the arithmetic unit 20 further calculates the newer part procurement plan candidate calculated this time. It is determined that the evaluation value of the procurement plan candidate is smaller than the evaluation value of the best parts procurement plan candidate. Then, the process proceeds to the twelfth step S12. In the twelfth step S12, the arithmetic unit 20 sets the new part procurement plan candidate acquired this time as the best part procurement plan candidate.

  In a thirteenth step S13, it is determined whether an end condition is satisfied. Here, it is assumed that the termination condition is satisfied. In this case, the arithmetic unit 20 determines a parts procurement plan corresponding to the best parts procurement plan candidate as a parts procurement plan to be adopted. And it progresses to 15th step S15.

  In the fifteenth step S <b> 15, the arithmetic unit 20 transmits data indicating the determined assembly plan and data indicating the determined parts procurement plan to the output device 60. The output device 60 outputs the determined assembly plan and the determined parts procurement plan. The assembly plan includes, for example, an assembly schedule plan and / or an assembly load plan. FIG. 22 shows an example of an assembly plan output to the output device 60. In the example shown in FIG. 22, the assembly schedule plan and the assembly load plan are displayed on one screen. Alternatively, the assembly schedule plan and the assembly load plan may be displayed on separate screens. In the example shown in FIG. 22, in the assembly schedule plan, the assembly schedule of each ordered product is displayed in a Gantt chart format. Further, regarding the assembly load plan, the load acting on at least one resource (resource A in FIG. 22) is displayed in a format that can be grasped for each date. Further, a line indicating the absolute load upper limit value, a line indicating the reference load upper limit value, and the like are added to the display screen of the assembly load plan.

  FIG. 23 shows an example of a parts procurement plan output to the output device 60. In the example shown in FIG. 23, a parts procurement schedule plan, a parts procurement load plan, and a parts inventory plan are displayed on one screen. Alternatively, each of the parts procurement schedule plan, parts procurement load plan, and parts inventory plan may be displayed on a separate screen. In the example shown in FIG. 23, the parts procurement schedule is displayed in the Gantt Chart format in the parts procurement plan. In addition, regarding the parts procurement load plan, the load acting on at least one resource (resource A in FIG. 23) is displayed in a format that can be grasped for each date. In addition, a line indicating an absolute load upper limit value, a line indicating a reference load upper limit value, and the like are added to the display screen of the parts procurement load plan. Furthermore, regarding the parts inventory plan, the inventory quantity of at least one part (part A in FIG. 23) is displayed in a format that can be grasped for each date. In addition, a line indicating the safety stock number is added to the display screen of the parts inventory plan.

  In the embodiment, a production plan optimization system and a production plan optimization method for optimizing both an assembly plan for an ordered product and a procurement plan for parts necessary for the assembly of the ordered product are provided. In the production plan optimization system and the production plan optimization method in the embodiment, an assembly plan and a parts procurement plan are created in consideration of the lead time reduction penalty value or the mode penalty value. For this reason, it is possible to create an assembly plan and a parts procurement plan in which the lead time of each process or the mode of each process is set more appropriately.

(Matters that may be taken into account when calculating assembly plan candidate evaluation values)
Hereinafter, items that are arbitrarily considered when calculating the assembly plan candidate evaluation value will be described.

(Assembly resource load penalty value)
The computing device 20 has a constraint that the load acting on each assembly resource does not exceed the absolute load upper limit value (or the load acting on each assembly resource does not exceed the absolute load upper limit value and the absolute load lower limit value). The assembly plan may be determined under a constraint condition that the value is not less than the value. In other words, in the assembly plan candidate, when the load acting on each assembly resource exceeds the absolute load upper limit value (or the load acting on each assembly resource exceeds the absolute load upper limit value, or the absolute load lower limit value). When the value is lower than the value), the assembly resource load penalty value may be added to the assembly plan candidate evaluation value. In other words, the assembly resource load penalty value may be included in the total of the assembly penalty values. The assembly resource load penalty value is a large value (for example, a value larger than the above-described first threshold value used for determining whether or not the termination condition is satisfied).

  In this case, for example, the arithmetic device 20 calculates an assembly resource used in each assembly process based on the process master data 120 (data in which the product ID, the process ID, and the resource ID are associated). Next, based on the calculated assembly resource (resource ID) and resource master data 150 (data associated with the resource ID and the absolute load upper limit value or the absolute load lower limit value), the arithmetic device 20 It is determined whether the load of the assembly resource does not exceed the absolute load upper limit value, or does not fall below the absolute load lower limit value. When there is an assembly resource whose assembly resource load exceeds the absolute load upper limit value, or when there is an assembly resource whose assembly resource load falls below the absolute load lower limit value, the assembly plan candidate evaluation value includes Add the resource load penalty value.

  Alternatively, or in addition, when the load acting on each resource exceeds the reference load upper limit value, the arithmetic unit 20 adds a reference upper limit excess penalty value that is an assembly resource load penalty value to the assembly plan candidate evaluation value. May be added. That is, the reference upper limit excess penalty value may be included in the total of the assembly penalty values. The total of the reference upper limit excess penalty values is the process master data 120 (product ID, process ID, and resource ID associated with each other) and resource master data 150 (resource ID, reference load upper limit value and reference upper limit exceeded). Data associated with a penalty value). Alternatively, or in addition, when the load acting on each resource falls below the reference load lower limit value, the arithmetic unit 20 adds a reference lower limit interrupt penalty value that is an assembly resource load penalty value to the assembly plan candidate evaluation value. May be added. That is, the reference lower limit interrupt penalty value may be included in the sum of the assembly penalty values.

  FIG. 24 shows a conceptual diagram of an assembly resource load penalty value (more specifically, a reference upper limit excess penalty value or a reference lower limit interrupt penalty value). As shown in FIG. 24, when the load (resource load) acting on the resource is between the reference load lower limit value and the reference load upper limit value, the assembly resource load penalty value is zero. As the resource load deviates from the region between the reference load lower limit value and the reference load upper limit value, the total assembly resource load penalty value increases. For example, when the assembly condition is changed to shorten the assembly lead time, the load acting on the resource per day increases accordingly. As a result, the resource load is likely to exceed the reference load upper limit value. Therefore, if the assembly resource load penalty value is included in the total assembly penalty value, it can be said that there is a high possibility that an assembly plan with less reduction of the assembly lead time will be created. As a result, load leveling is realized, workers' overtime is reduced, and the operating rate of the assembly equipment is leveled.

(Resource overlap penalty value)
Alternatively or additionally, the computing device 20 may determine an assembly plan under the constraint that a plurality of operations are not allocated in the same period for resources corresponding to non-overlapping. In other words, in the assembly plan candidate, when a plurality of operations are allocated in the same period for resources corresponding to non-overlapping, the arithmetic unit 20 adds the resource duplication penalty value to the assembly plan candidate evaluation value. May be. That is, the resource duplication penalty value may be included in the total of the assembly penalty values. Note that the resource duplication penalty value is, for example, a value larger than the first threshold value used to determine whether or not the termination condition is satisfied.

(Resource load fluctuation penalty value)
Alternatively or additionally, the computing device 20 may determine the assembly plan under the constraint that the load fluctuation of the assembly resource does not exceed the fluctuation upper limit and does not fall below the fluctuation lower limit. Good. In other words, when the assembly plan candidate includes a state in which the load fluctuation of the assembly resource exceeds the fluctuation upper limit or falls below the fluctuation lower limit, the arithmetic unit 20 includes the resource load fluctuation penalty value in the assembly plan candidate evaluation value. May be added. That is, the resource load variation penalty value may be included in the total assembly penalty value. Note that the resource load fluctuation penalty value is, for example, a value larger than the first threshold value used to determine whether or not the termination condition is satisfied.

(Matters that may be taken into account when calculating candidate procurement plan candidate evaluation values)
Next, items that are arbitrarily considered when calculating the part procurement plan candidate evaluation value will be described.

(Procurement resource load penalty value)
The computing device 20 has a constraint that the load acting on each procurement resource does not exceed the absolute load upper limit value (or the load acting on each procurement resource does not exceed the absolute load upper limit value and the absolute load lower limit value). The part procurement plan may be determined under a constraint that the value is not lower than the value. In other words, in the parts procurement plan candidate, when the load acting on each procurement resource exceeds the absolute load upper limit value (or the load acting on each procurement resource exceeds the absolute load upper limit value, or the absolute load When the value is below the lower limit value), the procurement resource load penalty value may be added to the part procurement plan candidate evaluation value. In other words, the procurement resource load penalty value may be included in the total procurement penalty value. The procurement resource load penalty value is a large value (for example, a value larger than the above-described third threshold value used for determining whether or not the termination condition is satisfied).

  In this case, for example, the arithmetic device 20 calculates a procurement resource used in each component procurement process based on the process master data 125 (data in which the component ID, the process ID, and the resource ID are associated). Next, based on the calculated procurement resource (resource ID) and resource master data 150 (data in which the resource ID is associated with the absolute load upper limit value or the absolute load lower limit value), the arithmetic device 20 It is determined whether the load of the procurement resource does not exceed the absolute load upper limit value or does not fall below the absolute load lower limit value. If there is a procurement resource where the procurement resource load exceeds the absolute upper limit value, or there is a procurement resource where the procurement resource load falls below the absolute lower limit value, procurement will be included in the part procurement plan candidate evaluation value. Add the resource load penalty value.

  Alternatively or additionally, when the load acting on each resource exceeds the reference load upper limit value, the arithmetic unit 20 adds a reference upper limit excess penalty that is a procurement resource load penalty value to the component procurement plan candidate evaluation value. Values may be added. That is, the reference upper limit excess penalty value may be included in the total procurement penalty value. The total of the reference upper limit excess penalty value is the process master data 125 (data in which the component ID, process ID, and resource ID are associated) and the resource master data 150 (resource ID, reference load upper limit value and reference upper limit excess). Data associated with a penalty value). Alternatively, or in addition, when the load acting on each resource falls below the reference load lower limit value, the arithmetic unit 20 adds a reference lower limit interrupt penalty that is a procurement resource load penalty value to the component procurement plan candidate evaluation value. Values may be added. That is, the reference lower limit interrupt penalty value may be included in the total procurement penalty value.

(Resource overlap penalty value)
Alternatively or additionally, the computing device 20 may determine a parts procurement plan under the constraint that a plurality of operations are not allocated in the same period for resources corresponding to non-overlapping. . In other words, in the parts procurement plan candidate, when a plurality of operations are assigned in the same period for resources corresponding to non-overlapping, the arithmetic unit 20 sets a resource duplication penalty value as the part procurement plan candidate evaluation value. You may add. That is, the resource duplication penalty value may be included in the total procurement penalty value. Note that the resource duplication penalty value is, for example, a value larger than the above-described third threshold value used for determining whether or not the termination condition is satisfied.

(Resource load fluctuation penalty value)
Alternatively or additionally, the computing device 20 determines a parts procurement plan under the constraint that the load fluctuation of the procurement resource does not exceed the fluctuation upper limit and does not fall below the fluctuation lower limit. Also good. In other words, if the parts procurement plan candidate includes a state in which the load fluctuation of the procurement resource exceeds the upper limit of fluctuation or falls below the lower limit of fluctuation, the arithmetic unit 20 determines that the part procurement plan candidate evaluation value includes the resource load fluctuation. Penalty values may be applied. That is, the resource load variation penalty value may be included in the total of the procurement penalty values. Note that the resource load fluctuation penalty value is, for example, a value larger than the above-described third threshold value used for determining whether or not the termination condition is satisfied.

(Inventory penalty value)
The total procurement penalty value may include an inventory penalty value (eg, a safety stock interruption penalty value and / or a safety stock excess penalty value) that indicates the magnitude of the deviation of the parts inventory number from the safety inventory number. . The inventory penalty value will be described with reference to FIGS. 25 and 26. FIG.

  Referring to the BOM data 130 (FIG. 9), the types of parts necessary for assembling the order-made product (product A) are the part A with the part ID “G001” and the part C with the part ID “G003”. Be grasped. Further, it is understood that the required number of parts A is “1” and the required number of parts C is “20 = (10 + 10)”. Further, the part A is required one day before the start of the process C, ten of the parts C are required one day before the start of the process B, and ten of the parts C are started of the process C. It is understood that it is necessary one day before. This situation is shown in FIG.

  For example, it is assumed that the current inventory quantity of part A is “5” and the safety inventory quantity of part A is “3”. In this case, as shown in FIG. 25, even if one part A is allocated on the 8th day, the number of parts in stock does not fall below the number of safety stocks. Therefore, it is not necessary to procure (manufacture) the part A newly.

  On the other hand, it is assumed that the current inventory quantity of part C is “21” and the safety inventory quantity of part C is “10”. In this case, as shown in FIG. 25B, even if 10 parts C are allocated on the 6th day, the parts inventory quantity does not fall below the safety inventory quantity. However, if 10 parts C are further allocated on the 8th day, the number of parts C in stock ("1") will fall below the number of safety stocks ("10"). For this reason, it is understood that it is necessary to procure at least nine parts C on the eighth day at the latest. That is, the number of parts to be procured may be nine, ten, or twenty, but parts C need to be procured by the eighth day at the latest. is there. Note that the number of parts to be procured is preferably determined by the arithmetic unit 20 based on the “standard production quantity” of the parts master data 140 (see FIG. 10). For example, the number of procured parts is determined to be an integer multiple of the “standard production number”. Referring to FIG. 10, the “standard production number” of the part C is “20”. Therefore, in the example shown in FIG. 25B, 20 parts C are procured (that is, delivered) on the 8th day. In the example shown in (B) of FIG. 25, the excess of the inventory of the part C with respect to the safety stock number (10) is “11” from the first day to the fifth day, and from the sixth day to the seventh day. The eyes are “1”, the 8th to 12th days are “11”, and the 13th (delivery date) is “11”. For this reason, in the example shown in FIG. 25B, the total safety stock excess penalty value is calculated as “246 = (11 × 5 + 1 × 2 + 11 × 6) × safety stock excess penalty value (“ 2 ”)”. Is done.

  Referring to FIG. 8, when the part C is manufactured in the standard mode “J001”, it is understood that a resource with the resource ID “RES11” is necessary. For example, for the reason that the load of the resource with the resource ID “RES11” exceeds the absolute load upper limit value or the reason that it corresponds to the resource check date, the resource is designated as “day 7” in FIG. It is assumed that it cannot be used. In this case, the process G for manufacturing the part C may be assigned to, for example, “5th day” and “6th day”. In this case, the excess of the inventory of the part C with respect to the safety stock number (10) is “11” from the first day to the fifth day, “1” on the sixth day, “21”, “11” on the 8th to 12th days, and “11” on the 13th (delivery date). Therefore, the total of the safety stock excess penalty values in this case is calculated as “286 = (11 × 5 + 1 × 1 + 21 × 1 + 11 × 6) × safety stock excess penalty value (“ 2 ”)”.

  As described above, the total safety stock excess penalty value is calculated on the basis of the number of parts in excess of the number of parts stock with respect to the number of safety stocks for the parts required for assembly of the ordered product. The total safety stock excess penalty value is, for example, the start date of the process (part procurement process) where the part is required, the required number of the part, the number of procurement of the part and the procurement date, and the part. Is calculated based on the number of parts in stock, the safety stock number of the part, and the safety stock excess penalty value. Similarly, the sum of the safety stock interruption penalty values is calculated based on the shortage of the number of parts stock with respect to the number of safety stocks for parts required for assembly of the ordered product. The total safety stock interruption penalty value is, for example, the start date of the process (part procurement process) where the part is required, the required number of the part, the number of procurement of the part and the procurement date, and the part. The number of parts in stock, the safety stock number of the part, and the safety stock interruption penalty value are calculated.

  FIG. 26 shows a conceptual diagram of the total of the inventory penalty values (the total of the safety stock excess penalty value and the total of the safety stock interruption penalty value). As shown in FIG. 26, the closer the inventory quantity of parts is to the safety inventory quantity, the smaller the total inventory penalty value. In addition, when there is a shortage of parts, the total inventory penalty value greatly increases. For this reason, if the inventory penalty value is included in the procurement penalty value, it is easy to create a parts procurement plan with a small deviation in the number of parts inventory from the safety inventory number. As a result, the risk of excess inventory is reduced. In addition, it is easy to create a parts procurement plan with a low risk of missing parts.

  In addition to the procurement parameters related to the procurement mode or the procurement parameters related to the lead time, the parts procurement conditions include the procurement parameters related to the number of parts procured or the procurement parameters related to the parts procurement time (part delivery date). Also good. That is, the procurement parameters in the 14th step S14 described above may be changed by changing the number of parts procured or the delivery date of the parts. By changing the number of parts procured or the delivery date of parts, the total inventory penalty value (that is, the total procurement penalty value) changes. As a result, there is a possibility that the newly calculated evaluation value of the parts procurement plan candidate is improved.

(Transport cost penalty value)
Assume that parts necessary for assembling an ordered product are procured using transportation. That is, it is assumed that the parts procurement process includes a transportation process. In this case, the transportation cost varies depending on the transportation means selected. For this reason, the total of the procurement penalty values may include the transportation cost penalty value. The computing device 20 calculates a transportation cost penalty value based on the transportation master data 170 (see FIG. 13). When the transportation cost penalty value is included in the total of the procurement penalty values, there is a high possibility that a parts procurement plan with a small transportation cost is created as the parts procurement plan.

  The present invention is not limited to the above embodiment, and it is obvious that the embodiment can be appropriately modified or changed within the scope of the technical idea of the present invention.

1: Production plan optimization system 10: Input device 20: Arithmetic device 22: Assembly plan creation means 24: Parts procurement plan creation means 50: Storage device 60: Output device 70: Recording medium 110: Order data 120: Process master data 125 : Process master data 130: BOM data 140: Parts master data 150: Resource master data 160: Load fluctuation setting data 170: Transportation master data

Claims (15)

An arithmetic unit that creates an assembly plan for the ordered product and a parts procurement plan for the parts of the ordered product;
An output device for outputting the created assembly plan and the parts procurement plan;
The computing device includes a plurality of first data indicating the type of the ordered product, second data indicating the delivery date of the ordered product, and a plurality of assembly conditions obtained by changing assembly parameters. An assembly plan candidate, and an assembly plan candidate evaluation value for each of the plurality of assembly plan candidates,
The arithmetic unit determines an assembly plan candidate having the best assembly plan candidate evaluation value as the assembly plan among the plurality of assembly plan candidates.
The computing device has a plurality of parts procurement plan candidates and a plurality of parts procurement plans based on the determined parts procurement conditions obtained by changing the determined assembly plan, BOM data, and procurement parameters. Calculate candidate parts procurement plan evaluation values for each candidate,
The arithmetic device determines a component procurement plan candidate having the best component procurement plan candidate evaluation value as the component procurement plan among the plurality of component procurement plan candidates ,
The BOM data is data in which the first data, parts necessary for assembling the ordered product, and an assembly process necessary for assembling the ordered product are associated with each other.
The procurement parameter is a production plan optimization system including data related to lead times for parts procurement and the number of parts procured . The assembly plan candidate evaluation value includes a total of assembly penalty values,
The production plan optimization system according to claim 1, wherein the component procurement plan candidate evaluation value includes a total of procurement penalty values. The sum of the previous SL assembly penalty value, the arithmetic unit for the assembly process, the production plan optimized according to claim 2 including the penalty value to be added when selecting different assembly conditions from the standard assembly conditions System. The sum of the previous SL assembly penalty value, the arithmetic unit for the assembly process, the assembling mode penalty value is added in selecting the different assembling modes than the normal assembly mode, or the arithmetic unit, the assembly The production plan optimization system according to claim 2, wherein the process includes an assembly lead time reduction penalty value that is added when a lead time shorter than the standard lead time is selected. The computing device calculates assembly resources used in the assembly process,
The production plan optimization system according to claim 3 or 4, wherein the assembly plan is determined under a constraint that a load of the assembly resource does not exceed an absolute load upper limit value. The production plan optimization system according to claim 5, wherein the total of the assembly penalty values includes a reference upper limit excess penalty value that is added when a load of the assembly resource exceeds a reference load upper limit value. The production plan optimization system according to claim 5 or 6, wherein the assembly plan is determined under a constraint that a load fluctuation of the assembly resource does not exceed a fluctuation upper limit and does not fall below a fluctuation lower limit. Each of the plurality of parts procurement plan candidates includes a parts procurement process,
The total of the procurement penalty values includes a penalty value that is added when the arithmetic device selects a component procurement condition different from a standard procurement condition for the component procurement process. The production plan optimization system according to one item. Each of the plurality of parts procurement plan candidates includes a parts procurement process,
The total of the procurement penalty values includes a procurement mode penalty value that is added when the computing device selects a procurement mode different from the standard procurement mode for the component procurement process, or the computing device has the component The production plan optimization system according to any one of claims 2 to 7, wherein a procurement lead time reduction penalty value added when a lead time shorter than the standard lead time is selected for the procurement process is included. The production plan optimizing system according to claim 8 or 9, wherein the total of the procurement penalty values includes an inventory penalty value indicating a magnitude of a deviation of the number of parts in stock of the parts with respect to the number of safety stocks. The sum of the inventory penalty values includes the sum of the safety stock excess penalty value and the sum of the safety stock interruption penalty value,
The computing device calculates the total of the safety stock excess penalty value based on the number of excess of the parts inventory with respect to the safety stock for the parts, and sets the number of parts inventory to the number of shortages with respect to the safety inventory. The production plan optimization system according to claim 10, wherein a sum of the safety stock interruption penalty values is calculated based on the production plan optimization system. The procurement parameters include the number of parts procured or the delivery date of the parts,
The production plan optimization system according to claim 10 or 11, wherein changing the procurement parameter includes changing the number of parts to be procured or a delivery date of the parts. The assembly parameters include the assembly process and assembly lead time, and the assembly process is associated with an assembly resource used for assembly.
The production plan optimization system according to any one of claims 1 to 12. An arithmetic unit that creates an assembly plan, an assembly plan creation process;
The arithmetic unit, a parts procurement plan creation process for creating a parts procurement plan,
An output device comprising the created assembly plan and a plan output step for outputting the parts procurement plan;
The assembly plan creation process includes:
A plurality of assembly plan candidates based on the first data indicating the type of the ordered product, the second data indicating the delivery date of the ordered product, and the plurality of assembly conditions obtained by changing the assembly parameters; Calculating an assembly plan candidate evaluation value for each of the plurality of assembly plan candidates;
Determining an assembly plan candidate having the best assembly plan candidate evaluation value among the plurality of assembly plan candidates as the assembly plan,
The parts procurement plan creation process includes:
Based on a plurality of parts procurement conditions obtained by changing the determined assembly plan, BOM data, and procurement parameters, a plurality of parts procurement plan candidates and parts of each of the plurality of parts procurement plan candidates A process for calculating a procurement plan candidate evaluation value;
A step of determining, as the parts procurement plan, a parts procurement plan candidate having the best part procurement plan candidate evaluation value among the plurality of parts procurement plan candidates ,
The BOM data is data in which the first data, parts necessary for assembling the ordered product, and an assembly process necessary for assembling the ordered product are associated with each other.
The procurement parameter is a method for optimizing a production plan including data related to lead times for parts procurement and the number of parts procured . A step of preparing a storage device;
The storage device
Second association data in which a product ID, an assembly process ID, a standard assembly condition, and an assembly penalty value added when the assembly condition is changed from the standard assembly condition are associated;
Fourth association data in which the product ID, the component ID, and the assembly process ID are associated;
Storing the part ID, the procurement process ID, the standard procurement condition, and the third association data associated with the procurement penalty value added when the procurement condition is changed from the standard procurement condition;
The assembly plan creation step calculates the plurality of assembly plan candidates and the respective assembly plan candidate evaluation values based on the first data, the second data, and the second association data. Including
The component procurement plan creation step includes the plurality of component procurement plan candidates and each of the component procurement plan candidates based on the determined assembly plan, the third association data, and the fourth association data. The production plan optimization method according to claim 14 , further comprising calculating an evaluation value.

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