Information processing apparatus, determination method, and non-transitory computer-readable recording medium storing determination program

Abstract

The present invention causes a computer to execute the following processing: based on a result of assigning each of a plurality of objects to any one of a plurality of job devices, respectively, based on first information indicating a processing order in which the plurality of objects of a plurality of kinds are processed, and second information indicating kinds that each of the plurality of job devices can process, respectively, among the plurality of kinds, a simulation result is obtained; under a condition in which each of the plurality of objects waits while a job device that can perform processing of the object is performing processing of another object, for each of the plurality of job devices, a number N1 of objects that can perform processing of a next assigned object but have moved to another job device is counted, and a number N2 of objects that can perform processing of a next assigned object but are on standby because another object is being processed is counted; and a job device that increases or decreases the plurality of job devices is determined based on at least any one of N1 and N2.

Description

TECHNICAL FIELD

[0001] The present application relates to an information processing apparatus, a determination method, and a non-transitory computer-readable recording medium storing a determination program. BACKGROUND

[0002] Techniques have been disclosed that automatically provide information related to a work plan for a production line or the like (see, for example, Patent Documents 1 to 3).

[0003] Patent Document 1: Japanese Patent Application Publication No. 2020-047301

[0004] Patent Document 2: Japanese Patent Application Publication No. 2005-301653

[0005] Patent Document 3: Japanese Patent Application Publication No. 2015-087803

[0006] For example, consider searching for a processing order so that KPIs (Key Performance Indicator) such as work completion time, work cost, and the like satisfy desired conditions by performing simulation while changing the processing order of objects on a work line. However, there are cases in which the search result for the processing order does not satisfy the desired conditions for the KPIs. In this case, it is required to improve the KPIs by increasing or decreasing work devices. SUMMARY

[0007] On one side, an object of the present application is to provide an information processing apparatus, a determination method, and a determination program that can provide information related to increasing or decreasing work devices.

[0008] In one embodiment, the determining program causes a computer to execute the following process: obtaining first information indicating a processing order in which a plurality of objects of a plurality of kinds are processed; obtaining second information indicating kinds of processing that can be performed by each of a plurality of job devices among the plurality of kinds; assigning each of the plurality of objects to any one of the plurality of job devices based on the first information and the second information; obtaining a simulation result related to processing by the plurality of job devices based on the result of the assignment of each of the plurality of objects; counting, for each of the plurality of job devices, the number Nl of objects that have moved to another job device although processing of the next assigned object can be performed but are on standby because another object is being processed, and counting, for each of the plurality of job devices, the number N2 of objects that are on standby although processing of the next assigned object can be performed but are on standby because another object is being processed, according to the simulation result, under the condition that each of the plurality of objects is on standby while the job device that can perform processing of the object is performing processing of another object; and determining a job device to be added or removed from the plurality of job devices based on at least any one of the number Nl and the number N2.

[0009] Information related to addition or removal of a job device can be provided. BRIEF DESCRIPTION OF DRAWINGS

[0010] Figure 1 (a) ~ Figure 1 (c) is a diagram for explaining an outline of a production line.

[0011] Figure 2 (a) ~ Figure 2 (c) is a diagram for explaining an outline of a production line.

[0012] Figure 3 is a diagram illustrating a production line model.

[0013] Figure 4 (a) is a functional block diagram showing the overall configuration of an information processing device, Figure 4 (b) is a block diagram illustrating the hardware configuration of each part of the information processing device.

[0014] Figure 5 is a diagram illustrating the relationship between products for which each manufacturing device can perform a manufacturing process and the process time of the manufacturing process.

[0015] Figure 6 is a flowchart illustrating an optimization process.

[0016] Figure 7 (a) ~ Figure 7 (d) is a diagram illustrating counting of the number Nl of products and the number N2 of products.

[0017] Figure 8 (a) is a graph illustrating the result of step S3. Figure 8 (b) is a graph illustrating the result of step S3.

[0018] Figure 9 (a) ~ Figure 9 (e) is a graph illustrating another way of step S9. DETAILED DESCRIPTION

[0019] First, as one example of a production line, a summary of the production line will be described. Figure 1 (a) ~ Figure 1 (c) is a graph for explaining a summary of the production line. Figure 1 (a) is a graph illustrating a production line of a unit production type. Figure 1 (b) is a graph illustrating a production line of a flow production type. As Figure 1 (a) or Figure 1 (b) illustrates, a product (raw material) is put into the inlet. Each product becomes a finished product by passing through each manufacturing process in the middle of the production line. In Figure 1 (a) or Figure 1 (b), as one example, a turning process, a hole opening process, and a planing process are implemented.

[0020] In recent years, multi-variety small-lot production is performed. Therefore, the manufacturing process implemented differs for each product number (variety of product). For example, as illustrated in Figure 1 (c), for product #1, a turning process is implemented first, and a planing process is implemented second to be finished. For product #2, a hole opening process is implemented first, and a planing process is implemented second. Also, even in the hole opening process, there are cases where the contents of the hole opening differ according to the variety of product. In this way, in multi-variety small-lot production, the manufacturing process is complicated. Therefore, it is considered that the production line is made efficient by arranging a plurality of manufacturing devices capable of implementing each manufacturing process.

[0021] For example, as illustrated in Figure 2 (a), the order of putting in each product is specified. In Figure 2 (a), the order of putting in is specified from top to bottom. As illustrated in Figure 2 (b), each product that flows to the front end of the standby area is assigned to each manufacturing device. The assignment destination of each product is a manufacturing device capable of implementing a necessary manufacturing process for the product at the front end. As illustrated in Figure 2 (c), the product for which each manufacturing device is capable of implementing a necessary manufacturing process, and the process time necessary for the manufacturing process of the product are specified. The product that can be manufactured differs for each manufacturing device.

[0022] For example, manufacturing device a is able to implement manufacturing processes of product #1 and product #3, but is not able to implement manufacturing of product #2 and product #4. Manufacturing device b is able to implement manufacturing processes of product #2, but is not able to implement manufacturing of products #1, #3, #4. Manufacturing device c is able to implement manufacturing processes of any one of products #1 to #4.

[0023] If manufacturing processes of other products are being implemented in any one of the manufacturing devices that are able to implement the necessary manufacturing processes for the product at the front end of the standby area, the product stands by at the front end of the standby area. In the case where the other product moves from the manufacturing device, the product that is standing by at the front end moves to the manufacturing device. Further, in the case where there are a plurality of manufacturing devices that are able to be allocated from the standby area, the allocation destination is selected in accordance with a prescribed rule. For example, a manufacturing device with a shorter process time, a manufacturing device with lower manufacturing cost, and the like are selected. Further, in the standby area, products are sequentially allocated to each manufacturing device from the product at the front end, so if the product at the front end stands by, other products also stand by.

[0024] Further, if a general-purpose device that is able to implement a larger number of product types of manufacturing processes is used, the operating efficiency of each manufacturing device improves, and the standby time of each product becomes shorter. However, general-purpose devices have a tendency to be expensive and have longer manufacturing process times. The manufacturing process becomes longer, for example, because of the occurrence of setting change work (changeover preparation work) corresponding to product types, and the like. On the other hand, a special-purpose device that is able to implement a smaller number of product types of manufacturing processes has a tendency to be inexpensive and have a shorter manufacturing process time, but is not able to implement manufacturing processes for other product types. In this way, general-purpose devices and special-purpose devices each have advantages and disadvantages, so there is a tendency for general-purpose devices and special-purpose devices to be mixed on a production line.

[0025] If the standby time for each product becomes longer, the time required for manufacturing of all products to be completed (production preparation time) becomes longer. Alternatively, depending on the installation cost of each manufacturing device, the operating time of each manufacturing device, and the like, the manufacturing cost varies. Therefore, a plurality of KPIs are required to satisfy prescribed conditions. In addition to the production preparation time and the manufacturing cost, the number of changeover preparations for each manufacturing device, the delivery delay time for each product, the number of delays for each product, and the like can be cited as the plurality of KPIs.

[0026] Therefore, it is considered that the input order of products to the production line model is exchanged while simulation is performed using a production line simulator or the like, and the input order is optimized so that the KPIs satisfy prescribed conditions. The production line simulator performs simple model calculations that divide the production line model into small units, make a product flow to a unit if the previous unit is idle, and the like. Figure 3is a diagram illustrating a production line model. Products input to the production line model stay in units corresponding to each manufacturing device for a prescribed process time, and then move to the next unit. In the standby area, a prescribed number of units are provided, for example. Thus, in the standby area, the prescribed number of products can stand by. However, in the standby area, products that have arrived at the standby area stand by in order from the front end. The production line simulator performs simulation from the start of inputting each product to the unit at the start according to the input order at prescribed time intervals until all products arrive at the end.

[0027] By using the production line simulator, it is possible to optimize the input order with KPIs such as production preparation time and manufacturing cost as objective functions so that the objective functions determined according to the input order satisfy prescribed conditions. The objective functions can be one or two or more. In the case where the objective functions are one, single-objective optimization is performed. In the case where the objective functions are two or more, multi-objective optimization is performed. From the obtained results, an input plan that satisfies desired conditions is adopted. However, in the case where there are no results that satisfy desired conditions of KPIs, it is necessary to adjust the types and numbers of manufacturing devices. At this time, it is necessary to increase or decrease the manufacturing devices.

[0028] Hereinafter, an information processing device, a determination method, and a determination program that can provide information for increasing or decreasing manufacturing devices to obtain desired KPIs will be described.

[0029] Embodiment 1

[0030] Figure 4 (a) is a functional block diagram showing the overall configuration of the information processing device 100 of Embodiment 1. The information processing device 100 is a server or the like for optimization processing. As shown in (a), Figure 4 As illustrated in (a), the information processing device 100 includes a production line model storage section 10, a manufacturing master data storage section 20, an input order storage section 30, an operation result storage section 40, an acquisition section 50, an optimization execution section 60, a count section 70, a determination section 80, and a result output section 90.

[0031] Figure 4 (b) is a block diagram illustrating the hardware configuration of each section of the information processing device 100. As shown in (b), Figure 4 As illustrated in (b), the information processing device 100 includes a CPU 101, a RAM 102, a storage device 103, an input device 104, and a display device 105.

[0032] CPU (Central Processing Unit) 101 is a central processing unit. CPU 101 includes one or more cores. RAM (Random Access Memory) 102 is a volatile memory that temporarily stores programs executed by CPU 101, data processed by CPU 101, etc. Storage device 103 is a non-volatile storage device. As storage device 103, for example, ROM (Read Only Memory), solid state disks (SSDs) such as flash memory, hard disks driven by hard disk drives, etc. can be used. Storage device 103 stores the determination program of this embodiment. Input device 104 is an input device such as a mouse and keyboard. Display device 105 is a display device such as a liquid crystal display. Display device 105 displays the result output by result output unit 90. By executing the determination program by CPU 101, it is realized Figure 4 (a) In addition, dedicated circuits and other hardware may be used as Figure 4 (a) The parts.

[0033] The production line model storage unit 10 stores Figure 3 The production line model shown above.

[0034] The manufacturing master data storage unit 20 stores manufacturing master data that associates each product type with a manufacturing device capable of performing a manufacturing process for each product type. Figure 5 This is a diagram illustrating manufacturing master data. Figure 5 In the example, for example, manufacturing device a can perform the required manufacturing process on product #1, product #3, and product #4, but cannot perform the required manufacturing process on product #2 and product #5.

[0035] The input sequence storage unit 30 stores Figure 2 (a) The initial order of betting can be the order in which orders are received from customers, for example, and can be pre-entered by the user using input device 104. Alternatively, the initial order of betting can be generated using random numbers. Since the initial order of betting is generated without considering the objective function, it is often the case that any objective function does not reach a good value.

[0036] The following, according to Figure 6 The flow chart of the optimization process is used to explain the optimization process. First, the acquisition unit 50 acquires the information required for the optimization calculation (step S1). The information required for the optimization calculation includes the production line model stored in the production line model storage unit 10. In addition, the information required for the optimization calculation includes Figure 5 Furthermore, the information required for the optimization calculation includes the initial input sequence stored in the input sequence storage unit 30 .

[0037] Next, the optimization execution section 60 performs optimization calculation using the information acquired by the acquisition section 50 in step S1 (step S2). The optimization calculation here simulates the production line according to the input order, and acquires the production preparation time and the manufacturing cost from the start of product input to the start point to the arrival of all products to the end point as an objective function. The input order is optimized by an evolutionary algorithm such as a genetic algorithm (GA) so that the specified objective function becomes good.

[0038] Next, the counting section 70 counts, for each manufacturing device, the number N1 of products that have moved to other manufacturing devices although the manufacturing process is not being performed and the manufacturing process can be performed on the product in the case where the product has arrived at the forefront of the standby area. In addition, the counting section 70 counts, for each manufacturing device, the number N2 of products that are standing by because the manufacturing process is being performed on other products although the manufacturing process can be performed on the product in the case where the product has arrived at the forefront of the standby area (step S3). Furthermore, standing by here means stopping at the forefront of the standby area for a prescribed time (0 ≥ 0) or more.

[0039] For example, as illustrated in Figure 7 (a), it is assumed that, in the standby area, products are standing by in the order of product #3, product #2, and product #1 from the forefront toward the end. In this state, as illustrated in Figure 7 (b), it is assumed that the manufacturing process of product #5 is being performed in the manufacturing device b, and the manufacturing process of product #4 is being performed in the manufacturing device c. In this case, from the manufacturing master data of Figure 5 , product #3 can be assigned to the manufacturing device a or the manufacturing device d. However, the process time in the manufacturing device d is longer than the process time in the manufacturing device a, so as illustrated in Figure 7 (b), it is assumed that product #3 moves to the manufacturing device a.

[0040] In this case, the manufacturing device d becomes a state in which the product moves to other manufacturing devices although the manufacturing process is not being performed and the manufacturing process can be performed on the product in the case where the product has arrived at the forefront of the standby area. Therefore, as illustrated in Figure 7 (c), the number N1 of products of the manufacturing device d is accumulated by one.

[0041] Since product #3 moves to the manufacturing device a, as illustrated in Figure 7 (a), product #2 moves to the forefront of the standby area. In this state, since the manufacturing device b and the manufacturing device c are performing the manufacturing process on other products, from the manufacturing master data of Figure 5Looking at the manufacturing master data of Product #2, Product #2 is on standby in the standby area.

[0042] In this case, the manufacturing devices b, c make the product on standby although they can implement the manufacturing process to the product since the manufacturing process is being implemented to other products in the case where the product reaches the front end of the standby area. Therefore, as shown in Figure 7 (d), the product number N2 of the manufacturing devices b, c is accumulated one.

[0043] Figure 8 (a) and Figure 8 (b) are charts showing the results of step S3. In Figure 8 (a), the product number Nl is counted for each manufacturing device. In Figure 8 (b), the product number N2 is counted for each manufacturing device. Further, the product number Nl and the product number N2 are cumulative values of all input sequences simulated in the optimization of step S2. For example, in the case where the input sequence is changed ninety-nine times from the initial input sequence, the counted values of the product number Nl and the product number N2 are cumulative values of the product number Nl and the product number N2 of the simulation results of one hundred times. The counted values of the product number Nl and the product number N2 are stored in the operation result storage section 40.

[0044] Next, the determination section 80 determines whether the KPI of the production line simulation result under the optimal input sequence obtained by the execution of step S2 satisfies a prescribed condition (step S4). In the case where step S4 is determined as "Yes", the desired KPI is obtained, so that the addition or deletion of the manufacturing device is not needed. Therefore, in the case where step S4 is determined as "Yes", the result output section 90 causes the results of step S2, the results of step S3, and the like to be displayed on the display device 105 (step S5). Thereafter, the execution of the flowchart ends.

[0045] In the case where step S4 is determined as "No", the determination section 80 sets a threshold value for each of the product number Nl and the product number N2 (step S6). Next, the determination section 80 determines whether the product number Nl of each manufacturing device is equal to or greater than the threshold value set in step S6 (step S7). In the case where there is a manufacturing device determined as "Yes" in step S7, the determination section 80 deletes the manufacturing device from the production line model as shown in Figure 8 (a) (step S8). The production line model storage section 10 stores the updated production line model. In step S8, the manufacturing device to be deleted can also be displayed on the display device 105 by the result output section 90, so that the user deletes the manufacturing device from the production line model.

[0046] Next, the determination section 80 determines whether the product number N2 is above the threshold value set in step S6 for each manufacturing device (step S9). In the case of a manufacturing device determined to be "Yes" in step S9, the determination section 80 causes the manufacturing device to be added to the production line model as in Figure 8 (b). The production line model storage section 10 stores the updated production line model. Thereafter, the process is performed again from step S1. In step S10, the manufacturing device to be added can be displayed on the display device 105 by the result output section 90, so that the user adds the manufacturing device to the production line model.

[0047] Further, in the case of "No" determined in step S7, step S8 is not performed and step S9 is performed. In the case of "No" determined in step S9, step S10 is not performed and the process is performed again from step S1.

[0048] According to the present embodiment, the manufacturing device that should be added or deleted can be determined by counting the product number Nl and the product number N2. For example, by counting the product number Nl, the number of times that the product is not assigned can be counted, so the product number Nl can be used as an index of necessity of each manufacturing device. Further, by counting the product number N2, the number of times that the product is on standby can be counted, so the product number N2 can be used as an index of necessity of each manufacturing device. By comparing the product number Nl with the threshold value, the manufacturing device that should be deleted can be determined. Further, by comparing the product number N2 with the threshold value, the manufacturing device that should be added can be determined.

[0049] using Figure 9 (a) ~ Figure 9 (e), the other mode of step S9 of Figure 6 will be described. It is assumed that the manufacturing master data storage section 20 stores Figure 9 (a). In the example of Figure 9 (a), the manufacturing device a is a dedicated device that can perform only the necessary manufacturing process on the product #4. Further, the manufacturing device b is a dedicated device that can perform only the necessary manufacturing process on the product #3. On the other hand, the manufacturing device c is a general-purpose device that can perform the necessary manufacturing process on the product #2, the product #4, and the product #5. The manufacturing devices d and e are also general-purpose devices like the manufacturing device c.

[0050] It is assumed that the product number N2 is obtained as in Figure 9 (b) as a result of step S3. Further, it is assumed that the number of times that each product of each kind is on standby at the front end of the standby area is obtained as in Figure 9 (c) as a result of step S3. Figure 9 (b) andFigure 9 The result of (c) is a cumulative value for all input sequences simulated in the optimization process of step S2.

[0051] The additional manufacturing device selects a manufacturing device whose number of products N2 is greater than a threshold value (e.g., average value). In Figure 9 In the example of (b), manufacturing devices b, c, and e are candidates. From among these manufacturing devices, a manufacturing device that can perform manufacturing processes for all products is selected. Figure 9 (c) is a manufacturing device that performs manufacturing processes for all products whose number of idle waits is greater than a threshold value (e.g., average value). The selected manufacturing device is deleted in step S8.

[0052] However, it is preferable to take into account the cost increase due to the additional manufacturing device. Figure 9 (d) is a graph that illustrates the fixed cost required in the case of adding a manufacturing device. It is preferable to determine the number of additional manufacturing devices within a range that satisfies a desired condition for the cost. In the case of limiting the number of products to be manufactured to a certain number, the number of additional manufacturing devices is determined within a range that satisfies the desired condition for the cost. Figure 9 In the case where the allowable cost (e.g., 5000) illustrated in (e) is exceeded, and a manufacturing device that can perform manufacturing processes for all products (#3, #4, #5) whose number of idle waits is greater than the average cannot be selected, a manufacturing device that can perform manufacturing processes for a product whose number of idle waits is greater is selected preferentially (in order from the greatest to the least: #4, #3, #5). In the case of expressing this by a formula, first, candidates for the number of additional manufacturing devices for each manufacturing device are listed within a range that is limited to the allowable cost. For example, (manufacturing device b, manufacturing device c, manufacturing device d) = (1, 1, 0), (0, 1, 1),... are listed. In each of these candidates, the number of additional manufacturing devices that can perform manufacturing processes for each product (#1, #2, #3, #4, #5) = (0, 1, 1, 1, 1), (1, 2, 1, 1, 1),... = (xl, x2, x3, x4, x5) is calculated. The type and number of manufacturing devices that maximize the formula (n1xl+ n2x2+ n3x3+ n4x4+ n5x5) that takes the number of idle waits for each product as a coefficient for the number of additional manufacturing devices are selected as the additional manufacturing devices for reducing the production preparation time. In this way, by multiplying the number of idle waits for each product as a coefficient, a manufacturing device that can perform manufacturing for a product whose number of idle waits is greater preferentially can be added.

[0053] In the above example, products #1 to #5 are one example of a plurality of objects that include a plurality of types. The input sequence of products to the production line is one example of a processing sequence in which a plurality of objects that include a plurality of types are processed. The manufacturing device is one example of a work device.

[0054] The acquisition unit 50 is an example of an acquisition unit that acquires first information indicating a processing order in which a plurality of objects of a plurality of kinds are processed and second information indicating kinds in which a plurality of job devices can respectively perform processing among the plurality of kinds. The counting unit 70 is an example of a counting unit that obtains a simulation result related to processing of the plurality of job devices based on a result of assigning the plurality of objects to any one of the plurality of job devices respectively according to the first information and the second information, and counts, for each of the plurality of job devices, the number Nl of objects that can perform processing on a next assigned object but have moved to another job device and the number N2 of objects that can perform processing on a next assigned object but are on standby because another object is being processed according to the simulation result under a condition in which each of the plurality of objects waits while the job device that can perform processing on the object is performing processing on another object. The determination unit 80 is an example of a determination unit that determines a job device to be added or removed to or from the plurality of job devices according to at least any one of the number Nl and the number N2 of objects.

[0055] The embodiments of the present application have been described above, but the present application is not limited to such specific embodiments and various modifications and changes can be made within the scope of the present application as set forth in the claims.

[0056] Explanation of Reference Numerals

[0057] 10…production line model storage unit, 20…manufacturing master data storage unit, 30…input order storage unit, 40…calculation result storage unit, 50…acquisition unit, 60…optimization execution unit, 70…counting unit, 80…determination unit, 90…result output unit, 100…information processing device, 101…CPU, 102…RAM, 103…storage device, 104…input device, 105…display device.

Claims

1. A non-transitory computer-readable recording medium storing a determination program, characterized in that: Make the computer execute the following processing: obtaining first information indicating a processing order for processing a plurality of objects including a plurality of categories; obtaining second information indicating the types that can be processed by the plurality of working devices, respectively, from the plurality of types; Based on the first information and the second information, each of the plurality of objects is assigned to any one of the plurality of working devices; obtaining simulation results related to processing of the plurality of working devices based on the results of allocating the plurality of objects; Under the condition that each of the plurality of objects is on standby while the working device capable of processing the object is processing another object, based on the simulation result, for each of the plurality of working devices, the number N1 of objects that can process the next assigned object but have moved to another working device is counted, and the number N2 of objects that can process the next assigned object but are on standby because they are processing another object is counted; and Determine the working device to be added or reduced among the plurality of working devices based on at least one of the number of objects N1 and the number of objects N2; The above simulation results are simulation results from the time when the product is fed into the starting point until all products arrive at the end point, in which the production line is simulated according to the feeding order.

2. The non-transitory computer-readable recording medium according to claim 1, wherein The computer is caused to execute the following processing: Based on the number of objects N1, the work device to be deleted from the plurality of work devices is determined.

3. The non-transitory computer-readable recording medium according to claim 1 or 2, wherein: The computer is caused to execute the following processing: Based on the number of objects N2, the working device to be added to the plurality of working devices is determined.

4. The non-transitory computer-readable recording medium according to claim 3, wherein When determining the working device to be added to the plurality of working devices, the number of waiting times for each of the plurality of types is taken into consideration in addition to the number of objects N2.

5. The non-transitory computer-readable recording medium according to claim 3 or 4, wherein: When determining the working device to be added to the plurality of working devices, in addition to the number of objects N2, the additional cost of the working device to be added is also considered.

6. The non-transitory computer-readable recording medium according to any one of claims 1 to 5, wherein: The above-mentioned multiple types of processing orders are sequentially searched so that the objective function determined by the processing order becomes good.

7. The non-transitory computer-readable recording medium according to any one of claims 1 to 6, wherein: When searching for the plurality of types of processing orders, an evolutionary algorithm for optimizing the objective function is used.

8. The non-transitory computer-readable recording medium according to any one of claims 1 to 7, wherein: When there are a plurality of working devices capable of processing the next assigned object and the plurality of working devices are not performing work on other objects, the assignment destination is selected based on a predetermined rule.

9. A determination method, characterized in that: The computer performs the following processing: obtaining first information indicating a processing order for processing a plurality of objects including a plurality of categories; obtaining second information indicating the types that can be processed by the plurality of working devices, respectively, from the plurality of types; Based on the first information and the second information, each of the plurality of objects is assigned to any one of the plurality of working devices; obtaining simulation results related to processing of the plurality of working devices based on the results of allocating the plurality of objects; Under the condition that each of the plurality of objects is on standby while the working device capable of processing the object is processing another object, based on the simulation result, for each of the plurality of working devices, the number N1 of objects that can process the next assigned object but have moved to another working device is counted, and the number N2 of objects that can process the next assigned object but are on standby because they are processing another object is counted; and Determine the working device to be added or reduced among the plurality of working devices based on at least one of the number of objects N1 and the number of objects N2; The above simulation results are simulation results from the time when the product is fed into the starting point until all products arrive at the end point, in which the production line is simulated according to the feeding order.

10. The determination method according to claim 9, characterized in that: The above computer performs the following processing: Based on the number of objects N1, the work device to be deleted from the plurality of work devices is determined.

11. The determination method according to claim 9 or 10, characterized in that: The above computer performs the following processing: Based on the number of objects N2, the working device to be added to the plurality of working devices is determined.

12. The determination method according to claim 11, characterized in that: When determining the working device to be added to the plurality of working devices, the number of waiting times for each of the plurality of types is taken into consideration in addition to the number of objects N2.

13. The determination method according to claim 11 or 12, characterized in that: When determining the working device to be added to the plurality of working devices, in addition to the number of objects N2, the additional cost of the working device to be added is also considered.

14. The determination method according to any one of claims 9 to 13, characterized in that: The above-mentioned multiple types of processing orders are sequentially searched so that the objective function determined by the processing order becomes good.

15. The determination method according to any one of claims 9 to 14, characterized in that: When searching for the plurality of types of processing orders, an evolutionary algorithm for optimizing the objective function is used.

16. The determination method according to any one of claims 9 to 15, characterized in that: When there are a plurality of working devices capable of processing the next assigned object and the plurality of working devices are not performing work on other objects, the assignment destination is selected based on a predetermined rule.

17. An information processing device, characterized in that: have: an acquiring unit configured to acquire first information indicating a processing order for processing a plurality of objects of a plurality of categories, and second information indicating categories that can be processed by the plurality of working devices, respectively, among the plurality of categories; a counting unit that obtains simulation results related to processing by the plurality of working devices based on a result of assigning the plurality of objects to any one of the plurality of working devices according to the first information and the second information, and counts, for each of the plurality of working devices, a number N1 of objects that can be processed by the next assigned object but have moved to another working device, and counts a number N2 of objects that can be processed by the next assigned object but are on standby because they are currently processing another object, under the condition that each of the plurality of objects is on standby while the working device that can process the object is currently processing another object, based on the simulation results; and The determining unit determines the working device to be added or reduced among the plurality of working devices based on at least one of the number of objects N1 and the number of objects N2. The above simulation results are simulation results from the time when the product is fed into the starting point until all products arrive at the end point, in which the production line is simulated according to the feeding order.

18. The information processing device according to claim 17, wherein: The specifying unit specifies a work device to be deleted from the plurality of work devices based on the number of objects N1.

19. The information processing device according to claim 17 or 18, characterized in that The specifying unit specifies a working device to be added to the plurality of working devices based on the number of objects N2.

20. The information processing device according to claim 19, wherein When specifying a working device to be added to the plurality of working devices, the specifying unit may consider the number of waiting times for each of the plurality of types in addition to the number of objects N2.

21. The information processing device according to claim 19 or 20, characterized in that When determining the working device to be added to the plurality of working devices, the specifying unit considers the additional cost of the working device to be added in addition to the number of objects N2.

22. The information processing device according to any one of claims 17 to 21, wherein: The above-mentioned multiple types of processing orders are sequentially searched so that the objective function determined by the processing order becomes good.

23. The information processing device according to any one of claims 17 to 22, wherein: The determination unit uses an evolutionary algorithm that optimizes the objective function when searching for the plurality of types of processing orders.

24. The information processing device according to any one of claims 17 to 23, wherein: When there are a plurality of working devices capable of processing the next assigned object and the plurality of working devices are not performing work on other objects, the assignment destination is selected based on a predetermined rule.

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