Planning program, planning method, and information processing device

Abstract

To provide a planning program, a planning method, and an information processing apparatus capable of planning a feeding order to a work line while suppressing man-hours of a user.SOLUTION: A planning program that causes a computer to execute processing of: generating a plurality of priority order patterns with priority orders assigned thereto, for a plurality of work groups, when processes selected from the plurality of work groups are sequentially performed on each of a plurality of objects; specifying the order of processes in the work group for each of the priority orders according to the priority orders of the plurality of priority order patterns; and specifying the order of the plurality of objects corresponding to a combination of the specified processes.SELECTED DRAWING: Figure 6

Description

Technical Field

[0001] This case relates to a filing program, a filing method, and an information processing apparatus.

Background Art

[0002] The formulation of a process plan for sequentially performing operations on multiple types of objects has been carried out (for example, see Patent Documents 1 to 3).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Patent Document 3

Summary of the Invention

Problems to be Solved by the Invention

[0004] For example, a plurality of products may be sequentially input in a predetermined order on a single production line, and operations respectively selected from a plurality of work groups may be performed on each product. In this case, in order to equalize the frequency of each operation, it is conceivable to equalize the work pattern using the target tracking method. However, in this case, there is a risk of local concentration for each operation. Therefore, a method of specifying the processing order on the production line (also referred to as the input order to the production line) by introducing a weight coefficient for each operation is conceivable. However, the weight coefficient and the output result may not match the user's assumption, and the user's man-hour tends to increase.

[0005] On one aspect, an object of this case is to provide a filing program, a filing method, and an information processing apparatus that can formulate the input order to the production line while suppressing the user's man-hour. [Means for solving the problem]

[0006] In one embodiment, the planning program causes the computer to perform the following processes: generate multiple priority patterns, each assigning priority to a plurality of work groups, under the condition that a process selected from a plurality of work groups is performed sequentially for each of the plurality of objects; and, according to the priority of the plurality of priority patterns, identify the order of the processes within the work group for each priority, and identify the order of the plurality of objects corresponding to each combination of identified processes. [Effects of the Invention]

[0007] This invention provides a planning program, a planning method, and an information processing device that can plan the order in which materials are input to a work line while reducing the user's workload. [Brief explanation of the drawing]

[0008] [Figure 1] This diagram illustrates the overview of multi-product mixed-model production. [Figure 2] This diagram illustrates the work sequence in a multi-product mixed-flow production system. [Figure 3] (a) and (b) are diagrams illustrating the order in which items should be added to the work line. [Figure 4] (a) to (c) are diagrams illustrating the goal tracking method. [Figure 5] This is a diagram to explain the principle. [Figure 6] This diagram illustrates a specific explanation for identifying solutions using priority patterns. [Figure 7] (a) is a block diagram illustrating the overall configuration of the information processing device 0 according to Embodiment 1, and (b) is a block diagram illustrating the hardware configuration of the information processing device. [Figure 8] This flowchart illustrates an example of the planning and design process performed by an information processing system. [Figure 9]It is a flowchart showing an example of the planning process executed by the information processing apparatus. [Figure 10] It is a diagram illustrating the content of prod2cond_map. [Figure 11] It is a diagram illustrating the content of cond_group. [Figure 12] It is a diagram illustrating a priority pattern. [Figure 13] (a) and (b) are diagrams for explaining other examples of the generation method of the priority pattern. [Figure 14] It is a diagram for explaining an example in which the result output by the output unit is output to the traveling machine and the input device. [[ID=1⑧]]

Mode for Carrying Out the Invention

[0009] Prior to the description of the embodiments, an overview of mixed production of multiple product types will be described. <°

[0010] In the warehouse, the inventory of various parts is stored in a vast space. In such a warehouse, each part for producing multiple types of products is stored. Each parts case containing multiple of the same part is stored in a designated parts shelf. In the warehouse, the parts necessary for the work in the factory are picked.

[0011] In these parts picking operations, there are cases where the operator collects from the parts cases on each parts shelf, or cases where the traveling machine (automated guided vehicle: AGV) automatically collects. For example, as illustrated in FIG. 1, the traveling machine 201 travels to the parts shelf where the parts designated for collection are stored and picks the parts. For each product, one or more parts are picked. At least a part of the necessary parts is different for each product type.

[0012] For each product type, a set of necessary parts is sequentially fed into the factory's production line. As a result, the work for producing each product is sequentially carried out in the factory. For example, for each set of each product type picked by the traveling machine 201, the feeding device 202 sequentially feeds them into the production line according to the specified feeding order.

[0013] Figure 2 is a diagram illustrating the working sequence in the mixed production of multiple product types. In the mixed production of multiple product types, multiple types of products with different working patterns are sequentially fed into a single production line in a predetermined order and move along the production line in sequence. As an example, assume that work is carried out on multiple products with different working patterns, namely product A to product C. Different work is carried out for each of these products. For example, as illustrated in Figure 2, for each product, after any one of the operations A1, A2, and A3 included in work group A is carried out, any one of the operations B1 and B2 included in work group B is carried out.

[0014] As illustrated in Figure 3(a), for each product, at least part of the necessary work is different. On the other hand, part of the necessary work overlaps among multiple products. For example, for product A, operations A1 and B1 are carried out in sequence. For product I, operations A2 and B2 are carried out in sequence. For product U, operations A3 and B1 are carried out in sequence. For product E, operations A1 and B2 are carried out in sequence. For product O, operations A2 and B1 are carried out in sequence. For product C, operations A3 and B2 are carried out in sequence.

[0015] In the example of Figure 3(a), they are fed into the production line in the order of product A, product I, product U, product E, product O, and product C. In this feeding order, the same operation is not carried out for two consecutive products. Therefore, since the same operation is not concentrated, the supply frequency of each part is dispersed without being concentrated. Also, if the same operation does not need to be repeated, the work efficiency of the workers will not decrease.

[0016] In contrast, in Figure 3(b), products A, D, E, C, B, and F are fed into the work line in that order. In this feeding order, task A1, performed on product A, is then performed on product D as well. Similarly, task B1, performed on product E, is then performed on product C as well. In this case, since the same task is performed consecutively on two products, the supply frequency of each component becomes uneven. Furthermore, because the same task needs to be repeated, there is a risk that the work efficiency of the workers will decrease.

[0017] Therefore, it is desirable to eliminate the congestion of tasks and level out the frequency of tasks. For example, the goal tracking method can be used to suppress the bias of the same task patterns. The goal tracking method is a technique that selects task patterns with a high probability of occurrence for each product, index by index (input count).

[0018] Figures 4(a) to 4(c) illustrate the objective tracking method. As an example, four products are entered into the production line under work pattern A1B1, where tasks A1 and B1 are performed. Two products are entered into the production line under work pattern A1B2, where tasks A1 and B2 are performed. One product is entered into the production line under work pattern A2B1, where tasks A2 and B1 are performed. One product is entered into the production line under work pattern A2B2, where tasks A2 and B2 are performed. In this objective tracking method, the overlap of tasks between products is not considered, and the solution for the input order is found so that the same work pattern is leveled out.

[0019] As illustrated in Figure 4(a), first, the base probability of occurrence is calculated for each work pattern. The base probability of occurrence is the ratio of the number of products for each work pattern to the total number of products. Since the total number of products is 8, the base probability for work pattern A1B1 is 4 / 8 = 1 / 2. The base probability for work pattern A1B2 is 2 / 8 = 1 / 4. The base probability for work pattern A2B1 is 1 / 8. The base probability for work pattern A2B2 is 1 / 8.

[0020] Next, the probability of each work pattern occurring is calculated for each input. Input = 1 represents the operation of inputting the first product into the work line. Input = n represents the operation of inputting the nth product into the work line. The probability of occurrence is calculated as Input = n × Base value - (Number of times that work pattern has been selected up to that point).

[0021] As illustrated in Figure 4(b), with input count = 1, the probability of work pattern A1B1 occurring is 0.5 (base value). The probability of work pattern A1B2 occurring is 0.25 (base value). The probability of work pattern A2B1 occurring is 0.125 (base value). The probability of work pattern A2B2 occurring is 0.125 (base value). In the target tracking method, the work pattern with the highest probability of occurrence is selected, so the product of work pattern A1B1 is selected as the first input product. At this point, the number of times the product of work pattern A1B1 has been selected is 1, and the number of times the products of work patterns A1B2, A2B1, and A2B2 have been selected is 0.

[0022] With input count = 2, the probability of work pattern A1B1 occurring is 0. The probability of work pattern A1B2 occurring is 0.5. The probability of work pattern A2B1 occurring is 0.25. The probability of work pattern A2B2 occurring is 0.25. Therefore, the product of work pattern A1B2 is selected as the second input product. At this point, the number of times products of work patterns A1B1 and A1B2 have been selected is 1, and the number of times products of work patterns A2B1 and A2B2 have been selected is 0.

[0023] If we determine the order in which each product is fed into the work line using a similar procedure, for example, as illustrated in Figure 4(c), the output will be work pattern A1B1, work pattern A1B2, work pattern A1B1, work pattern A2B2, and work pattern A2B1. In this order, the frequency of each task is leveled to some extent, but in feeding cycles 1 to 3, task A1 is performed three times in a row. The reason why the same task can be selected consecutively is that the same task is included in multiple work patterns. Furthermore, for work patterns of product types with a large number of units, the base value of the probability of occurrence is higher, and therefore the probability of consecutive selection is also higher.

[0024] Therefore, one might consider introducing weighting coefficients for each task and determining the order of each task pattern based on these weighting coefficients. However, with this method, the user must set the weighting coefficients, meaning they need to readjust them every time the task pattern or the number of target products changes. In this case, the weighting coefficients and the output results may not match the user's expectations, requiring trial and error to achieve the desired output, which tends to increase the user's workload.

[0025] Therefore, in the following embodiment, we will describe a configuration that generates multiple priority patterns for each work group, identifies the optimal input order for each priority pattern, and presents these multiple optimal solutions to the user, thereby enabling the user to plan the input order to the work line while suppressing their workload. [Examples]

[0026] First, we will explain the principle of generating multiple priority patterns for each work group, identifying the optimal input order for each priority pattern, and presenting these multiple optimal solutions to the user. Figure 5 is a diagram illustrating this principle.

[0027] As illustrated in Figure 5, multiple priority patterns are created, assigning priorities to each work group. In priority pattern #1, work group A is given priority 1, work group B 2, work group C 3, and work group D 4. In priority pattern #2, work group A is given priority 2, work group B 4, work group C 1, and work group D 3.

[0028] Priority patterns may be generated to cover all priorities, or they may be generated for only a portion of all priorities. Priority patterns may also be generated randomly, for example.

[0029] Priority pattern #1 yields solution #1 for input order. Priority pattern #2 yields solution #2 for input order. Priority pattern #3 yields solution #3 for input order. In this way, the system automatically changes the priority pattern, obtains solutions for each priority pattern, and presents them to the user, thereby reducing the user's effort. For example, among the solutions obtained for each priority pattern, only those solutions where the Key Performance Indicator (KPI) satisfies predetermined conditions may be presented as the optimal solution.

[0030] Figure 6 illustrates a more specific explanation for identifying the solution in priority pattern #1. First, as illustrated in Figure 6, create slots equal to the number of products that will be fed into the work line. A slot is a variable used to store the work pattern for each product. If the total number of products is N, create slots 1 through N. Initially, the work for each work group in each slot has not yet been determined.

[0031] In priority pattern #1, work group A has the highest priority. Therefore, the goal tracking method is used to equalize each task in work group A. As an example, suppose that on the work line, task A1 is performed on 6 products, task A2 is performed on 4 products, and task A3 is performed on 2 products. Calculate the base probability of occurrence for each task. The base probability of occurrence is the ratio of the number of tasks to the total number of products. Since the total number of products is 12, the base probability for task A1 is 6 / 12 = 1 / 2. The base probability for task A2 is 4 / 12 = 1 / 3. The base probability for task A3 is 2 / 12 = 1 / 6. Next, calculate the probability of occurrence for each task for each input round. The probability of occurrence is calculated as input round × base probability - (number of times that task has been selected up to that point). In each input round, select the task with the highest probability of occurrence. Enter the selection results into each slot.

[0032] In priority pattern #1, work group B has the second highest priority. Therefore, the goal tracking method is used to equalize each task in work group B. As an example, suppose that on the work line, task B1 is performed on 6 products, task B2 is performed on 4 products, and task B3 is performed on 2 products. Calculate the base probability of occurrence for each task. The base probability of occurrence is the ratio of the number of tasks to the total number of products. Since the total number of products is 12, the base probability for task B1 is 6 / 12 = 1 / 2. The base probability for task B2 is 4 / 12 = 1 / 3. The base probability for task B3 is 2 / 12 = 1 / 6. Next, calculate the probability of occurrence for each task for each input round. The probability of occurrence is calculated as input round × base probability - (number of times that task has been selected up to that point). In each input round, select the task with the highest probability of occurrence. Input the task pattern corresponding to the selection result into each slot.

[0033] The following steps involve leveling each task in work groups with priority levels 3 and below using the goal tracking method, according to priority pattern #1. This identifies the tasks of each work group within each slot. Next, the products for which each slot's work pattern is performed are arranged in order. This yields the solution for priority pattern #1. Similarly, solutions for priority patterns #2 and below are obtained. By presenting the solutions for each priority pattern to the user, the user can select their desired solution from among those presented.

[0034] Figure 7(a) is a block diagram illustrating the overall configuration of the information processing device 100 according to Embodiment 1. As illustrated in Figure 7(a), the information processing device 100 includes a product information storage unit 10, a work pattern creation unit 20, a work pattern storage unit 30, a priority generation unit 40, a priority storage unit 50, a sequence identification unit 60, a judgment unit 70, an output unit 80, and the like.

[0035] Figure 7(b) is a block diagram illustrating the hardware configuration of the information processing device 100. As illustrated in Figure 7(b), the information processing device 100 includes a CPU 101, RAM 102, storage device 103, input device 104, display device 105, etc.

[0036] The CPU (Central Processing Unit) 101 is a central processing unit. The CPU 101 includes one or more cores. The RAM (Random Access Memory) 102 is a volatile memory that temporarily stores programs executed by the CPU 101, data processed by the CPU 101, etc. The storage device 103 is a non-volatile storage device. As the storage device 103, for example, a ROM (Read Only Memory), a solid-state drive (SSD) such as flash memory, or a hard disk driven by a hard disk drive can be used. The storage device 103 stores the planning program. The input device 104 is an input device such as a keyboard or mouse. The display device 105 is a display device such as an LCD (Liquid Crystal Display). When the CPU 101 executes the planning program, the product information storage unit 10, the work pattern creation unit 20, the work pattern storage unit 30, the priority generation unit 40, the priority storage unit 50, the order identification unit 60, the judgment unit 70, and the output unit 80 are realized. Furthermore, dedicated hardware such as circuits may be used for the product information storage unit 10, work pattern creation unit 20, work pattern storage unit 30, priority generation unit 40, priority storage unit 50, sequence identification unit 60, judgment unit 70, and output unit 80.

[0037] Figures 8 and 9 are flowcharts showing an example of the planning process executed by the information processing device 100. The work pattern creation unit 20 obtains product information from the product information storage unit 10 and creates a work pattern, which is a combination of tasks required for each product (step S1). The product information includes the quantity for each product type and the total number of items included in each product type (total number of products). The product information also includes the tasks required for each product. For example, for a certain product, a work pattern is created with the combination of tasks A1, B3, and C2. The work pattern for each product is represented as prod2cond_map. prod2cond_map is stored in the work pattern storage unit 30.

[0038] Figure 10 illustrates the contents of prod2cond_map. As illustrated in Figure 10, work patterns, which are combinations of tasks performed for each product, are defined. The same work pattern may be performed for multiple products. Within each work pattern, some tasks may overlap, while others may differ.

[0039] Next, the sequence identification unit 60 refers to prod2cond_map and extracts all the created work patterns (step S2). The extracted groups of work patterns are represented as cond_group. cond_group is stored in the work pattern storage unit 30. Figure 11 is an example of the contents of cond_group. As illustrated in Figure 11, the types of work patterns are stored. In the example in Figure 11, 27 types of work patterns are stored.

[0040] Next, the sequence identification unit 60 refers to the contents of cond_group stored in the work pattern storage unit 30 and identifies the number of work groups M (step S3). For example, in the example in Figure 11, work group A, work group B, and work group C are included. In this case, the number of work groups M is 3.

[0041] Next, the order determination unit 60 creates an array result_set with the total number of elements equal to the total number of products, and sets it to an empty set (step S4). By sequentially inputting each product into each element of this array result_set, the input order for each product can be created.

[0042] Next, the sequence determination unit 60 sets the loop count loop_count to its initial value of 0 (step S5).

[0043] Next, the sequence determination unit 60 determines whether the loop count loop_count is less than the maximum loop count N (step S6). By defining the maximum loop count N, an upper limit can be set on the number of loop operations.

[0044] If "Yes" is determined in step S6, the priority generation unit 40 generates a priority pattern for the work group (step S7). For example, the priority generation unit 40 randomly generates a priority pattern for each work group. The generated priority patterns are stored in the priority storage unit 50. Figure 12 is an example of a priority pattern. In the example in Figure 12, the priority for each work group is generated randomly.

[0045] Next, the sequence identification unit 60 generates slots equal to the total number of products (step S8). At this stage, the task for each slot has not yet been determined.

[0046] Next, the sequence identification unit 60 assigns 1 as the initial value of variable i (step S9). Variable i is a variable that represents the priority of each work group in the priority pattern.

[0047] Next, the sequence identification unit 60 determines whether the variable i is less than M+1 (step S10). This process makes it possible to determine whether the identification of each task in each task group has been completed in each priority pattern.

[0048] If "Yes" is determined in step S10, the sequence determination unit 60 assigns 0 to the variable j (step S11). The variable j is used to determine the number of slots in which the work of the work group with priority i has been identified. If the work of the work group with priority i has not yet been identified in any slot, the variable j is 0.

[0049] Next, the sequence identification unit 60 determines whether the variable j is less than the number of slots (step S12). This process makes it possible to determine whether the work of the work group with priority i has been identified for all slots.

[0050] If "Yes" is determined in step S12, the sequence identification unit 60 focuses on each slot and uses the target tracking method to calculate the probability of each task appearing in the task group with priority i (step S13). Specifically, the probability of appearance for each task is calculated as input count × base value - (number of times that task has been selected up to that point). For example, if task group A has priority i, the probabilities of appearance for tasks A1, A2, and A3 are calculated.

[0051] Next, the sequence identification unit 60 creates a combination of tasks registered in slot j, from priority 1 to priority (i-1) (step S14). The created combination is named cond_group j,i This is how it is expressed. For example, if i is 3 and j=3, it creates a combination of tasks from the first and second priority work groups registered in slot 3 (for example, A1 and B2).

[0052] Next, the sequence identification unit 60 selects the elements of cond_group j,i Extract those that have as a subset and create a set of workgroups with priority i, named candidate, from them (step S15). For example, the cond_group created in step S14. j,i If it is A1B2, then work patterns A1B2C1, A1B2C2, and A1B2C3 will be extracted from cond_group in Figure 11.

[0053] Next, the order determination unit 60 registers the element with the highest probability of appearance from the set candidate into slot j (step S16).

[0054] Next, the sequence identification unit 60 adds 1 to the variable j (step S17). Then, the process is executed again from step S12.

[0055] If "No" is determined in step S12, the sequence determination unit 60 adds 1 to the variable i. Then, the process is executed again from step S10.

[0056] If "No" is determined in step S10, the sequence identification unit 60 refers to the prod2cond_map stored in the work pattern storage unit 30, assigns the corresponding product to each slot, and adds it to result_set (step S18).

[0057] Next, the sequence identification unit 60 adds 1 to loop_count (step S19). Then, the process is executed again from step S6.

[0058] If "No" is determined in step S6, the output unit 80 outputs all solutions stored in result_set whose KPIs are equal to or greater than the threshold (step S20). After that, the execution of the flowchart ends. Whether or not the KPIs of the solutions are equal to or greater than the threshold is determined by the judgment unit 70.

[0059] For example, the following formula can be used as a KPI. The formula below outputs the variance of the selection interval for each task within each work group. The closer the output of the formula below is to zero, the more it indicates that the selection interval for each task within each work group is approximately constant, and that local congestion in the input order is suppressed. Therefore, if the reciprocal of the formula below is used as the KPI, any KPI that is above a threshold will be considered a good solution.

number

[0060] Note that in the above formula, spec group `#(spec)` refers to a work group, and `spec` refers to each work (for example, A1, B1, etc.). `#(spec)` is the number of products that have `spec`. `Index(spec,i)` is the index of the i-th product in the input product column that has `spec`.

[0061] According to this embodiment, the order of tasks within a work group can be identified for each priority according to the priority of multiple priority patterns, and the order of products corresponding to each identified combination of tasks can be identified. In this way, trial and error in leveling the frequency of each task is performed automatically. As a result, the effort required for the user to individually introduce weighting coefficients for each task is eliminated, and the order of input to the work line can be planned while suppressing the user's effort.

[0062] Furthermore, by using the goal tracking method when determining the order of tasks within a work group, the frequency of each task can be easily equalized.

[0063] By generating multiple priority patterns and repeatedly performing a series of processes to determine the order in which products are introduced, and outputting the introduction order obtained in each of these processes, a solution can be presented to the user. In doing so, the optimal solution can be presented to the user by outputting the order in which the variance of the intervals in which each task within a work group is performed is below a threshold.

[0064] In the example above, priority patterns were generated randomly, but this is not the only way. For example, they could be generated based on the priority patterns generated so far and the quality of the obtained optimal solution.

[0065] Figure 13(a) illustrates another example of a priority pattern generation method. For example, as illustrated in Figure 13(a), two priority patterns that yield the optimal solution may be extracted, and other priority patterns may be generated based on their average rank. Specifically, priority may be determined from the one with the lowest average rank. In this way, other priority patterns that yield the optimal solution can be generated.

[0066] Figure 13(b) illustrates another example of a priority pattern generation method. For example, as illustrated in Figure 13(b), each work group may be assigned a classification based on the quality of its optimal solution, and a priority pattern may be generated based on this classification. For example, a first-place priority solution may be assigned 4 points, a second-place priority solution 3 points, a third-place priority solution 2 points, and a fourth-place priority solution 1 point, and the total score for each optimal solution may be calculated. Priorities may then be determined from those with the highest total scores.

[0067] In the above examples, the output of the output unit 80 is output to the display device 105, but it may also be output to the mobile machine 201 that automatically patrols the warehouse. Figure 14 is a block diagram illustrating this case. As illustrated in Figure 14, the output of the output unit 80 is output to the mobile machine 201 and the input device 202. The mobile machine 201 collects parts from product cases on each parts shelf so that the input order received from the output unit 80 is realized. Alternatively, the output of the output unit 80 may also be output to the input device 202. In this case, the input device 202 sequentially inputs each set of each product type into the work line according to the specified input order. The mobile machine 201 is equipped with a CPU, RAM, storage device, etc. For example, the storage device stores a control program that controls the travel route of the mobile machine 201, the RAM stores sequence information indicating the input order received from the output unit 60, and the CPU controls the operation of the mobile machine 201 based on the control program and sequence information. The input device 202 also includes a CPU, RAM, and a memory device. For example, the memory device stores a control program that controls the input process performed by the input device 202, and the RAM stores sequence information indicating the input order received from the output unit 60. The CPU controls the operation of the input device 202 based on the control program and the sequence information.

[0068] In each of the above examples, the product is an example of an object that is fed into the work line in a predetermined order. The priority generation unit 40 is an example of a priority generation unit that generates multiple priority patterns, assigning priorities to multiple work groups, under the condition that each of the multiple objects is sequentially subjected to a process selected from a plurality of work groups. The order identification unit 60 is an example of an order identification unit that identifies the order of processing within the work group for each priority according to the priority of the plurality of priority patterns, and identifies the order of the plurality of objects corresponding to each combination of identified processing. The output unit 80 is an example of an output unit that outputs the order of the objects obtained in each of the processes obtained in the process of generating multiple priority patterns and identifying the order of the objects, which is repeated multiple times.

[0069] Although embodiments of the present invention have been described in detail above, the present invention is not limited to these specific embodiments, and various modifications and changes are possible within the scope of the gist of the present invention as described in the claims. (Note) (Note 1) On the computer, Under the condition that processes selected from multiple work groups are performed sequentially on each of multiple objects, A process for generating multiple priority patterns by assigning priorities to the aforementioned multiple work groups, A planning program characterized by executing a process that identifies the order of processes within the work group for each priority according to the priority of a plurality of priority patterns, and identifies the order of a plurality of objects corresponding to each combination of processes identified. (Note 2) The planning program described in Appendix 1, characterized in that, when determining the order of processing within the aforementioned work group, for each processing within the work group, the ratio of the number of processing items to the total number of items is used as a base value, the product of the number of processing items and the base value is subtracted from the number of times the processing pattern has already been selected as the probability of occurrence, and the next processing is selected using the calculated probability of occurrence. (Note 3) To the aforementioned computer, The planning program described in Appendix 1 or Appendix 2, characterized by generating multiple priority patterns, repeating a series of processes to determine the order of the objects multiple times, and executing a process to output the order of the objects obtained in each of the series of processes. (Note 4) The planning program described in Appendix 3, characterized in that when outputting the order of the objects obtained in each of the aforementioned series of processes, it outputs the order in which the variance of the intervals in which each process within the work group is performed is less than a threshold. (Note 5) To the aforementioned computer, A planning program according to any one of the appendices 1 to 4, characterized by executing a process to randomly generate the aforementioned priority patterns. (Note 6) To the aforementioned computer, The planning program according to Appendix 3 or Appendix 4, characterized in that when generating the priority pattern, it executes a process to generate the next priority pattern according to the order of the objects obtained in each of the series of processes. (Note 7) Under the condition that processes selected from multiple work groups are performed sequentially on each of multiple objects, Multiple priority patterns are generated by assigning priorities to the aforementioned multiple work groups. A planning method characterized in that a computer executes a process to identify the order of processing within the work group for each priority according to the priority of a plurality of priority patterns, and to identify the order of the plurality of objects corresponding to each combination of the identified processing. (Note 8) The planning method described in Appendix 7, characterized in that, when determining the order of processing within the work group, for each processing within the work group, the ratio of the number of processing items to the total number of items is used as a base value, the product of the number of processing items and the base value is subtracted from the number of times the processing pattern has already been selected as the probability of occurrence, and the next processing is selected using the calculated probability of occurrence. (Note 9) The planning method according to Appendix 7 or Appendix 8, characterized in that the computer performs a process of generating multiple priority patterns, repeatedly performing a series of processes to identify the order of the objects, and outputting the order of the objects obtained in each of the series of processes. (Note 10) The planning method according to Appendix 9, characterized in that when outputting the order of the objects obtained in each of the series of processes, the order in which the variance of the intervals in which each process within the work group is performed is less than a threshold is output. (Note 11) The planning method according to any one of the appendices 7 to 10, characterized in that the computer performs a process to randomly generate the priority patterns. (Note 12) The planning method according to Appendix 9 or Appendix 10, characterized in that when generating the priority pattern, the computer performs a process to generate the next priority pattern according to the order of the objects obtained in each of the series of processes. (Note 13) A priority generation unit generates multiple priority patterns that assign priorities to the multiple work groups, under the condition that processes selected from multiple work groups are performed sequentially for each of the multiple objects, An information processing apparatus comprising: an order identification unit that identifies the order of processing within the work group for each priority according to the priority of a plurality of priority patterns, and identifies the order of a plurality of objects corresponding to each identified combination of processing. (Note 14) The information processing apparatus according to Appendix 13, characterized in that, when determining the order of processing within the work group, the sequence determination unit uses the ratio of the number of each processing to the total number of objects as a base value for each processing within the work group, calculates the probability of occurrence by subtracting the number of times the work pattern has already been selected from the product of the number of processing times and the base value, and selects the next processing using the calculated probability of occurrence. (Note 15) The information processing apparatus according to Appendix 13 or Appendix 14, characterized in that it comprises an output unit that outputs the order of the objects obtained in each of the processes, which is a series of processes that are repeated multiple times to generate a series of priority patterns and determine the order of the objects. (Note 16) The information processing apparatus according to Appendix 15, characterized in that when the output unit outputs the order of the objects obtained in each of the series of processes, it outputs from the obtained order that the variance of the intervals in which each process within the work group is performed is less than a threshold. (Note 17) The information processing apparatus according to any one of the appendices 13 to 16, characterized in that the priority generation unit randomly generates the priority pattern. (Note 18) The information processing apparatus according to Appendix 15 or Appendix 16, characterized in that the priority generation unit generates the next priority pattern according to the order of the objects obtained in each of the series of processes when generating the priority pattern. [Explanation of Symbols]

[0070] 10. Product Information Storage Unit 20. Work Pattern Creation Section 30 Work Pattern Storage Unit 40 Priority generation unit 50 Priority storage unit 60 Order specific part 70 Judgment Department 80 Output section 100 Information Processing Devices 101 CPU 102 RAM 103 Storage device 104 Input device 105 Display device 201 Running machine 202 Feeding device

Claims

1. On the computer, Under the condition that tasks selected from multiple work groups are performed sequentially on each of multiple objects, A process for generating multiple priority patterns by assigning priorities to the aforementioned multiple work groups, The process involves identifying the order of tasks within the work group for each priority according to the priority of the multiple priority patterns, and identifying the order of the multiple objects corresponding to each identified combination of tasks. A planning program characterized by determining the order of tasks within the aforementioned work group, using the ratio of the number of tasks to the total number of objects as a base value for each task within the work group, calculating the probability of occurrence by subtracting the number of times the task has already been selected from the product of the number of tasks and the base value, and selecting the task with the highest calculated probability of occurrence as the next task.

2. The planning program according to claim 1, characterized in that, when determining the order of tasks within the task group, for each task within the task group, the ratio of the number of tasks to the total number of objects is used as a base value, the probability of occurrence is calculated by subtracting the number of times the task has already been selected from the product of the number of tasks and the base value, and the next task is selected using the calculated probability of occurrence.

3. To the aforementioned computer, The planning program according to claim 1 or 2, characterized in that it generates multiple priority patterns, repeats a series of processes to identify the order of the objects multiple times, and executes a process to output the order of the objects obtained in each of the series of processes.

4. The planning program according to claim 3, characterized in that when outputting the order of the objects obtained in each of the series of processes, it outputs the order in which the variance of the intervals in which each operation within the work group is performed is less than a threshold.

5. To the aforementioned computer, A planning program according to any one of claims 1 to 4, characterized by performing a process to randomly generate the aforementioned priority patterns.

6. To the aforementioned computer, The planning program according to claim 3 or 4, characterized in that when generating the priority pattern, it executes a process to generate the next priority pattern according to the order of the objects obtained in each of the series of processes.

7. Under the condition that tasks selected from multiple work groups are performed sequentially on each of multiple objects, Computers A process for generating multiple priority patterns by assigning priorities to the aforementioned multiple work groups, A process to identify the order of tasks within the work group for each priority according to the priority of the multiple priority patterns, and to identify the order of the multiple objects corresponding to each identified combination of tasks, A planning method characterized by the following steps when determining the order of tasks within the aforementioned work group: For each task within the work group, the ratio of the number of tasks to the total number of objects is used as a base value; the probability of occurrence is calculated by subtracting the number of times the task has already been selected from the product of the number of tasks and the base value; and the task with the highest calculated probability of occurrence is selected as the next task.

8. A priority generation unit generates multiple priority patterns that assign priorities to the multiple work groups, under the condition that tasks selected from multiple work groups are performed sequentially for each of the multiple objects, An information processing device comprising: an order identification unit that, according to the priority of a plurality of priority patterns, identifies the order of tasks within the task group for each priority, identifies the order of a plurality of objects corresponding to each identified combination of tasks, and, when identifying the order of tasks within the task group, uses the ratio of the number of tasks to the total number of objects as a base value for each task within the task group, calculates an occurrence probability by subtracting the number of times the task has already been selected from the product of the number of tasks and the base value, and selects the task with the highest calculated occurrence probability as the next task.

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