Planning program, planning method, and information processing device

Abstract

To provide a planning program, a planning method, and an information processing apparatus capable of suppressing local congestion in a feeding order into a work line.SOLUTION: A planning program that causes a computer configured to sequentially specify the order of processing a plurality of types of objects in a work line for processing the plurality of types of objects, to execute processing of: specifying, as a basic value, a ratio of the number of objects of each of the types to the total number of the plurality of types of objects; calculating, as an appearance probability, a value obtained by subtracting the number of objects of the type of which a processing order has been determined, from a product of a process number and the basic value for each of the respective types; and changing a frequency of the processing for each of the types in accordance with a selection status of each of the types, in selecting a type to be processed next using the calculated appearance probability.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to a planning program, a planning method, and an information processing device. [Background technology]

[0002] Process plans are being created for sequentially performing work on a plurality of types of objects (see, for example, Patent Documents 1 and 2). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 6-196899 [Patent Document 2] Japanese Patent Application Laid-Open No. 2008-65751 Summary of the Invention [Problem to be solved by the invention]

[0004] When determining the order for each type, there may be cases where the same type is repeated consecutively. In this case, the same work may be repeated, which may reduce work efficiency. Therefore, target tracking may be used to level out the processing order for each type on the work line (called the input order to the work line). However, even when target tracking is used, there is a risk of localized congestion occurring.

[0005] In one aspect, the present invention aims to provide a planning program, a planning method, and an information processing device that can suppress localized congestion in the order in which parts are put into a work line. [Means for solving the problem]

[0006] In one aspect, the planning program causes a computer that sequentially determines the processing order of multiple types of objects in a work line that processes the multiple types of objects to perform the following steps: determine the ratio of the number of objects of each type to the total number of objects of the multiple types as a base value; calculate, for each type, the occurrence probability by subtracting the number of objects of that type whose processing order has already been determined from the product of the number of processing times and the base value; and vary the processing frequency for each type depending on the selection status for each type when selecting the type to be processed next using the calculated occurrence probability. [Effects of the Invention]

[0007] It is possible to provide a planning program, a planning method, and an information processing device that can suppress localized congestion in the order in which parts are put into a work line. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram for explaining an overview of multi-item mixed flow production. [Figure 2] 10(a) and 10(b) are diagrams illustrating an example of a work sequence in a multi-item mixed production work system. [Figure 3] 10(a) to 10(c) are diagrams illustrating a target tracking method. [Figure 4] 10(a) and 10(b) are diagrams for explaining the principle of dynamic adjustment of product selection according to the product selection situation. [Figure 5] 1A is a block diagram illustrating an example of the overall configuration of an information processing apparatus according to a first embodiment, and FIG. 1B is a block diagram illustrating an example of the hardware configuration of the information processing apparatus. [Figure 6] 10 is a flowchart illustrating an example of a planning process executed by an information processing device. [Figure 7] 10(a) to 10(f) are diagrams illustrating product selection in the second embodiment. [Figure 8] FIG. 10 is a diagram illustrating an example of occurrence probability calculated in the second embodiment. [Figure 9]10 is a flowchart illustrating an example of a planning process according to the second embodiment. [Figure 10] 10 is a flowchart illustrating an example of a planning process according to the second embodiment. [Figure 11] FIG. 10 is a block diagram showing a case where the results output by the output unit are output to a traveling machine. DETAILED DESCRIPTION OF THE INVENTION

[0009] Before describing the embodiments, an overview of multi-item mixed flow production will be described.

[0010] In warehouses, various parts are stored in stock in vast spaces. In such warehouses, each part for producing multiple types of products is stored. Each parts case containing multiple copies of the same part is stored on a designated parts shelf. In the warehouse, parts required for work in the factory are picked.

[0011] In the picking of these parts, workers may collect them from parts cases on each parts shelf, or a traveling vehicle (automated vehicle: AGV) may collect them automatically. For example, as shown in FIG. 1, a traveling vehicle 201 travels to the parts shelf that contains the parts designated for collection and picks them up. One or more parts are picked for each product. At least some of the required parts differ depending on the product type.

[0012] Sets of necessary parts for each product type are sequentially input onto the factory work line. As a result, the work to produce each product is carried out sequentially in the factory. For example, for each set of each product type picked up by the traveling machine 201, the input device 202 inputs them sequentially onto the work line in a specified input order.

[0013] Figures 2(a) and 2(b) are diagrams illustrating an example of a work sequence in a multi-product mixed flow work method. In a multi-product mixed flow work method, different types of products are input into a single work line in a predetermined order and move along the work line in order. As an example, it is assumed that products A to C are to be worked on as multiple different product types. It is assumed that one or more products of each product type are to be worked on. It is assumed that at least some of the work required for each product is different.

[0014] In Figure 2(a), product A flows first, followed by product B, then product C, product A, product B, and product C. In the example of Figure 2(a), the same product does not flow consecutively, so the supply frequency of each part is evenly distributed. Also, since the same work does not need to be repeated, work efficiency does not decrease.

[0015] In contrast, in Figure 2(b), product A flows first, followed by product A, then product B, product B, product C, and product C. In the example of Figure 2(b), the same products flow consecutively, resulting in an uneven supply frequency for each part. Also, the need to repeat the same work can lead to a decrease in work efficiency.

[0016] Therefore, it is desirable to standardize the order in which products are put onto the production line. For example, it is possible to suppress bias in the order of each product by using a target tracking method. The target tracking method is a method of selecting from each product a product with a high probability of appearing for each index (put-in).

[0017] Figures 3(a) to 3(c) are diagrams illustrating the target tracking method. As an example, assume that six products A, four products B, and two products C are being worked on on a production line. As illustrated in Figure 3(a), first, the base value of the occurrence probability is calculated for each product type. The base value of the occurrence probability is the ratio of the number of each product type to the total number of products. Since there are 12 products in total, the base value of product A is 6 / 12 = 1 / 2. The base value of product B is 4 / 12 = 1 / 3. The base value of product C is 2 / 12 = 1 / 6.

[0018] Next, calculate the appearance probability of each product type for each input. Input = 1 represents the operation of inputting the first product into the production line. Input = n represents the operation of inputting the nth product into the production line. The appearance probability is calculated as input x base value - (number of times that product type has been selected up to that point).

[0019] As shown in Figure 3(b), when the number of times an item is introduced is 1, the probability of product A appearing is 0.5, the probability of product B appearing is 0.33..., and the probability of product C appearing is 0.16.... The target tracking method selects the product type with the highest probability of appearance, so product A is selected as the first product to be introduced. At this point, the number of times product A has been selected is 1, and the number of times product B and product C have been selected is 0.

[0020] When the number of inputs is 2, the probability of product A appearing is 0, the probability of product B appearing is 0.66, and the probability of product C appearing is 0.33. Therefore, product B is selected as the second input product. At this point, the number of times that product A and product B have been selected is 1, and the number of times that product C has been selected is 0.

[0021] If the order in which each product is put onto the production line is determined using a similar procedure, the output order will be product A, product B, product C, product A, product B, product A, product A, as shown in Figure 3(c). This order equalizes the order in which each product is put onto the production line to a certain extent, but product A will be put onto the production line in succession along the way. The reason why the same product can be selected consecutively like this is because the base value of the occurrence probability differs for each product. For example, for products with a large number of units, the base value of the occurrence probability is high, so there is a high possibility that they will be selected consecutively.

[0022] Therefore, it is possible to control local density variations by introducing spacing constraints and continuity constraints. A spacing constraint is a constraint such as "there must be an interval of at least X units between specified products." A continuity constraint is a constraint such as "there must not be more than Y units of specified products in a row." However, with this method, the user must set the values ​​of the spacing constraints and continuity constraints, which means that the user must reset them every time the target products or the number of tasks for each product changes. This can easily increase the user's workload.

[0023] Therefore, in the following embodiment, we will explain a configuration that dynamically adjusts product selection in each input depending on the product selection situation up to that point, making it more difficult to select the most recently selected product, thereby suppressing local congestion. [Example]

[0024] First, we will explain the principle of dynamic adjustment of product selection according to the product selection situation. Figures 4(a) and 4(b) are diagrams for explaining this principle. Figure 4(a) illustrates the base value of the appearance probability, similar to Figure 3(a).

[0025] In the first embodiment, the appearance probability of each product is calculated for each insertion, and the appearance probability of each product type is corrected according to the selection status of each product type.

[0026] When the number of inputs is 1, no product type has been selected yet, so the correction value is not taken into account. Therefore, when the number of inputs is 1, the appearance probability is calculated as the number of inputs (= 1) x the base value. Therefore, as shown in Figure 4(b), the appearance probability of product A is 0.5, the appearance probability of product B is 0.33..., and the appearance probability of product C is 0.16.... Therefore, product A is selected as the first product to be input. At this point, the number of times product A has been selected is 1, and the number of times product B and product C have been selected is 0.

[0027] From the number of times inserted = 2 onwards, the occurrence probability is calculated as the number of times inserted x base value - (the number of times that product type has been selected up to that point), as in Figure 3(b). However, for each product type, if the selection is within the range corresponding to the inverse of the base value (decimals are rounded down), a correction value is temporarily subtracted from the occurrence probability.

[0028] For example, for each product type, it is determined whether it has been selected in the most recent number of insertions, which corresponds to the reciprocal of the base value. For product A, the reciprocal of the base value is 2, so it is determined whether product A has been selected in the most recent two insertions. For product B, the reciprocal of the base value is 4, so it is determined whether product B has been selected in the most recent four insertions. For product C, the reciprocal of the base value is 6, so it is determined whether product C has been selected in the most recent six insertions.

[0029] In the example of Figure 4(b), a base value is used as an example of a correction value. When the number of times input is 2, the probability of appearance of product A is 0, the probability of appearance of product B is 0.66..., and the probability of appearance of product C is 0.33.... As product A was selected when the number of times input is 1, the base value of 0.5 is subtracted from the probability of appearance of 0, correcting it to -0.5. In this case, product B is selected as the second input product. At this point, the number of times that product A and product B have been selected is 1, and the number of times that product C has been selected is 0.

[0030] When the number of times input is 3, the probability of appearance of product A is 0.5, the probability of appearance of product B is 0, and the probability of appearance of product C is 0.5. As product A was selected with input = 1, the base value of 0.5 is subtracted from the probability of appearance of 0.5, correcting it to 0. As product B was selected with input = 2, the base value of 0.33... is subtracted from the probability of appearance of 0, correcting it to -0.33... In this case, product C is selected as the third input product. At this point, the number of times products A to C have been selected is 1.

[0031] When the number of times input is 4, the probability of appearance of product A is 1, the probability of appearance of product B is 0.33..., and the probability of appearance of product C is -0.33.... For product B, since it was selected with input = 2, the base value of 0.33... is subtracted from the probability of appearance of 0.33..., correcting it to 0. For product C, since it was selected with input = 3, the base value of -0.16... is subtracted from the probability of appearance of -0.33..., correcting it to -0.5. In this case, product A is selected as the fourth product input. At this point, the number of times product A was selected is 2, and the number of times products B and C were selected is 1.

[0032] When the number of times input is 5, the probability of appearance of product A is 0, the probability of appearance of product B is 0.66…, and the probability of appearance of product C is -0.16…. For product A, since it was selected with input of 4, the base value of 0.5 is subtracted from the probability of appearance of 0.5, correcting it to 0. For product C, since it was selected with input of 3, the base value of -0.16… is subtracted from the probability of appearance of -0.16…, correcting it to -0.33…. In this case, product B is selected as the fifth input product.

[0033] Similarly, the occurrence probability is calculated for each product type, and a correction value is subtracted and corrected depending on whether or not selection occurs within the range corresponding to the reciprocal of the base value. The reciprocal of the base value represents the average interval of selection. Therefore, by correcting the occurrence probability for the interval corresponding to the average interval of selection, it is possible to suppress selection at intervals smaller than the average interval of selection. For example, a correction value is subtracted from the occurrence probability for six inputs, which is the reciprocal of the base value of product C. As a result, product C becomes less likely to be selected during those six inputs. On the other hand, for product A, the correction value is only subtracted from the occurrence probability for two inputs, which is the reciprocal of the base value. Therefore, although product A is less likely to be selected consecutively, the interval between inputs is also shorter. Therefore, product A is selected with priority over product C, and localized high concentrations of product A, which are in large quantities, are suppressed in the work sequence.

[0034] The correction value is not particularly limited. For example, a fixed value common to each product type may be used as the correction value. This fixed value may be a value adjustable by the user. Alternatively, the correction value may be calculated by multiplying the base value of each product type by a coefficient α. This coefficient α may be a value adjustable by the user.

[0035] Fig. 5(a) is a block diagram illustrating an example of the overall configuration of an information processing device 100 according to Example 1. As illustrated in Fig. 5(a), the information processing device 100 includes a product information storage unit 10, a base value calculation unit 20, a base value storage unit 30, an order calculation unit 40, an order storage unit 50, an output unit 60, and the like.

[0036] Fig. 5(b) is a block diagram illustrating an example of a hardware configuration of the information processing device 100. As illustrated in Fig. 5(b), the information processing device 100 includes a CPU 101, a RAM 102, a storage device 103, an input device 104, a display device 105, and the like.

[0037] 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. Examples of the storage device 103 include a read-only memory (ROM), a solid-state drive (SSD) such as a flash memory, and a hard disk driven by a hard disk drive. The storage device 103 stores a planning program. The input device 104 is an input device such as a keyboard or a 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 base value calculation unit 20, the base value storage unit 30, the sequence calculation unit 40, the sequence storage unit 50, and the output unit 60 are realized. The product information storage unit 10, base value calculation unit 20, base value storage unit 30, sequence calculation unit 40, sequence storage unit 50, and output unit 60 may be implemented using hardware such as dedicated circuits.

[0038] FIG. 6 is a flowchart showing an example of a planning and planning process executed by the information processing device 100. The base value calculation unit 20 acquires product information from the product information storage unit 10 and calculates a base value and the reciprocal of the base value for each product type (step S1). The product information includes the number of each product type and the total number of items included in each product type (total number of products). The base value of each product is the ratio of the number of each product type to the total number of products. The base value storage unit 30 stores the base value and the reciprocal of the base value for each product.

[0039] Next, the sequence calculation unit 40 creates an array "result" having elements equal to the total number of products (step S2). The sequence of inputting each product type onto the production line is registered in the array "result." The sequence storage unit 50 stores the array "result."

[0040] Next, the sequence calculation unit 40 assigns 0 to the variable j (step S3). The variable j is used to determine the insertion time when the type of product to be inserted is specified. When no product type has been specified yet, the variable j is 0.

[0041] Next, the sequence calculation unit 40 determines whether the variable j is smaller than the total number of products (step S4). This process makes it possible to determine whether the loading sequence for the total number of products has been identified.

[0042] If the answer in step S4 is "Yes," the sequence calculation unit 40 calculates the appearance probability of each product type using the target tracking method (step S5). In this process, the appearance probability is calculated using the same procedure as in FIG. 3(b). Specifically, the appearance probability is calculated as the number of times the product type has been inserted multiplied by the base value minus (the number of times the product type has been selected up to that point).

[0043] Next, the order calculation unit 40 checks the range of (j-the inverse of the base value) to (j-1) in the array result for each product type, and counts the number of times each product type is selected (step S6).

[0044] Next, the order calculation unit 40 subtracts the correction value from the appearance probability of the product type that has been selected one or more times according to the count result of step S6 (step S7), thereby correcting the appearance probability of each product type.

[0045] Next, the order calculation unit 40 registers the product type with the highest occurrence probability after executing step S7 in an array result[j] (step S8), where result[j] represents the j-th index.

[0046] Next, the order calculation unit 40 adds 1 to the variable j (step S9). After that, the process is executed again from step S4. If the determination in step S4 is "No," the order has been determined for each of the total number of products, and the execution of the flowchart ends. In this case, the output unit 60 outputs the determined order. The output order is displayed on the display device 105.

[0047] According to this embodiment, when selecting the product to be dispensed in the next dispense using the target tracking method, the dispense frequency is dynamically adjusted according to the product selection status up to that point. As a result, the most recently selected product becomes less likely to be selected, thereby suppressing localized congestion. [Example]

[0048] In the second embodiment, a method for creating a tree structure connecting each product type and updating the probability of each node using a target tracking method will be described.

[0049] As illustrated in Figure 7(a), as in Example 1, the base value of product A is 6 / 12 = 1 / 2, the base value of product B is 4 / 12 = 1 / 3, and the base value of product C is 2 / 12 = 1 / 6.

[0050] Next, as illustrated in FIG. 7(b), the order calculation unit 40 calculates the order of the nodes in such a way that the sum of the base values ​​at each node is (1 / 2) n A tree structure is created using a greedy method, starting with the smallest base value, so that each product type is grouped. A node is a point where each product type meets in the tree structure. In the example of Figure 7(b), the sum of the base values ​​at the node where product B and product C meet is 1 / 2. The sum of the base values ​​at the final node where all products meet is 1 / 1, which corresponds to n=0. In a tree structure created in this way, product types with low base values ​​form a single group that shares a single node.

[0051] Next, the order calculation unit 40 calculates the occurrence probability of each product type using the target tracking method, as illustrated in FIG. 7(c). Since no product type has been selected at input count = 1, the occurrence probability is equal to the base value. An example of the calculated occurrence probability is shown in FIG. 8. FIG. 8 also illustrates the sum of the occurrence probabilities of product B and product C. The order calculation unit 40 traces the highest occurrence probability at each node from the top of the tree structure (the point where all product types converge). In the example of FIG. 7(c), the occurrence probability of product A is 1 / 2, and the sum of the occurrence probabilities of product B and product C is 1 / 2, which is the same value. In this case, priority is given to the node containing a product type with a lower base value. Therefore, the process proceeds to product B and product C. Next, product B is selected because the occurrence probability of product B is 1 / 3 and the occurrence probability of product C is 1 / 6.

[0052] Next, the order calculation unit 40 calculates the appearance probability of each product type for the number of insertions = 2 according to the appearance probability = number of insertions × base value - (number of times that product type has been selected up to that point). As a result, the appearance probability of product A is 1, the appearance probability of product B is -1 / 3, the appearance probability of product C is 1 / 3, and the sum of the appearance probabilities of product B and product C is 0. In this case, product A is selected, as shown in the example of Figure 7(d).

[0053] Next, the order calculation unit 40 calculates the appearance probability of each product type for the number of insertions = 3 according to the appearance probability = number of insertions × base value - (number of times that product type has been selected up to that point). As a result, the appearance probability of product A is 1 / 2, the appearance probability of product B is 0, the appearance probability of product C is 1 / 2, and the sum of the appearance probabilities of product B and product C is 1 / 2. In this case, as illustrated in Figure 7(e), the appearance probability of product A is 1 / 2, and the sum of the appearance probabilities of product B and product C is 1 / 2, which are the same. In this case, priority is given to the selection that includes a product type with a lower base value. Therefore, the selection proceeds to product B and product C. Next, since the appearance probability of product B is 0 and the appearance probability of product C is 1 / 2, product C is selected.

[0054] Similarly, the probability of occurrence is calculated for each product type for each input. Examples of the results are shown in Figures 7(f) and 8. The input order is output as follows: Product B, Product A, Product C, Product A, Product B, Product A, Product B, Product A, Product C, Product A, Product B, Product A. In this example, consecutive selection of the same product type is prevented.

[0055] The sum of the base values ​​at each node is (1 / 2) n This makes it difficult for the same product type to be selected in a short period of time. Because a target tracking method is used, the probability of a node containing the most recently selected product type is low, and the probability of other nodes being selected is high. Because product types with low base values ​​are grouped as nodes, it is possible to prevent those product types from being selected consecutively, and it is possible to reduce the density of product types with high base values.

[0056] 9 and 10 are flowcharts illustrating an example of a planning process according to Example 2. The base value calculation unit 20 acquires product information from the product information storage unit 10 and calculates a base value for each product (step S11). The base value storage unit 30 stores the base value for each product.

[0057] Next, the order calculation unit 40 creates a list by arranging the product types in ascending order of base values ​​(step S12).

[0058] Next, the order calculation unit 40 sets min_n=min(log2(basic value of product type in list[0])×(−1)) (step S13).

[0059] Next, the order calculation unit 40 determines whether min_n is greater than 0 (step S14).

[0060] If the determination in step S14 is "Yes," the order calculation unit 40 selects a sequence having a base value of 2 from the list. min_n-1 An element smaller than is extracted and stored in the candidate (step S15).

[0061] Next, the order calculation unit 40 repeatedly extracts one element from the beginning and one from the end of the candidate candidates and adds the node combining the two to List until the candidate candidates are empty (step S16). The sum of the two base values ​​is set as the base value of the new node.

[0062] Next, the order calculation unit 40 updates the list by sorting the base values ​​in ascending order (step S17). After that, the process is repeated again from step S13.

[0063] If the determination in step S14 is "No," the tree structure for each product type is completed. The order calculation unit 40 creates a tree structure based on the nodes in the list (step S18).

[0064] Next, the sequence calculation unit 40 creates an array "result" having elements equal to the total number of products (step S19). The sequence of putting each product type into the production line is registered in the array "result."

[0065] Next, the sequence calculation unit 40 assigns 0 to the variable j (step S20). The variable j is used to determine the insertion time when the type of product to be inserted is specified. When no product type has been specified yet, the variable j is 0.

[0066] Next, the sequence calculation unit 40 determines whether the variable j is smaller than the number of products (step S21). This process makes it possible to determine whether the loading sequence has been identified for the total number of products.

[0067] If the answer in step S20 is "Yes," the sequence calculation unit 40 calculates the appearance probability of each product type using the target tracking method (step S22). In this process, the appearance probability is calculated using the same procedure as in FIG. 3(b). Specifically, the appearance probability is calculated as the number of times inserted multiplied by the base value minus (the number of times the product type has been selected up to that point).

[0068] Next, the order calculation unit 40 updates the occurrence probability of each node from the end of the tree to the top (step S23).

[0069] Next, the order calculation unit 40 selects the node with the highest appearance probability from the top of the tree, and registers the final product in the array result[j] (step S24), where result[j] represents the j-th index.

[0070] Next, the order calculation unit 40 adds 1 to the variable j (step S25). After that, the process is executed again from step S20. If the determination in step S20 is "No," the order has been determined for each of the total number of products, and the execution of the flowchart ends. In this case, the output unit 60 outputs the determined order (step S26). The output order is displayed on the display device 105.

[0071] In this embodiment, when selecting the product to be dispensed in the next dispense using the target tracking method, the dispense frequency is dynamically adjusted according to the product selection status up to that point. This makes it less likely that the most recently selected product will be selected, thereby suppressing localized congestion.

[0072] In the above examples, the results output by the output unit 60 are output to the display device 105. However, they may also be output to a traveling machine 201 that automatically patrols the warehouse. FIG. 11 is a block diagram illustrating this case. As illustrated in FIG. 11, the results output by the output unit 60 are output to the traveling machine 201 and the input device 202. The traveling machine 201 collects parts from the parts product cases on each parts shelf so as to realize the input order received from the output unit 60. The results output by the output unit 60 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 traveling machine 201 includes a CPU, RAM, a storage device, etc., and the storage device stores, for example, a control program that controls the travel route of the traveling machine 201, etc., and the RAM stores order information indicating the input order received from the output unit 60. The CPU controls the operation of the traveling machine 201 based on the control program and order information. The input device 202 also includes a CPU, RAM, a storage device, etc., and for example, the storage device stores a control program that controls the input process performed by the input device 202, the RAM stores sequence information indicating the input sequence received from the output unit 60, and the CPU controls the operation of the input device 202 based on the control program and sequence information.

[0073] In each of the above examples, a product is an example of an object to be processed in a predetermined order on a work line. In a process in which the order calculation unit 40 sequentially determines the processing order of multiple types of objects on a work line that processes the multiple types of objects, the order calculation unit 40 determines the ratio of the number of objects of each type to the total number of objects of the multiple types as a base value, calculates the occurrence probability for each type by subtracting the number of objects of that type whose processing order has already been determined from the product of the number of times the type has been processed and the base value, and selects the type to be processed next using the calculated occurrence probability.

[0074] Although the embodiments of the present invention have been described in detail above, the present invention is not limited to such specific embodiments, and various modifications and variations are possible within the scope of the gist of the present invention as described in the claims. (Addendum) (Appendix 1) a computer that sequentially specifies a processing order for a plurality of types of objects in a work line that processes the plurality of types of objects, a process of identifying a ratio of the number of objects of each type to the total number of objects of the plurality of types as a base value; a process of calculating, as an occurrence probability for each type, a value obtained by subtracting the number of objects of the type whose processing order has already been determined from the product of the number of times the processing has been performed and the reference value; When selecting the type to be processed next using the calculated occurrence probability, the program executes a process of varying the processing frequency for each of the types depending on the selection status for each of the types. (Appendix 2) The computer, The planning program described in Appendix 1 is characterized in that the occurrence probability calculated for each of the types is corrected according to the selection status for each of the types, and a process is executed to select the type to be processed next according to the corrected occurrence probability. (Appendix 3) The computer, The planning program described in Appendix 2 is characterized in that, when the type for which the processing order has been identified has been selected a number of times corresponding to the reciprocal of the base value, a process is executed to calculate the corrected occurrence probability by correcting the calculated occurrence probability. (Appendix 4) The computer, 4. The planning program according to claim 2 or 3, wherein when correcting the calculated occurrence probability, a process of subtracting the base value for each of the types is executed. (Appendix 5) The computer, The sum of the basic values ​​is (1 / 2)n and for each group, selecting the type with the highest calculated probability of occurrence, and for the selected group, selecting the type with the highest calculated probability of occurrence, thereby executing a process of selecting the type to be introduced next. (Appendix 6) The computer, A planning program as described in Appendix 5, characterized in that if the calculated total value of the occurrence probability is the same for each group, a process is executed to select the group that includes the lower basic value. (Appendix 7) a computer that sequentially specifies a processing order for a plurality of types of objects in a work line that processes the plurality of types of objects, a process of identifying a ratio of the number of objects of each type to the total number of objects of the plurality of types as a base value; a process of calculating, as an occurrence probability for each type, a value obtained by subtracting the number of objects of the type whose processing order has already been determined from the product of the number of times the processing has been performed and the reference value; and when selecting the type to be processed next using the calculated occurrence probability, varying the processing frequency for each of the types depending on the selection status for each of the types. (Appendix 8) The planning method described in Appendix 7, characterized in that the computer executes a process of correcting the occurrence probability calculated for each of the types in accordance with the selection status for each of the types, and selecting the type to be processed next in accordance with the corrected occurrence probability. (Appendix 9) The planning method described in Appendix 8, characterized in that the computer executes a process of calculating the corrected occurrence probability by correcting the calculated occurrence probability when the type for which the processing order has been identified has been selected a number of times for processing corresponding to the reciprocal of the base value. (Appendix 10) 10. The planning method according to claim 8 or 9, wherein the computer executes a process of subtracting the base value for each of the types when correcting the calculated occurrence probability. (Appendix 11) The computer, The sum of the basic values ​​is (1 / 2) n and for each group, selecting the type with the highest calculated probability of occurrence, and for the selected group, selecting the type with the highest calculated probability of occurrence, thereby executing a process of selecting the type to be introduced next. (Appendix 12) The planning method described in Appendix 11, characterized in that if the calculated total value of the occurrence probability is the same for each group, a process is executed to select the group that includes the lower basic value. (Appendix 13) a selection unit that, in a process of sequentially specifying a processing order for a plurality of types of objects in a work line that processes the plurality of types of objects, specifies a ratio of the number of objects of each type to the total number of objects of the plurality of types as a base value, calculates an occurrence probability for each type by subtracting the number of objects of the type whose processing order has already been determined from the product of the number of times the object has been processed and the base value, and selects the type to be processed next using the calculated occurrence probability; the selection unit executes a process of varying a processing frequency for each of the types in accordance with a selection status for each of the types. (Appendix 14) The information processing device described in Appendix 13, characterized in that the selection unit corrects the occurrence probability calculated for each of the types depending on the selection status for each of the types, and selects the type to be processed next depending on the corrected occurrence probability. (Appendix 15) The information processing device described in Appendix 14 is characterized in that the selection unit calculates the corrected occurrence probability by correcting the calculated occurrence probability when the type whose processing order has been identified has been selected for a number of processing times corresponding to the reciprocal of the base value. (Appendix 16) 16. The information processing device according to claim 14, wherein the selection unit subtracts the base value for each of the types when correcting the calculated occurrence probability. (Appendix 17) The selection unit selects whether the sum of the base values ​​is (1 / 2) n and for each group, selecting the type with the highest calculated probability of appearance, and for the selected group, selecting the type with the highest calculated probability of appearance, thereby selecting the type to be inserted next. (Appendix 18) The information processing device described in Appendix 17, characterized in that the selection unit selects the group that includes the lower basic value when the calculated total value of the occurrence probability is the same for each group. [Explanation of symbols]

[0075] 10 Product information storage section 20 Basic value calculation section 30 Base value storage section 40 Order calculation part 50 Order storage section 60 Output section 100 Information processing device 101 CPU 102 RAM 103 Storage device 104 Input Device 105 Display device 201 Running Machine 202 Feeding device

Claims

1. a computer that sequentially specifies a processing order for a plurality of types of objects in a work line that processes the plurality of types of objects, a process of identifying a ratio of the number of objects of each type to the total number of objects of the plurality of types as a base value; a process of calculating, for each type, a value obtained by subtracting the number of objects of the type whose processing order has already been determined from the product of the number of times the processing has been performed and the base value, as an occurrence probability; When selecting the type to be processed next using the calculated occurrence probability, a process is executed in which the processing frequency for each of the types is changed according to the selection status for each of the types; a corrected occurrence probability is calculated by correcting the calculated occurrence probability when the type for which the processing order has been specified has been selected for a number of processing times corresponding to the reciprocal of the base value.

2. The computer, The planning program according to claim 1, characterized in that the occurrence probability calculated for each of the types is corrected according to the selection status for each of the types, and a process is executed to select the type to be processed next according to the corrected occurrence probability.

3. The computer, 3. The planning program according to claim 1, wherein the program executes a process of subtracting the base value for each of the types when correcting the calculated occurrence probability.

4. A computer that sequentially identifies the processing order of multiple types of objects in a work line that processes the multiple types of objects, a process of identifying a ratio of the number of objects of each type to the total number of objects of the plurality of types as a base value; a process of calculating, for each type, a value obtained by subtracting the number of objects of the type whose processing order has already been determined from the product of the number of times the processing has been performed and the base value, as an occurrence probability; When selecting the type to be processed next using the calculated occurrence probability, a process is executed in which the processing frequency for each of the types is changed according to the selection status for each of the types; a planning program that, when selecting the next type to be processed, groups the types in ascending order of base value so that the sum of the base values ​​is (1 / 2) n , selects the type with the highest calculated total occurrence probability for each group, and selects the type with the highest calculated occurrence probability for the selected group, thereby selecting the next type to be entered.

5. The computer, 5. The planning program according to claim 4, wherein when the calculated total values ​​of the occurrence probabilities are the same for each group, a process of selecting a group including a lower base value is executed.

6. a computer that sequentially specifies a processing order for a plurality of types of objects in a work line that processes the plurality of types of objects, a process of identifying a ratio of the number of objects of each type to the total number of objects of the plurality of types as a base value; a process of calculating, for each type, a value obtained by subtracting the number of objects of the type whose processing order has already been determined from the product of the number of times the processing has been performed and the base value, as an occurrence probability; When selecting the type to be processed next using the calculated occurrence probability, a process is executed in which the processing frequency for each of the types is changed according to the selection status for each of the types; a corrected occurrence probability is calculated by correcting the calculated occurrence probability when the type for which the processing order has been specified has been selected a number of times corresponding to the reciprocal of the base value.

7. A computer that sequentially identifies a processing order for a plurality of types of objects in a work line that processes the plurality of types of objects, a process of identifying a ratio of the number of objects of each type to the total number of objects of the plurality of types as a base value; a process of calculating, for each type, a value obtained by subtracting the number of objects of the type whose processing order has already been determined from the product of the number of times the processing has been performed and the base value, as an occurrence probability; When selecting the type to be processed next using the calculated occurrence probability, a process is executed in which the processing frequency for each of the types is changed according to the selection status for each of the types; a planning method characterized by: when selecting the next type to be processed, grouping the types in order of decreasing base value so that the sum of the base values ​​is (1 / 2) n , selecting the type with the highest calculated total occurrence probability for each group, and selecting the type with the highest calculated occurrence probability for the selected group, thereby selecting the next type to be introduced.

8. a selection unit that, in a process of sequentially specifying a processing order for a plurality of types of objects in a work line that processes the plurality of types of objects, specifies a ratio of the number of objects of each type to the total number of objects of the plurality of types as a base value, calculates an occurrence probability for each type by multiplying the number of times of processing by the base value by the number of objects of the type whose processing order has already been determined, and selects the type to be processed next using the calculated occurrence probability; when calculating the occurrence probability, the selection unit corrects the calculated occurrence probability when the type whose processing order has been identified has been selected a number of times corresponding to the reciprocal of the base value, thereby calculating a corrected occurrence probability; The information processing device, wherein the selection unit varies a processing frequency for each of the types in accordance with a selection status for each of the types.

9. An information processing device comprising: a selection unit that selects the type to be input next by performing the following processes in a work line that processes a plurality of types of objects, in a process of sequentially specifying the processing order of the plurality of types of objects: a process of specifying the ratio of the number of objects of each type to the total number of objects of the plurality of types as a base value; a process of calculating, for each type, the product of the number of processing times and the base value minus the number of objects of that type for which the processing order has already been determined, as an occurrence probability; and a process of varying the processing frequency for each type in accordance with the selection status for each type when selecting the type to be processed next using the calculated occurrence probability; and when selecting the type to be processed next, groups the types in order from the smallest base value so that the sum of the base values ​​is (1 / 2)n, and for each group, selects the type with the highest calculated sum of the occurrence probabilities, and within the selected group, selects the type with the highest calculated occurrence probability, thereby selecting the type to be input next.

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