Calculation program, calculation method, and information processing device

Abstract

To provide a calculation program, a calculation method, and an information processing device that are capable of planning a processing order for a plurality of objects.SOLUTION: Under the conditions that each of multiple objects is processed at one of multiple processing timings in a set order, a processing specification is defined for each of the multiple objects, a processing period during which processing is performed for each of the multiple objects is defined, and the processing period includes one or more of the processing timings, a computer is caused to execute processes of: calculating, for each processing timing, a probability obtained as an inverse of the number of the processing timings included in the processing period for each of the processing specifications of the multiple objects: calculating a sum of probabilities of the processing specifications for each of the processing timings; calculating a cumulative sum of the sums of the probabilities for the processing timings; and identifying the processing timings of the multiple objects using the cumulative sum.SELECTED DRAWING: Figure 11

Description

[Technical field]

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

[0002] Process plans are being made for sequentially processing a plurality of types of objects (see, for example, Patent Documents 1 to 6). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2005-107735 A [Patent Document 2] Patent Publication No. 2022-6819 [Patent Document 3] JP 2007-34429 A [Patent Document 4] JP 2018-165952 A [Patent Document 5] JP 2020-194587 A [Patent Document 6] JP 2008-198129 A Summary of the Invention [Problem to be solved by the invention]

[0004] For example, a specification constraint is set such that multiple objects are input into one processing line in a predetermined order, and each object is subjected to processing according to a set specification. In this case, it is possible to use a target tracking method to level out the frequency of each specification.

[0005] On the other hand, there is also a need to consider constraints (time constraints) such as the fact that certain objects can only be processed within a certain period of time. It is difficult to plan a production sequence that satisfies specification constraints while observing time constraints.

[0006] In one aspect, the present invention aims to provide a calculation program, a calculation method, and an information processing device that can plan a processing order for a plurality of objects. [Means for solving the problem]

[0007] In one aspect, the calculation program causes a computer to execute the following processes, under the conditions that each of a plurality of objects is processed at one of a plurality of processing times in a predetermined order, processing specifications are defined for each of the plurality of objects, a processing period is defined in which each of the plurality of objects is processed, and the processing period includes one or more of the processing times: calculating, for each processing time, a probability obtained as the reciprocal of the number of the processing times included in the processing period, calculating the sum of the probabilities of the processing specifications for each of the processing times, calculating a cumulative sum of the sums of the probabilities for the processing times, and identifying the processing time for the plurality of objects using the cumulative sum. Effect of the Invention

[0008] It is possible to provide a calculation program, a calculation method, and an information processing device that are capable of planning a processing order for a plurality of objects. [Brief description of the drawings]

[0009] [Figure 1] FIG. 11 is a diagram illustrating an example of product data. [Diagram 2] FIG. 1 is a diagram illustrating a production period. [Diagram 3] This is an example of production sequence planning. [Figure 4] This is an example in which the production order for slots 1 to 4 is determined so that the cumulative number of appearances of each specification is as close as possible to the target number of appearances. [Diagram 5] FIG. 13 is a diagram illustrating an example of criteria for selecting a product for slot 5. [Figure 6] FIG. 13 is a diagram illustrating a case where there is no partial overlap in the period constraints of each product. [Figure 7] FIG. 13 is a diagram illustrating a case where there is partial overlap in the period constraints of each product. [Figure 8] FIG. 11 is a diagram illustrating product data. [Figure 9] FIG. 13 is a diagram illustrating a probability distribution. [Figure 10] 1A is a block diagram illustrating an example of an overall configuration of an information processing device 0 according to a first embodiment, and FIG. 1B is a block diagram illustrating an example of a hardware configuration of the information processing device. [Figure 11] 11 is a flowchart illustrating an example of a process executed by an information processing device. [Figure 12] FIG. 11 is a diagram illustrating a flowchart illustrating details of step S2. [Figure 13] FIG. 11 is a diagram illustrating an example of a flowchart illustrating details of step S3. [Figure 14] FIG. 11 is a diagram illustrating a flowchart illustrating details of step S4. [Figure 15] FIG. 13 is a diagram illustrating a flowchart illustrating details of a sequence specification process in step S41. [Figure 16] This is the probability distribution calculated for the product data in Figure 1. [Figure 17] This is a production sequence planned using the method of the first embodiment. [Figure 18] FIG. 13 is a diagram for comparing production sequences. [Figure 19] FIG. 13 is a diagram illustrating a tally calculation. [Figure 20] FIG. 1 illustrates an operating device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] In production lines for personal computers, automobiles, etc., a mixed-flow production system is used in which multiple types of products are mixed on one production line and assembled into products. In a mixed-flow production system, different products flow in sequence on the same production line. In this case, if there is a bias in the production sequence of the products, it can have a significant impact on quality, cost, and productivity. For example, if the same product is produced repeatedly, workers' concentration will decrease, which may lead to mistakes. In addition, if the same product is produced repeatedly, there will be an unevenness in the frequency of parts supply, which may result in parts running out of stock.

[0011] Therefore, in a mixed-flow production system, it is desirable to plan the production sequence taking into consideration constraints (specification constraints) that prevent the various specifications of products from appearing unevenly. On the other hand, it is also necessary to consider constraints (time constraints) that a certain product can only be produced within a certain period due to production plans and worker shifts.

[0012] In recent years, as products become more diverse, specification constraints have become more complex, making it difficult to plan a production sequence that satisfies specification constraints while also respecting time constraints.

[0013] Fig. 1 is a diagram illustrating an example of product data. For example, as illustrated in Fig. 1, specification constraints are defined for each of products A to J. For example, products A, D, F, and I require processing of the same specification α. ​​Products C, F, G, and H require processing of the same specification β. Products B and J require processing of the same specifications α and β.

[0014] In addition, a period constraint is set for each of products A to J. For example, products A to C need to be put into the production line at any of slots 1 to 3. Products D and E need to be put into the production line at any of slots 4 to 8. Products F and G need to be put into the production line at any of slots 4 to 10. Product H needs to be put into the production line at any of slots 6 to 10. Products I and J need to be put into the production line at any of slots 8 to 10.

[0015] Here, the slot represents an input time into the production line. The slot number is the number of the input time, and corresponds to the input order into the production line. Therefore, the slot corresponds to the processing time in the production line. The fact that a slot is determined for each product corresponds to the fact that the constraints of the processing period in each production line are determined. For each product, the processing period includes one or more slots. In the example of FIG. 1, the processing period includes multiple slots for each product. In one slot, one product is input into the production line. For example, as illustrated in FIG. 2, on April 1, one product is input in the input time of slot 1, one product is input in the next input time of slot 2, and one product is input in the next input time of slot 3. The number of slots per day does not have to be constant. In the example of FIG. 1, the number of slots on April 1 and April 3 is three, and the number of slots on April 2 is four.

[0016] Figure 3 is an example of a production sequence plan. In the example of Figure 3, product B is inserted into slot 1 of the production line, product A into slot 2, product C into slot 3, and product F into slot 4. In the example of Figure 3, the production sequence is determined so that products with the same specifications are not inserted consecutively as much as possible.

[0017] Here, we will explain the target tracking method as an example of a method for determining the production sequence so that the same specifications do not appear consecutively as much as possible. The target tracking method is a method for determining the order in which products appear on the production line so that the cumulative number of appearances of each specification on the line approaches the target number of appearances as much as possible. For example, the target number of appearances is calculated as the average number of appearances (= number of appearances of the specification / total number of slots).

[0018] For example, it is assumed that the specifications of each product are determined as shown in FIG. 1. In this case, FIG. 4 shows an example in which the production sequence is determined from slot 1 to slot 4 so that the cumulative appearance count of each specification is as close as possible to the target appearance count. As illustrated in FIG. 4, the production sequence of products is determined from slot 1 to slot 4 so that the cumulative appearance count of specification α is as close as possible to the target appearance count. In addition, the production sequence of products is determined from slot 1 to slot 4 so that the cumulative appearance count of specification β is as close as possible to the target appearance count.

[0019] FIG. 5 is a diagram illustrating the criteria for selecting a product for slot 5. As illustrated in FIG. 5, the error (root-sum-of-squares) of each specification when the following product is selected is calculated. In this case, the product is selected from among products E, G, and H having the specification β that minimizes the error. In this way, the cumulative appearance count of each specification approaches the target appearance count.

[0020] However, such target tracking methods only consider the specification constraints and cannot respect the duration constraints.

[0021] Therefore, a method of creating a target appearance count for the target tracking method for each period constraint will be considered. For example, slots are grouped into sections by period constraints, and a target appearance count is created for each group. For example, as illustrated in FIG. 6, products A to D are inserted into any of slots 1 to 4, and products E to H are inserted into any of slots 5 to 8. In this case, the target appearance count for a certain section is set to (number of specifications in that section) / (number of slots in that section). For example, the target appearance count for slots 1 to 4 is (number of specifications from slot 1 to slot 4) / 4. In this case, the production sequence can be determined by the target tracking method, as illustrated in FIG. 6.

[0022] However, the example in Figure 6 shows a case where there is no partial overlap in the time constraints of each product. For example, if there is only a partial overlap between two time constraints, it will be impossible to determine a production sequence that satisfies the specification constraints unless the products to be assigned to each interval are appropriately determined in advance.

[0023] Fig. 7 is a diagram illustrating a case where there is partial overlap in the period constraints of each product. In the example of Fig. 7, the period constraints of products A to D are slot 1 to slot 5, and the period constraints of products E to H are slot 4 to slot 8. In this case, there is partial overlap between products A to D and products E to H for slot 4 and slot 5.

[0024] In this case, the target appearance count cannot be derived unless the number of specifications that fall within each interval is known, so product allocation is required in advance. Also, if the product allocation is not appropriate, the target appearance count will be incorrect and the specification constraints will not be satisfied. In this way, when there is partial overlap in the period constraints, the target tracking method cannot determine the production sequence.

[0025] Therefore, in order to obtain a production sequence that satisfies the specification constraints while observing the period constraints, it is possible to try all patterns of products to be assigned to each section. However, this would result in an enormous amount of calculation time. For example, in the case of automobile production, since products are manufactured on the scale of 1,000 units per day, production period constraints are set for each vehicle, and the respective period constraints may partially overlap. For example, in the case of the product data in Figure 8, there are approximately 50 product allocation patterns for each section. 950 × 50! ways, so if we assume that one target tracking method takes 1 second, it will take about 10 1584 It will take seconds.

[0026] In the following embodiment, an example will be described in which a production sequence can be calculated in a short time even if there is partial overlap in the period constraints of each product. EXAMPLES

[0027] First, the principle of the first embodiment will be described. It is assumed that product data is defined as described in Fig. 1. In the example in Fig. 1, the period constraints of products A to D are slot 1 to slot 5, and the period constraints of products E to H are slot 4 to slot 8. In this case, slot 4 and slot 5 partially overlap between products A to D and products E to H.

[0028] In this case, the probability distribution of each specification taking into account the time constraint is calculated for each product, as shown in the example of Fig. 9. For example, the sum of the probabilities of each specification for each slot number is calculated, and the cumulative sum of these is calculated to generate the target appearance count taking into account the time constraint.

[0029] For example, for product A, the specification is specification α only, so the probability for each of slots 1 to 5 for specification α is 1 / 5 = 0.2. In other words, the reciprocal of the number of slots is obtained. For specification β, the probability in each slot is 0. For product B, the specifications are specification α and specification β, so the probability for each of slots 1 to 5 for specification α is 1 / 5 = 0.2, and for specification β, the probability in each slot is 0. For product H, the specification is specification β only, so the probability for each of slots 4 to 8 for specification β is 1 / 5 = 0.2. For specification α, the probability in each slot is 0. Similarly, calculate the probabilities for other products.

[0030] Next, for each specification, the probabilities in each slot are summed. Next, for each slot number, the cumulative sums of the probabilities are summed for each specification. For example, for specification α, the sum of the probabilities in slot 1 is 0.6, and the sum of the probabilities in slot 2 is 0.6, so the cumulative sum of the probabilities in slot 2 is 1.2. The sum of the probabilities in slot 3 is 0.6, so the cumulative sum of the probabilities in slot 3 is 1.8. The sum of the probabilities in slot 4 is 0.8, so the cumulative sum of the probabilities in slot 4 is 2.6. In this way, the cumulative sums are obtained.

[0031] The obtained cumulative sum is treated as the target appearance frequency, and the production sequence is planned by using the target tracking method.

[0032] In this way, according to the first embodiment, in planning a production sequence for a mixed-flow production system, even if the period constraints partially overlap, it is possible to plan a production sequence that satisfies the specification constraints while observing the period constraints. Also, it is possible to obtain a production sequence that satisfies the period constraints while maintaining the specification constraints in the target tracking method. Also, since it is not necessary to consider case-by-case distinctions for the number of products that fall into each section, it does not require a huge amount of calculation time. As a result, it is possible to improve the quality, cost, and productivity of the production line for a mixed-flow production system.

[0033] Fig. 10(a) is a block diagram illustrating an example of an overall configuration of an information processing device 100 according to Example 1. As illustrated in Fig. 10(a), the information processing device 100 includes a product information storage unit 10, a probability calculation unit 20, a cumulative sum calculation unit 30, a specification unit 40, an output unit 50, and the like.

[0034] Fig. 10(b) is a block diagram illustrating an example of a hardware configuration of the information processing device 100. As illustrated in Fig. 10(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.

[0035] 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 a program executed by the CPU 101, data processed by the CPU 101, and the like. The storage device 103 is a non-volatile storage device. For example, a ROM (Read Only Memory), a solid state drive (SSD) such as a flash memory, or a hard disk driven by a hard disk drive can be used as the storage device 103. 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). The CPU 101 executes the planning program to realize a product information storage unit 10, a probability calculation unit 20, a cumulative sum calculation unit 30, a specification unit 40, and an output unit 50. It should be noted that the product information storage unit 10, the probability calculation unit 20, the cumulative sum calculation unit 30, the identification unit 40, and the output unit 50 may be implemented using hardware such as dedicated circuits.

[0036] Fig. 11 is a flowchart showing an example of processing executed by the information processing device 100. The probability calculation unit 20 acquires product data from the product information storage unit 10 (step S1). As described in Fig. 1, the product data has a specification constraint and a period constraint associated with the product ID. For example, the user can input the product ID, the specification constraint, and the period constraint using the input device 104.

[0037] Next, the probability calculation unit 20 uses the product data acquired in step S1 to calculate a probability distribution for each specification based on the period constraint (step S2).

[0038] Next, the cumulative sum calculation unit 30 calculates the sum of the probabilities of each specification for each slot number using the probability distribution calculated in step S2, and calculates the cumulative sum to calculate the target appearance count (step S3).

[0039] Next, the identifying unit 40 identifies the production sequence of the products in the production line based on the target appearance frequency calculated in step 3 (step S4).

[0040] Next, the output unit 50 outputs the production sequence identified in step S4 (step S5). For example, the output production sequence is displayed on the display device 105.

[0041] Fig. 12 is a diagram illustrating a flowchart showing details of step S2. As illustrated in Fig. 12, the probability calculation unit 20 inputs each product ID into Prods representing a set of product IDs, inputs the specifications of each product into Conds representing a set of specifications of each product, and inputs the period constraints of each product into Times representing a set of period constraints of each product (step S11).

[0042] Next, the probability calculation unit 20 calculates the number of products Num_Slots from Prods (step S12). The number of products Num_Slots is the number of products included in Prods, and corresponds to the number of slots in which the products are input into the production line.

[0043] Next, the probability calculation unit 20 extracts all specifications All_Conds from Conds without overlapping (step S13).

[0044] Next, the probability calculation unit 20 creates a table P r,c,t is prepared (step S14).

[0045] Next, the probability calculation unit 20 selects one product from Prods (step S15). The selected product is designated as product r.

[0046] Next, the probability calculation unit 20 extracts the specifications of the product r from Conds and extracts the period constraints of the product r from Times (step S16). r The extracted period constraint is called period constraint T r Let us assume that.

[0047] Next, the probability calculation unit 20 selects one specification from All_Conds (step S17). The selected specification is designated as specification c.

[0048] Next, the probability calculation unit 20 calculates the product r, the specification c, and the specification C of the product r. r , the duration constraint T of product r r Calculate the probability for each slot number t from the table P r,c,t (Step S18). Note that the slot number t is a number ranging from 1 to NumSlots.

[0049] Steps S17 and S18 are executed for all specifications, and when they are completed, the loop ends. This allows the probability distribution of each specification to be calculated. Steps S15 to S18 are executed for all products, and when they are completed, the loop ends. This allows the probability distribution of each specification to be calculated for each product.

[0050] 13 is a diagram illustrating a flowchart showing details of step S3. As illustrated in FIG. 13, the cumulative sum calculation unit 30 stores a table PS c、t is prepared (step S21).

[0051] Next, the cumulative sum calculation unit 30 selects one specification from All_Conds (step S22). The selected specification is designated as specification c.

[0052] Next, the cumulative sum calculation unit 30 selects one of the slot numbers (step S23). The selected slot number is set as slot number t.

[0053] Next, the cumulative sum calculation unit 30 calculates the P.S. c,t =Σ r∈Prods P r,c,t Calculate (step S24).

[0054] Steps S23 and S24 are executed for all slot numbers, and the loop ends when they are completed. This causes the probabilities of each slot to be summed up. Steps S22 to S24 are executed for all specifications, and the loop ends when they are completed. This causes the probabilities of each slot to be summed up for each specification.

[0055] Next, the cumulative sum calculation unit 30 creates a table ACC for storing the cumulative sum for the specification c and the slot number t. c,t is prepared (step S25).

[0056] Next, the cumulative sum calculation unit 30 selects one specification from All_Conds (step S26). The selected specification is designated as specification c.

[0057] Next, the cumulative sum calculation unit 30 selects one slot number (step S27). The selected slot number is set as slot number t.

[0058] Next, the cumulative sum calculation unit 30 calculates Σ t´≦t P.S.c,t´ Calculate the table ACC c,t (step S28).

[0059] Steps S27 and S28 are executed for all slot numbers, and the loop ends when they are completed. This causes the probabilities of each slot to be summed up. Also, steps S26 to S28 are executed for all specifications, and the loop ends when they are completed. This causes the cumulative sums of each slot to be summed up for each specification.

[0060] Next, the cumulative sum calculation unit 30 calculates ACC c,t is output as the target appearance frequency (step S29).

[0061] Fig. 14 is a diagram illustrating a flowchart showing details of step S4. As illustrated in Fig. 14, the identification unit 40 inputs each product ID to Prods representing a set of product IDs, and inputs the specifications of each product to Conds representing a set of specifications of each product (step S31).

[0062] Next, the specification unit 40 inputs the period constraints of each product into Times, which represents a set of period constraints of each product (step S32).

[0063] Next, the specifying unit 40 inputs the target number of appearances of each specification into Goal_Lines that represents a set of the target number of appearances of each specification (step S33).

[0064] Next, the specifying unit 40 calculates the number of products Num_Slots from Prods (step S34). The number of products Num_Slots is the number of products included in Prods, and corresponds to the number of slots into which the products are input on the production line.

[0065] Next, the identifying unit 40 extracts all specifications All_Conds from Conds without overlapping (step S35).

[0066] Next, the identifying unit 40 groups the products for each combination of specifications, and inputs the grouped products into Cond_Groups, which represents a set of groups (step S36). For example, in the product data of FIG. 1, a group of only specification α, a group of only specification β, and a group of specifications α and β are created.

[0067] Next, the specification unit 40 prepares a structure Count for counting the number of times each specification appears (step S37).

[0068] Next, the specifying unit 40 prepares an empty array Order for storing the production sequence in the production line (step S38).

[0069] Next, the specification unit 40 sets the index to "0" (step S39).

[0070] Next, the identifying unit 40 determines whether or not Index is smaller than the number of products Num_Slots (step S40).

[0071] If the determination in step S40 is "Yes," the identification unit 40 executes a sequence identification process (step S41). The sequence identification process will be described in detail later.

[0072] Next, the specification unit 40 sets the value of Index to Index+1 (step S42). After that, the process is executed again from step S40.

[0073] If the result of the determination in step S40 is "No," the output unit 50 outputs the Order as the production sequence in the production line (step S43).

[0074] Fig. 15 is a diagram illustrating an example of a flowchart showing details of the order specification process in step S41. As illustrated in Fig. 15, the specification unit 40 acquires the target appearance count for each specification at the Index number from Goal_Lines (step S51). The acquired target appearance count is set as the target appearance count Goals.

[0075] Next, the specifying unit 40 prepares a structure Err for storing the error of each specification combination (step S52).

[0076] Next, the specifying unit 40 selects one specification combination from Cond_Groups (step S53). The selected combination is called combination comb.

[0077] Next, the specification unit 40 sets Diff to 0 (step S54).

[0078] Next, the identifying unit 40 selects one specification from All_Conds (step S55). The selected specification is designated as specification c.

[0079] Next, the identifying unit 40 determines whether the specification c selected in step S55 is included in the comb selected in step S53 (step S56).

[0080] If the result of the determination in step S56 is “Yes”, the determination unit 40 calculates Diff as follows: Diff+(Goals[c]−Count[c]−1) 2 (step S57).

[0081] If the determination in step S56 is "No", the determination unit 40 calculates Diff as Diff+(Goals[c]-Count[c]). 2 (step S58).

[0082] Note that steps S55 to S58 are executed for all specifications, and when the steps are completed, the loop ends. In this way, the sum of squares for each specification is calculated.

[0083] Next, the specification unit 40 calculates the square root of Diff, sqrt(Diff), as Err[comb] (step S59).

[0084] Steps S53 to S59 are executed for all specification combinations, and when the steps are completed, the loop ends. In this way, the error for each specification combination is calculated.

[0085] Next, the identifying unit 40 selects one specification combination with the smallest error from Err (step S60). The selected specification combination is designated as Target_comb.

[0086] Next, the identifying unit 40 selects one product that satisfies Times from Cond_Groups[Target_comb] (step S61). The selected product is designated as Target_Prod.

[0087] Next, the specification unit 40 adds Target_Prod to the end of Order (step S62).

[0088] Next, an example of calculating the production sequence according to this embodiment will be described. The product data explained in FIG. 1 was used. FIG. 16 shows the probability distribution calculated for the product data in FIG. 1. For each product, the probability P r,c,t Specifically, the probability of specification α and specification β in each slot is calculated.

[0089] The upper part of Fig. 17 shows the probability P c,t The middle part of Fig. 17 shows the cumulative sum ACC for specification c and slot number t. c,t The lower part of Fig. 17 shows the production sequence planned by the method of the first embodiment.

[0090] The top part of Figure 18 shows the calculation results in the bottom part of Figure 17 with the specifications of each product added. As shown in the top part of Figure 18, each specification is distributed. This production sequence also satisfies the period constraints in Figure 1.

[0091] The middle part of Fig. 18 is an example of a production sequence calculated using the goal tracking method that does not consider time constraints. As shown in the middle part of Fig. 18, the specifications are distributed, but product J does not comply with the time constraint in Fig. 1. In this way, with the goal tracking method that does not consider time constraints, there may be products that do not comply with the time constraint.

[0092] The bottom part of Fig. 18 shows an example of calculating the production sequence by simply applying the period constraint to the goal tracking method. As shown in the bottom part of Fig. 18, the calculated production sequence satisfies the period constraint, but the specifications cannot be distributed.

[0093] From these results, it can be seen that the method according to this embodiment can obtain a production sequence that distributes specifications while observing the period constraints, without trying all patterns of specification allocation to each section.

[0094] In the above embodiment, the target tracking method is applied when the production sequence is specified, but the present invention is not limited to this. For example, tally calculation may be applied when the production sequence is specified. Tally calculation is a method for determining the order of appearance of products in order on a production line so that the cumulative appearance frequency of the specification combination of each product does not deviate from the target appearance frequency, as illustrated in FIG. 19. In this case, when the product of slot number 5 is specified, the difference from the target appearance frequency is 1.6-2=-0.4 for specification combination α, 1.6-1=0.6 for specification combination β, and 0.8-1=-0.2 for specification combination α and β. Therefore, any one of products E, G, and H having the specification β with the largest difference is selected. The above embodiment may be applied to such tally calculation when the production sequence is specified.

[0095] In each of the above examples, the results output by the output unit 50 are output to the display device 105, but may be output to the operation device 200. FIG. 20 is a block diagram illustrating this case. The operation device 200 is a belt conveyor for conveying each type to each device in each process in a production line. As illustrated in FIG. 20, the results output by the output unit 50 are output to the operation device 200. The operation device 200 operates so as to realize the optimal solution received from the output unit 50. The operation device 200 includes a CPU, a RAM, a storage device, etc., and for example, the storage device stores a control program for controlling the operation of the operation device 200, the RAM stores the optimal solution received from the output unit 50, and the CPU controls the operation of the operation device 200 based on the control program and the optimal solution.

[0096] In each of the above examples, the multiple products put into the production line are an example of multiple objects. The slots are an example of multiple processing times with a set sequence. Specifications α and β are examples of processing specifications set for each product. The slots set for each product are an example of a processing period. This is an example of a probability calculation unit in which the probability calculation unit 20 calculates, for each processing time, a probability obtained as the reciprocal of the number of processing times included in the processing period for each processing specification of the multiple objects, and calculates the sum of the probabilities of the processing specifications for each processing time. This is an example of a cumulative sum calculation unit in which the cumulative sum calculation unit 30 calculates the cumulative sum of the sums of probabilities for the processing times. This is an example of an identification unit in which the identification unit 40 uses the cumulative sum to identify the processing times of the multiple objects.

[0097] Although the embodiment of the present invention has been described in detail above, the present invention is not limited to the specific embodiment, and various modifications and variations are possible within the scope of the gist of the present invention described in the claims. (Additional Note) (Appendix 1) Each of a plurality of objects is processed at one of a plurality of processing times having a set sequence, processing specifications are set for each of the plurality of objects, a processing period is set for each of the plurality of objects during which the processing is performed, and the processing period includes one or more of the processing times, On the computer, calculating a probability of obtaining each of the processing specifications of the plurality of objects as an inverse of the number of the processing times included in the processing period for each processing time; calculating a sum of probabilities of the processing specifications for each of the processing times; calculating a cumulative sum of the sums of the probabilities for the processing time; and specifying the processing times of the plurality of objects using the cumulative sum. (Appendix 2) 2. The computational program according to claim 1, wherein the processing periods partially overlap for at least two of the plurality of objects. (Appendix 3) The calculation program according to claim 1, characterized in that when sequentially identifying the processing times of the plurality of objects, the relationship between the processing times and the cumulative sum is used as a criterion, and the next object is selected using a difference between the cumulative number of occurrences of the processing specifications when the next object is selected. (Appendix 4) The calculation program according to claim 1, characterized in that, when sequentially identifying the processing times of the plurality of objects, the relationship between the processing times and the cumulative sum is used as a criterion, and the next object is selected so that the sum of squares of the difference between the cumulative number of occurrences of the processing specifications when the next object is selected is minimized. (Appendix 5) Each of a plurality of objects is processed at one of a plurality of processing times having a set sequence, processing specifications are set for each of the plurality of objects, a processing period is set for each of the plurality of objects during which the processing is performed, and the processing period includes one or more of the processing times, The computer calculating a probability of obtaining each of the processing specifications of the plurality of objects as an inverse of the number of the processing times included in the processing period for each processing time; calculating a sum of the probabilities of the processing specifications for each of the processing times; calculating a cumulative sum of the sums of the probabilities for the processing time; and using the cumulative sum to identify the processing times of the plurality of objects. (Appendix 6) 6. The calculation method according to claim 5, wherein the processing periods partially overlap for at least two of the plurality of objects. (Appendix 7) The calculation method according to claim 5, characterized in that when sequentially identifying the processing times of the plurality of objects, the relationship between the processing times and the cumulative sum is used as a criterion, and the next object is selected using a difference between the cumulative number of occurrences of the processing specifications when the next object is selected. (Appendix 8) The calculation method according to claim 5, characterized in that when sequentially identifying the processing times of the plurality of objects, the relationship between the processing times and the cumulative sum is used as a criterion, and the next object is selected so that the sum of squares of the difference between the cumulative number of appearances of the processing specifications when the next object is selected is minimized. (Appendix 9) a probability calculation unit that calculates, for each processing time, a probability that the processing specifications for the plurality of objects are obtained as the reciprocal of the number of the processing times included in the processing period, under the condition that each of the plurality of objects is processed at one of a plurality of processing times having a set order, a processing specification is set for each of the plurality of objects, a processing period is set for each of the plurality of objects in which the processing is performed, and the processing period includes one or more of the processing times, and calculates a sum of the probabilities of the processing specifications for each of the processing times; a cumulative sum calculation unit that calculates a cumulative sum of the sums of the probabilities for the processing time; an identification unit that identifies the processing times of the plurality of objects by using the cumulative sum. (Appendix 10) 10. The information processing device according to claim 9, wherein the processing periods partially overlap for at least two of the plurality of objects. (Appendix 11) The information processing device described in Appendix 9, characterized in that when sequentially identifying the processing times of the multiple objects, the identification unit uses the relationship between the processing time and the cumulative sum as a criterion, and selects the next object using the difference between the cumulative number of appearances of the processing specification when the next object is selected. (Appendix 12) The information processing device described in Appendix 9, characterized in that when sequentially identifying the processing times of the multiple objects, the identification unit uses the relationship between the processing time and the cumulative sum as a criterion, and selects the next object so that when the next object is selected, the sum of squares of the difference between the cumulative number of appearances of the processing specification and the processing time is minimized. [Explanation of symbols]

[0098] 10 Product information storage section 20 Probability Calculation Section 30 Cumulative sum calculation section 40 Specific section 50 Output section 100 Information processing device 101 CPU 102 RAM 103 Storage device 104 Input Devices 105 Display device 200 Operating equipment

Claims

1. Each of a plurality of objects is processed at one of a plurality of processing periods having a set sequence, processing specifications are set for each of the plurality of objects, a processing period is set for each of the plurality of objects during which the processing is performed, and the processing period includes one or more of the processing periods, On the computer, calculating a probability of obtaining each of the processing specifications of the plurality of objects as an inverse of the number of the processing times included in the processing period for each processing time; calculating a sum of the probabilities of the processing specifications for each of the processing times; calculating a cumulative sum of the sums of the probabilities for the processing time; and specifying the processing times of the plurality of objects using the cumulative sum.

2. 2. The computing program according to claim 1, wherein the processing periods for at least two of the plurality of objects partially overlap.

3. The calculation program according to claim 1, characterized in that, when sequentially identifying the processing times of the multiple objects, the relationship between the processing time and the cumulative sum is used as a criterion, and the next object is selected using the difference between the cumulative number of occurrences of the processing specification when the next object is selected.

4. The calculation program according to claim 1, characterized in that, when sequentially identifying the processing times of the multiple objects, the relationship between the processing time and the cumulative sum is used as a criterion, and the next object is selected so that the sum of squares of the difference between the cumulative number of occurrences of the processing specification and the processing time when the next object is selected is minimized.

5. Each of a plurality of objects is processed at one of a plurality of processing periods having a set sequence, processing specifications are set for each of the plurality of objects, a processing period is set for each of the plurality of objects during which the processing is performed, and the processing period includes one or more of the processing periods, The computer calculating a probability of obtaining each of the processing specifications of the plurality of objects as an inverse of the number of the processing times included in the processing period for each processing time; calculating a sum of the probabilities of the processing specifications for each of the processing times; calculating a cumulative sum of the sums of the probabilities for the processing time; and using the cumulative sum to identify the processing times of the plurality of objects.

6. a probability calculation unit that calculates, for each processing time, a probability that the processing specifications for the plurality of objects are obtained as the reciprocal of the number of the processing times included in the processing period, under the condition that each of the plurality of objects is processed at one of a plurality of processing times having a set order, a processing specification is set for each of the plurality of objects, a processing period is set for each of the plurality of objects in which the processing is performed, and the processing period includes one or more of the processing times, and calculates a sum of the probabilities of the processing specifications for each of the processing times; a cumulative sum calculation unit that calculates a cumulative sum of the sums of the probabilities for the processing time; an identification unit that identifies the processing times of the plurality of objects by using the cumulative sum.

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