Design support device, design support method, and computer program

Abstract

To make it possible to efficiently design a highly productive production line.SOLUTION: A design support device for supporting a design of a production line constructed by a combination of a plurality of lines which includes a plurality of steps executed in sequence to produce a product, is characterized by: acquiring production planning information of a product, reference information which is information about steps that make up a production line for a similar product, and constraint information; using reference information of each of the steps, an order of each step, and the constraint information to calculate a predetermined evaluation function for each combination of the order of each step on the line; using a calculation result of the evaluation function on the line, the order of each line, and the constraint information to calculate a predetermined evaluation function for each combination of the order of each line on the production line; calculating a degree of achievement of an index regarding a product production per predetermined period; and repeating a line optimization calculation, a production line optimizing calculation, and an achievement degree calculation until the achievement degree of the index reaches a target value.SELECTED DRAWING: Figure 4

Description

Technical Field

[0001] The present disclosure relates to a design support device and the like that support the design of a production line composed of a combination of a plurality of lines for producing a product, including a plurality of processes.

Background Art

[0002] An example of a technology for supporting process design optimization by a computer is disclosed in Patent Document 1 and Patent Document 2.

[0003] Patent Document 1 discloses a technology for automating the process design of a processing line. The process design automation module calculates the cutting time for each process of each processing site and extracts all combination patterns of the cutting times for each process of each processing site. Further, the process design automation module extracts a candidate group from all the combination patterns based on predetermined conditions. Furthermore, the process design automation module selects the combination with the shortest working time from the candidate group as the optimal combination and sequentially assigns that combination to the processing machines.

[0004] Patent Document 2 discloses a technology for selecting an optimal one from a plurality of assembly lines when there are a plurality of assembly lines for assembling a printed circuit board. The processing device first extracts the bottleneck process that requires the most working time among the processes in the plurality of assembly lines. Next, the processing device compares the working times of the bottleneck processes of each assembly line. Then, the processing device selects the assembly line with the shortest working time of the bottleneck process as the optimal one.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Patent Document 2

Summary of the Invention

[0006] A production line is composed of a combination of lines, each consisting of multiple processes. The technologies described in Patent Documents 1 and 2 above only optimize the combination of processes that make up the line. However, some processes cannot be rearranged, and the optimized combination of processes does not necessarily match the optimized line combination. Therefore, optimizing only the processes that make up the line does not make it possible to design a highly productive production line.

[0007] Even if we use computers to optimize the combination of both the production line and the process, simply calculating by brute force would consume an enormous amount of computation and be inefficient.

[0008] This invention was made to solve the above problems and aims to provide a design support device, etc., that enables the efficient design of highly productive production lines. [Means for solving the problem]

[0009] To achieve the above objective, the design support device of the present invention is a design support device that supports the design of a production line consisting of a combination of multiple lines including multiple processes to be performed in order for producing a product, and includes an acquisition means for acquiring product production plan information, reference information which is information relating to processes constituting a production line for a similar product whose production plan information is similar to the production plan information of the product, and constraint information; a process sequence optimization means which performs calculation of a predetermined evaluation function for each combination of the order of each process in the line using the reference information of each process, the order of each process, and constraint conditions based on the constraint information; and the calculation result of the process sequence optimization means, the order of each line, and the constraint information The system includes a line sequence optimization means that performs calculations of a predetermined evaluation function for each combination of the order of each line in the production line using constraints based on reports, and an achievement calculation means that calculates the degree of achievement of an indicator related to the production of the product per predetermined period, which is an indicator for the production line calculated by the line sequence optimization means, and if the degree of achievement of the indicator does not reach a target value, the calculation of line optimization by the process sequence optimization means, the calculation of production line optimization by the line sequence optimization means, and the calculation of the degree of achievement of the indicator by the achievement calculation means are repeated until the degree of achievement of the indicator reaches a target value.

[0010] The present invention provides a design support method for a production line consisting of a combination of multiple lines, each containing multiple processes executed in sequence, for producing a product. The design support device acquires product production plan information, reference information which is information about processes constituting a production line for a similar product whose production plan information is similar to that of the product, and constraint information. Using the reference information for each process, the sequence of each process, and constraint conditions based on the constraint information, it calculates a predetermined evaluation function for each combination of process sequences in the line. Using the calculation results of the evaluation function in the line, the sequence of each line, and constraint conditions based on the constraint information, it calculates the degree of achievement of an indicator related to product production per predetermined period, which is the target indicator for the calculated production line. If the degree of achievement of the indicator does not reach a target value, the calculation of line optimization, the calculation of production line optimization, and the calculation of the degree of achievement of the indicator are repeated until the degree of achievement of the indicator reaches a target value.

[0011] The computer program of the present invention causes a design support device, which assists a computer in designing a production line consisting of a combination of multiple lines, each containing multiple processes executed in sequence for producing a product, to perform the following processes: acquiring product production plan information, reference information which is information about processes constituting a production line for a similar product whose production plan information is similar to the product's production plan information, and constraint information; performing a predetermined evaluation function calculation for each combination of process sequences in the line using the reference information for each process, the sequence of each process, and constraint conditions based on the constraint information; performing a predetermined evaluation function calculation for each combination of line sequences in the production line using the calculation result of the evaluation function in the line, the sequence of each line, and constraint conditions based on the constraint information; calculating the degree of achievement of an indicator for the product per predetermined period, which is an indicator targeted by the calculated production line; and, if the degree of achievement of the indicator does not reach a target value, repeating the process of calculating line optimization, the process of calculating production line optimization, and the process of calculating the degree of achievement of the indicator until the degree of achievement of the indicator reaches a target value. [Effects of the Invention]

[0012] According to the present invention, it becomes possible to efficiently design highly productive production lines. [Brief explanation of the drawing]

[0013] [Figure 1] This figure shows an example of the configuration of the information processing system 1 in the first embodiment. [Figure 2] This figure illustrates the outline of the line design and process design in the first embodiment. [Figure 3] This figure shows an example of production plan information stored in the storage device 300 in the first embodiment. [Figure 4] This is a block diagram showing an example configuration of the design support device 100 in the first embodiment. [Figure 5] This figure shows an example of reference information acquired by the acquisition unit 101 in the first embodiment. [Figure 6] This flowchart shows an example of the operation of the design support device 100 in the first embodiment. [Figure 7] This figure shows an example of the operation of the process sequence optimization unit 102 in the first embodiment. [Figure 8] This figure shows an example of an individual generated by the process sequence optimization unit 102 in the first embodiment. [Figure 9A] This figure shows the specific selection operations performed by the process sequence optimization unit 102 in the first embodiment. [Figure 9B] This figure shows the specific crossover operation performed by the process sequence optimization unit 102 in the first embodiment. [Figure 9C] This figure shows the specific mutation operations performed by the process sequence optimization unit 102 in the first embodiment. [Figure 10] This figure shows an example of the operation of the line order optimization unit 103 in the first embodiment. [Figure 11] This figure shows an example of an individual generated by the line order optimization unit 103 in the first embodiment. [Figure 12] It is a diagram showing a configuration example of the design support device 100 in the second embodiment. [Figure 13] It is a diagram showing an example in which the process design unit 105 in the second embodiment extracts a process common to a plurality of products. [Figure 14] It is a flowchart showing an operation example of the design support device 100 in the second embodiment. [Figure 15] It is a diagram showing an example of a hardware configuration in which the design support device 100 in the present disclosure is realized by a computer device 10 including a processor. [Embodiments for Carrying Out the Invention]

[0014] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0015] [First Embodiment] FIG. 1 is a diagram showing a configuration example of the information processing system 1 in the first embodiment. The information processing system 1 is composed of a design support device 100, an input device 200, a storage device 300, and an output device 400.

[0016] The design support device 100 supports the design of a production line for producing a product, which consists of a combination of lines including a plurality of processes. The design support device 100 is connected to the input device 200, the storage device 300, and the output device 400 via an information communication network or directly.

[0017] In the present embodiment, when referring to a production line, it means a combination of lines designed for manufacturing a product. When simply referring to a line, it means a line constituting the production line. Also, in the present embodiment, support refers to an optimization process, that is, an operation using an optimization algorithm and the generation of an optimization operation result.

[0018] Figure 2 is a diagram illustrating the outline of the line design and process design of the first embodiment. The terms used in this embodiment will be explained with reference to Figure 2.

[0019] In Figure 2, patterns 4 to 6 are examples of production lines. A production line consists of one or more combinations of lines. A combination refers to the arrangement of lines laid out on the floor. In Figure 2, patterns 1 to 3 are examples of lines. A line consists of a combination of multiple processes. One process consists of process information, as shown in Figure 5, for example. Process information includes, for example, process number, worker, part name, category, work name, work elements, equipment name, number of equipment, work time, and quality risk. A process may also include information related to the work of the process other than this information. Furthermore, the information included in a process as described above is just an example, and it is not necessary to include all information.

[0020] The input device 200 receives input from the user to the design support device 100. The input device 200 can be implemented, for example, by a keyboard, mouse, touch panel, etc. The input device 200 can receive, for example, a startup instruction for the design support device 100 from the user. Alternatively, the input device 200 can receive production plan information and manufacturing instruction information stored in the storage device 300 from the user. The content input via the input device 200 is output to the design support device 100. The input device 200 may include a communication circuit and receive production plan information and manufacturing instruction information via a network. Next, the production plan information and manufacturing instruction information will be described in detail.

[0021] Production planning information refers to information regarding the production plan for a pre-designed product. Specifically, production planning information includes initial process data corresponding to multiple processes that make up the production line, and information indicating the predetermined configuration of the production line for the manufacturing of the product. Production planning information includes information about the aforementioned processes, specifically information such as process number, worker, part name, category, work name, work element, equipment name, number of equipment, work time, and quality risk. Production planning information is stored in the storage device 300 in association with product information, such as product name and manufacturing date.

[0022] Manufacturing instruction information represents indicators related to product production. For example, manufacturing instruction information includes the types of products to be produced and the production quantity for each product during a given period.

[0023] In this embodiment, constraint information is used in the optimization calculation of the process described later. The constraint information is generated from the production plan information of the product under design. Constraint information is information that represents the conditions (constraint conditions described later) that restrict the production of the product under design. Figure 3 shows an example of constraint information stored in the storage device 300. As shown in Figure 3, the constraint information includes, for example, information on the number of units produced per day, the work system (shifts / day), the working hours per shift (hours / person), and the wage rate for direct labor costs (yen / hour). In addition to the information shown in Figure 3, the constraint information also includes, for example, information related to the product's manufacturing process and information related to the components that make up the product. This information is associated with each product and stored in the storage device 300 described later.

[0024] The storage device 300 stores information used by the design support device 100 during the optimization calculation process. Specifically, in addition to production plan information and constraint information, the storage device 300 stores the lines determined in the calculation process of the process sequence optimization unit 102 and the production lines determined in the calculation process of the line sequence optimization unit 103. As mentioned above, a production line consists of one line or a combination of multiple lines, and a line consists of a combination of multiple processes. In this embodiment, the calculation process refers to the process sequence optimization unit 102 or the line sequence optimization unit 103, and the loop processing process in the optimization calculation. The results obtained as appropriate during the loop processing are information obtained in the process of optimization, and specifically refer to information generated by the calculation in one loop process. In this embodiment, the design support device 100 repeats the line optimization calculation by the process sequence optimization unit 102 and the production line optimization calculation by the line sequence optimization unit 103 until the termination condition is met, as will be described later. That is, the lines determined in the calculation process and the production lines determined in the calculation process differ for each value of the loop counter i, which will be described later. Therefore, the production line determined during the calculation process is different from the optimized production line output to the output device 400.

[0025] The output device 400 displays the optimization calculation results of the design support device 100. That is, the output device 400 displays the optimized production line. For this reason, the output device 400 is equipped with a display unit (not shown), such as a display. The output device 400 receives the optimized production line, which is the optimization calculation result from the design support device 100, and displays the optimization calculation result on the display unit.

[0026] Next, the configuration of the design support device 100 in the first embodiment will be described in detail. Figure 4 is a block diagram showing the configuration of the design support device 100 in the first embodiment. Referring to Figure 4, the design support device 100 comprises an acquisition unit 101, a process sequence optimization unit 102, a line sequence optimization unit 103, and an achievement level calculation unit 104.

[0027] The acquisition unit 101 is an example of an acquisition means for acquiring production plan information, reference information, and constraint information, which are related to the production plan of the product under design. Reference information refers to information about the processes that constitute the production line of a similar product that is similar to the product. Specifically, the acquisition unit 101 acquires production plan information, reference information, and constraint information from the storage device 300 in response to a command from the process sequence optimization unit 102 or the line sequence optimization unit 103. The acquisition unit 101 may also acquire optimized line information obtained in the calculation process of the process sequence optimization unit 102 and supply it to the line sequence optimization unit 103 as an initial value. Alternatively, in the aforementioned loop processing, the acquisition unit 101 may supply optimized production line information obtained in the calculation process of the line sequence optimization unit 103 to the process sequence optimization unit 102.

[0028] Specifically, the acquisition unit 101 acquires similar products that are similar to the product, and reference information, which is information regarding the design of the production line for similar products, based on the production plan information acquired from the storage device 300. For example, similar products may be inferred from the production plan information of various products. For example, the production plan information of various products includes the product, the parts included in the product, the work process and sequence, the work time (work man-hours), and the equipment or tools used for the work. Alternatively, the production plan information of various products includes the product, the parts used to produce that product, and the number of parts. Note that the reference information may be changed during the optimization calculation process by the process sequence optimization unit 102. The acquisition unit 101 may acquire as reference information elements that bring the solution obtained during the calculation process closer to the optimal solution. Acquiring reference information of similar products leads to increased efficiency in the optimization calculation.

[0029] The acquisition of similar products and reference information by the acquisition unit 101 will be described in more detail below. The acquisition unit 101, for example, takes the production plan information shown in Figure 3 as input and uses a pre-prepared learning model to infer similar products to the product to be designed. The learning model is generated by taking the feature quantities of known production plan information for known products as input and performing machine learning using training data with the production plan information of known products similar to the known product as the ground truth. As the machine learning learning algorithm, deep learning using a neural network or other learning algorithms may be used.

[0030] Next, the acquisition unit 101 acquires information regarding the design of the production line of a similar product as reference information. The reference information may be selected from information regarding the design of the production line of a similar product. Figure 5 shows an example of reference information for a similar product X acquired by the acquisition unit 101. Similar product X is a similar product that is similar to the product for which design support is being provided. As shown in Figure 5, the reference information is associated with values ​​for items such as worker, part name, category, work name, work element, equipment name, number of equipment, work time, and quality risk for each process (process 1 to process 5), and is stored in the storage device 300. Here, a work element refers to a unit element when the work in manufacturing a product is divided. Specifically, a work element refers to assembly, screw tightening, sealing, inspection, and packaging, as shown in Figure 5. The above items held by the reference information are just examples and are not limited to these items. The reference information may also be a feature vector in a feature space or feature plane with any of the above items as axes. For example, the reference information may be an m-dimensional feature vector (where m is an integer greater than or equal to 2) with two or more of the following as axes: work process, part name, work time, tools and fixtures, and number of parts. The elements of the feature vector are not limited to those mentioned above.

[0031] The process sequence optimization unit 102 is an example of a process sequence optimization means that uses reference information for each process and constraint conditions based on constraint information to perform calculations on a predetermined evaluation function for each combination of process sequences in the line. Constraint conditions are limitations imposed when performing optimization to find the optimal solution of the objective function. In addition to the reference information mentioned above, one or more constraint conditions are set from the production plan information as needed. An example of the objective function in the process sequence optimization unit 102 is a calculation formula that finds the manufacturing process with the best production efficiency in terms of the number of items manufactured per day and / or the quantity of products, and an example of the decision variable is the production quantity for each item, but is not limited to these. Furthermore, in the calculation process following the initial provisional calculation, the process sequence optimization unit 102 sets the calculation result obtained by the optimization process of the line sequence optimization unit 103 as the objective function and performs optimization.

[0032] The line sequence optimization unit 103 is an example of a line optimization means that uses the calculation results of the process sequence optimization unit 102, the sequence of each line, and the constraint conditions based on the constraint information to calculate a predetermined evaluation function for each combination of line sequences in the production line.

[0033] The achievement calculation unit 104 is an example of an achievement calculation means that calculates the degree of achievement of an indicator related to product production per predetermined period, which is an indicator calculated by the line sequence optimization unit 103 for the production line. Specifically, the achievement calculation unit 104 calculates the degree of achievement of an indicator related to product production per predetermined period, which is an indicator calculated by the line sequence optimization unit 103 for the production line. An example of an indicator is the number of products to be designed produced per predetermined period by the production line calculated by the line sequence optimization unit 103. The process sequence optimization unit 102 and the line sequence optimization unit 103 repeat the calculation of line optimization and production line optimization until the degree of achievement of the indicator reaches the target value. In the following embodiments, the indicator will be described as the number of products produced.

[0034] Next, an example of the operation of the design support device 100 in the first embodiment will be described with reference to the flowchart in Figure 6.

[0035] The design support device 100, for example, starts processing by receiving a start command from the user via the input device 200.

[0036] First, the design support device 100 prepares a counter i to count the number of times the processes in steps S104 to S107 have been repeated. Then, the design support device 100 initializes the counter i by setting its initial value to, for example, 0 (step S101).

[0037] Next, the acquisition unit 101 acquires production plan information and constraint information from the storage device 300 (step S102).

[0038] Next, the acquisition unit 101 acquires information regarding the production plans of similar products as reference information based on the acquired production plan information (step S103).

[0039] Next, the process sequence optimization unit 102 optimizes the processes constituting the line using constraints based on the constraint information obtained in step S103 (step S104). The process sequence optimization unit 102 performs optimization using any optimization algorithm. In this embodiment, as an example, the case in which optimization is performed using a genetic algorithm will be described. The process sequence optimization unit 102 may use optimization algorithms such as simulated annealing or tabu search instead of a genetic algorithm. A genetic algorithm is a metaheuristic optimization method. In this method, the solution is updated by manual local search on a provisional solution to prevent the desired solution from becoming a local optimal solution. Therefore, by using a genetic algorithm as the optimization algorithm, a more optimal solution can be found in less computation time.

[0040] The specific process of step S104 will be explained with reference to the flowchart in Figure 7. Figure 7 is a diagram showing an example of the operation of the process sequence optimization unit 102.

[0041] The process sequence optimization unit 102 first prepares a counter j for counting the number of generations. Then, the process sequence optimization unit 102 initializes the counter j by setting its initial value to, for example, 0 (step S111).

[0042] Next, the process sequence optimization unit 102 randomly generates first-generation individuals using information about the multiple processes that constitute the line (step S112). Figure 8 shows an example of individuals generated by the process sequence optimization unit 102. Each individual represents a line. The genes of each individual represent process numbers (for example, processes 1 to 5 in Figure 5). The number of individuals n (where n is a natural number) to be generated is arbitrarily determined by the user. The number of individuals n may be input by the input device 200. In this embodiment, the genes of each individual are represented in decimal, but they may also be represented in binary.

[0043] Next, the process sequence optimization unit 102 calculates a score for each individual using an evaluation function (step S113). Possible evaluation functions include, for example, a function that calculates production efficiency in terms of the number of items manufactured per day or the quantity of products manufactured, as well as production lead time, the number of workers, etc. "Items manufactured" refers to the product name. If there are multiple evaluation functions, the user may assign weights to each evaluation function.

[0044] Next, the process sequence optimization unit 102 identifies individuals that do not satisfy the constraints as lethal genes. The constraints refer to the production plan information shown in Figure 3. For individuals identified as lethal genes, the process sequence optimization unit 102 deducts points from their score, for example. This prevents individuals identified as lethal genes from being selected when step S114 is executed.

[0045] Next, the process sequence optimization unit 102 generates the next generation of individuals by performing either selection, crossover, or mutation on the current generation of individuals (step S115). The number of individuals in the next generation is set to be equal to the number n of individuals generated in the previous generation. Whether selection, crossover, or mutation is performed on the individuals of the previous generation depends on the calculated score and a probability predetermined by the user.

[0046] Examples of specific operations for selection, crossover, and mutation will be explained with reference to Figures 9A to 9C. Note that the specific operations for selection, crossover, and mutation are not limited to those described below.

[0047] Figure 9A shows a specific selection operation performed by the process sequence optimization unit 102. For example, the process sequence optimization unit 102 copies a predetermined number of individuals (lines) L1 with high scores to the next generation of individuals (lines) L1'. This operation of copying individuals as they are based on the calculated score is called selection.

[0048] Figure 9B shows the specific operation of crossover performed by the process sequence optimization unit 102. The process sequence optimization unit 102 performs crossover in the following procedure, for example. First, the process sequence optimization unit 102 randomly selects two individuals L1 and L2. Next, the process sequence optimization unit 102 randomly selects two crossover points in the two individuals (lines). Then, the process sequence optimization unit 102 swaps the genes (process numbers) in the region enclosed by the crossover points to generate the next generation individuals L1' and L2'. This operation of swapping a portion of the genes between two arbitrarily selected individuals is called crossover.

[0049] Figure 9C illustrates the specific operation of mutation performed by the process sequence optimization unit 102. The process sequence optimization unit 102 performs mutation in the following procedure, for example: First, it randomly selects one individual L1. Next, the process sequence optimization unit 102 randomly selects two genes from the individual. Then, the process sequence optimization unit 102 swaps the selected genes to generate the next generation individual L1'. This operation, in which a portion of an individual's genes are randomly altered, is called mutation.

[0050] The process sequence optimization unit 102 repeats selection, crossover, or mutation until the number of individuals in the next generation reaches n (step S115).

[0051] Next, the process sequence optimization unit 102 makes the next generation individual the current generation individual (step S116).

[0052] Next, the process sequence optimization unit 102 determines whether or not a predetermined termination condition is met (step S117). The predetermined termination condition refers to conditions such as "terminate when the process has been repeated up to the Nth generation (where N is a natural number)" or "terminate when the scores of all individuals in the current generation meet the target value." The termination condition is predetermined by the user. If the predetermined termination condition is met (step S117 Yes), the process sequence optimization unit 102 terminates the process and proceeds to step S105. If the predetermined termination condition is not met (step S117 No), the process sequence optimization unit 102 increments the value of the counter (step S118) and returns to step S113. The process sequence optimization unit 102 repeats steps S113 to S118 until the predetermined termination condition is met.

[0053] After completing the process in step S104 in Figure 6, the process sequence optimization unit 102 saves the calculation result to the storage device 300 (step S105).

[0054] Next, the line sequence optimization unit 103 performs line optimization (step S106). The line sequence optimization unit 103 performs optimization using an arbitrary optimization algorithm of the same type as that used by the process sequence optimization unit 102. In this embodiment, as an example, the case in which optimization is performed using a genetic algorithm will be described.

[0055] The specific processing of step S106 will be explained with reference to the flowchart in Figure 10. Figure 10 shows an example of the operation of the line sequence optimization unit 103. The operation of the line sequence optimization unit 103 is largely the same as that of the process sequence optimization unit 102. In the following, the procedures that are the same as those of the process sequence optimization unit 102 will be omitted from the explanation, and only the procedures that differ from the operation of the process sequence optimization unit 102 will be explained.

[0056] First, the operation of step S122 differs from that of the process sequence optimization unit 102. In step S122, the line sequence optimization unit 103 generates first-generation individuals (production lines) using the lines obtained in the calculation process of the process sequence optimization unit 102 as genes. Figure 11 shows an example of an individual (production line) generated by the line sequence optimization unit 103. The line sequence optimization unit 103 generates individuals by using the lines obtained in the calculation process of the process sequence optimization unit 102 as genes and arranging them randomly. The line sequence optimization unit 103 may, for example, assign a number to each line that is a gene, as shown in Figure 11, and use the number assigned to that line as the gene.

[0057] Other operations of the line sequence optimization unit 103 are the same as those of the process sequence optimization unit 102. The line sequence optimization unit 103 terminates the process in step S106 if predetermined conditions are met in step S127.

[0058] After completing the process in step S106, the line order optimization unit 103 saves the calculation result of the line order optimization unit 103 to the storage device 300 (step S107).

[0059] Next, the achievement calculation unit 104 determines whether the result from the line sequence optimization unit 103 satisfies the termination condition (step S108). Here, the termination condition differs from the termination condition in the process sequence optimization unit 102; for example, it refers to the daily production quantity for each item meeting the target value. If the design support device 100 satisfies the termination condition (step S108 Yes), it terminates the process. If the design support device 100 does not satisfy the termination condition (step S108 No), it increments counter i (step S109) and returns to the process in step S104. The design support device 100 repeats the processes in steps S113 to S118 until the termination condition is met.

[0060] The design support device 100 in the first embodiment is configured as described above. Next, the effects of the first embodiment will be described.

[0061] As described above, the acquisition unit 101 acquires production plan information for the product, reference information which is information about the processes that constitute the production line of a similar product, and constraint information. The process sequence optimization unit 102 uses the reference information for each process, the sequence of each process, and the constraint information to calculate a predetermined evaluation function for each combination of process sequences. The line sequence optimization unit 103 uses the calculation results of the process sequence optimization unit 102, the sequence of each line, and the constraint information to calculate a predetermined evaluation function for each combination of line sequences. The achievement level calculation unit 104 calculates the degree of achievement of an indicator related to the production of the product per predetermined period. If the degree of achievement of the indicator does not reach the target value, the design support device 100 repeats the calculations of the process sequence optimization unit 102, the line sequence optimization unit 103, and the achievement level calculation unit 104 until the degree of achievement of the indicator reaches the target value. Therefore, the design support device 100 in the first embodiment makes it possible to efficiently design a highly productive production line.

[0062] The design support device 100 in the first embodiment may be modified as follows.

[0063] In this embodiment, the storage device 300 and the design support device 100 have been described as separate configurations, but the embodiment is not limited to this one. For example, the storage device 300 may be located inside the design support device 100.

[0064] The storage device 300 may also store the production lines optimized by the process sequence optimization unit 102 and the production lines optimized by the line sequence optimization unit 103 for each product. This allows for the accumulation of actual data, thereby strengthening manufacturing know-how. Furthermore, the accumulated actual data can be used as feedback for the design of new products.

[0065] In this embodiment, counters i, j, and k are assumed to start at 0 and then be incremented by 1 each time. However, they may also be assumed to start at a predetermined value and be decremented by 1 each time.

[0066] [Second Embodiment] A second embodiment of the present invention will be described below. In the second embodiment, a design support device for assisting in mixed-model production will be described. Mixed-model production is a method of producing multiple products in a mixed-model flow on a single production line. In this embodiment, as an example, the design procedure for producing two products, product A and product B, in a mixed-model flow will be described. In this embodiment, the number of products will be described as two, but the number of products can be any number and is not limited to two. In this description of the second embodiment, the same reference numerals will be used for parts that have the same names as in the first embodiment, and detailed explanations will be omitted.

[0067] The information processing system 1 in the second embodiment is composed of a design support device 100, an input device 200, a storage device 300, and an output device 400, similar to the first embodiment.

[0068] Figure 12 shows an example of the configuration of the design support device 100 in the second embodiment. In addition to the configuration of the first embodiment, the design support device 100 in the second embodiment is newly equipped with a process design unit 105.

[0069] The acquisition unit 101 acquires production plan information and reference information for each of the multiple products being designed.

[0070] First, the acquisition unit 101 obtains production plan information for product A and product B, as shown in Figure 3. Next, the acquisition unit 101 takes the production plan information as input and uses a pre-learned model to infer product A' and product B', which are similar products to product A and product B, respectively. Next, the acquisition unit 101 obtains information regarding the design of the production lines for product A' and product B' as reference information.

[0071] The process design unit 105 is an example of a process design means that compares reference information of multiple products and extracts common or similar processes. Figure 13 shows an example of how the process design unit 105 extracts common processes for multiple products. The procedure for the process design unit 105 to extract common or similar processes will be explained with reference to Figure 13.

[0072] First, the process design unit 105 compares the processes of product A' and product B'. Specifically, the process design unit 105 compares the items included in each process of product A' with the items included in each process of product B' and searches for processes in which the values ​​of all items match. Alternatively, the process design unit 105 searches for processes in which the values ​​of all items are similar. For example, the process design unit 105 determines that the processes are similar if the values ​​of a predetermined number or more of the items match.

[0073] Next, the process design unit 105 extracts common or similar processes. The process design unit 105 extracts common or similar processes by, for example, color-coding each process as shown in Figure 13. Note that in Figure 13, instead of color-coding, processes are extracted by changing the fill pattern. The process design unit 105 then assigns a new process number to each of the extracted common processes. The process numbers shown at the bottom of Figure 13 are the new process numbers assigned by the process design unit 105.

[0074] The process sequence optimization unit 102 uses the newly assigned process number by the process design unit 105 as the initial value and performs process optimization. The process optimization process is the same as in the first embodiment, so a description is omitted.

[0075] Next, an example of the operation of the design support device 100 in the second embodiment will be described with reference to the flowchart in Figure 14.

[0076] The design support device 100 starts processing by receiving a start command from the user via the input device 200, as an example. The processing in step S201 is the same as the processing in step S101 in the first embodiment, so its description is omitted.

[0077] Next, the acquisition unit 101 acquires production plan information from the storage device 300 for each of the multiple products to be designed (step S202).

[0078] Next, the acquisition unit 101 acquires information regarding the production plans of similar products as reference information based on the acquired production plan information (step S203).

[0079] Next, the process design unit 105 compares reference information for multiple products and extracts common or similar processes (step S204). Then, the process design unit 105 assigns a new process number to each of the extracted common processes.

[0080] Next, the process sequence optimization unit 102 uses the newly assigned process number by the process design unit 105 as the initial value and performs process optimization using the constraints based on the production plan information acquired in step S203 (step S205). The specific process optimization is the same as in the first embodiment, so the explanation is omitted. Also, the processes from step S205 onwards, from step S206 to step S210, are the same as the processes from step S105 to step S109 in the first embodiment, so the explanation is omitted.

[0081] The design support device 100 in the second embodiment is configured as described above. Next, the effects of the design support device 100 in the second embodiment will be explained. In addition to the effects of the design support device 100 in the first embodiment, the design support device 100 in the second embodiment can obtain the following effects.

[0082] The design support device 100 described above compares reference information of multiple products and extracts common or similar processes. Then, the design support device 100 uses the extracted processes as initial values ​​and performs process optimization. According to the design support device 100 of this embodiment, it is possible to design a production line in which multiple products are mixed in a single production line, making it possible to design a production line with high productivity in mixed-model production.

[0083] [Computer-based hardware configuration] Each component in the embodiments of this disclosure described above can be implemented not only in hardware, but also by a computer device or firmware based on program control.

[0084] Figure 15 shows an example of a hardware configuration in which the design support device 100 in this disclosure is realized by a computer device 10 including a processor. As shown in Figure 15, the computer device 10 includes a CPU (Central Processing Unit) 11, memory 12, a storage device 13 such as a hard disk for storing programs, an input / output interface 14 for connecting input and output devices, and a communication interface 15 for network connection.

[0085] The CPU 11 controls the entire design support device 100 of the present invention by running the operating system. For example, the CPU 11 reads programs and data from a storage medium mounted on a drive device or the like into the memory 12. The CPU 11 also functions as part of the acquisition unit 101, process sequence optimization unit 102, line sequence optimization unit 103, and achievement level calculation unit 104 in the first embodiment, and executes processing or instructions based on the program. Alternatively, the CPU 11 may function as part of the acquisition unit 101, process sequence optimization unit 102, line sequence optimization unit 103, achievement level calculation unit 104, and process design unit 105 in the second embodiment, and executes processing or instructions based on the program.

[0086] The storage device 13 is, for example, an optical disc, a flexible disc, a magneto-optical disc, an external hard disk, or a semiconductor memory. Some of the storage media of the storage device are non-volatile storage devices, and programs are recorded thereon. The programs may also be downloaded from an external computer (not shown) connected to a communication network. For example, the storage device 13 functions as the storage device 300 in the first and second embodiments.

[0087] The input device connected to the input / output interface 14 is implemented by, for example, a mouse or keyboard, and is used for input operations. Similarly, the output device connected to the input / output interface 14 is implemented by, for example, a display, and is used for displaying and confirming output results. For example, the input / output interface 14 functions as the input device 200 or the output device 400 in the first and second embodiments.

[0088] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above embodiments. Various modifications can be made to the configuration and details of the present invention that can be understood by those skilled in the art within the scope of the present invention. For example, although multiple operations are described sequentially in the form of a flowchart, the order in which they are described does not limit the order in which the multiple operations are performed. Therefore, when implementing each embodiment, the order of the multiple operations can be changed to the extent that it does not impair the content. [Explanation of Symbols]

[0089] 1. Information Processing System 10 Computer equipment 11 CPU 12 memory 13 Storage device 14 Input / Output Interfaces 15 Communication Interfaces 100 Design support equipment 101 Acquisition Department 102 Process Sequence Optimization Unit 103 Line Order Optimization Unit 104 Achievement Calculation Department 105 Process Design Department 200 Input Devices 300 storage device 400 Output device

Claims

1. A design support device that assists in designing a production line consisting of a combination of multiple lines, each including multiple processes performed in sequence for producing a product, Acquisition means for acquiring production plan information for the said product, reference information which is information relating to processes constituting a production line for a similar product whose production plan information is similar to that of the said product, and constraint information for the production of the said product. Process sequence optimization means that uses the reference information for each process, the sequence of each process, and the constraint conditions based on the constraint information to perform calculations of a predetermined evaluation function for each combination of the sequence of each process in the line. A line sequence optimization means that uses the calculation results of the process sequence optimization means, the sequence of each line, and the constraint conditions based on the constraint information to perform the calculation of the predetermined evaluation function for each combination of the sequence of each line in the production line, and Achievement calculation means calculates the degree of achievement of the production indicators for the production line calculated by the line sequence optimization means, for a predetermined period of time. Equipped with, If the degree of achievement of the indicator does not reach the target value, the evaluation function of the process sequence optimization means is updated using the calculation result of the line sequence optimization means, and the calculation of the line optimization by the process sequence optimization means, the calculation of the production line optimization by the line sequence optimization means, and the calculation of the degree of achievement of the indicator by the achievement degree calculation means are repeated until the degree of achievement of the indicator reaches the target value. A design support device characterized by the following features.

2. Further equipped with process design means, The acquisition means acquires the reference information of multiple products, The process design means compares the reference information of the multiple products and extracts common or similar processes. The design support apparatus according to claim 1, characterized in that the process sequence optimization means performs optimization calculations for the line using the extracted processes.

3. The design support device according to claim 1 or 2, characterized in that the acquisition means acquires the reference information by taking the production plan information as input and inferring similar products using a pre-prepared learning model.

4. The design support apparatus according to claim 3, characterized in that the process sequence optimization means and the line sequence optimization means perform optimization based on a genetic algorithm.

5. The design support device according to claim 4, characterized in that the process sequence optimization means and the line sequence optimization means perform optimization using an evaluation function that includes at least one of production efficiency, production lead time, and number of workers.

6. The design support apparatus according to claim 1, characterized in that the process sequence optimization means and the line sequence optimization means perform optimization based on a genetic algorithm.

7. The design support device according to claim 6, characterized in that the process sequence optimization means and the line sequence optimization means perform optimization using an evaluation function that includes at least one of production efficiency, production lead time, and number of workers.

8. A design support device that assists in designing a production line consisting of a combination of multiple lines, each containing multiple processes performed in a specific sequence, for producing a product, The following are obtained: production plan information for the said product, reference information which is information about the processes constituting the production line of a similar product whose production plan information is similar to that of the said product, and constraint information for the production of the said product. Using the reference information for each process, the order of each process, and the constraints based on the constraint information, a process sequence optimization calculation is performed in the line, which involves calculating a predetermined evaluation function for each combination of the order of each process. Using the calculation results of the process sequence optimization, the sequence of each line, and the constraint conditions based on the constraint information, a line sequence optimization calculation is performed in the production line, where the predetermined evaluation function is calculated for each combination of the sequence of each line. The indicators targeted to the production line, calculated by the line sequence optimization calculation, are used to determine the degree of achievement of the indicators related to the production of the product per predetermined period. If the degree of achievement of the aforementioned indicator does not reach the target value, the evaluation function in the process sequence optimization calculation is updated using the calculation result of the line sequence optimization calculation, and the process sequence optimization calculation, the line sequence optimization calculation, and the calculation of the degree of achievement of the aforementioned indicator are repeated until the degree of achievement of the aforementioned indicator reaches the target value. A design support method characterized by the following features.

9. Computers A design support device that assists in designing a production line consisting of a combination of multiple lines, each containing multiple processes executed in a specific sequence, for producing a product. A process for acquiring production plan information for the said product, reference information which is information relating to the processes that constitute the production line of a similar product whose production plan information is similar to that of the said product, and constraint information for the production of the said product. A process that performs process sequence optimization calculations by using the reference information for each process, the sequence of each process, and constraint conditions based on the constraint information, and performing calculations of a predetermined evaluation function for each combination of the sequence of each process in the line, A process for performing line sequence optimization calculations, which uses the calculation results of the process for optimizing the process sequence, the sequence of each line, and the constraint conditions based on the constraint information, to perform the calculation of the predetermined evaluation function for each combination of the sequence of each line in the production line, The process involves calculating the degree to which the production line is targeted by the calculation performed by the line order optimization process, and the process involves calculating the degree to which the production indicator of the product per predetermined period is achieved. If the degree of achievement of the aforementioned indicator does not reach the target value, the evaluation function in the process sequence optimization calculation is updated using the calculation result of the process sequence optimization calculation, and the process sequence optimization calculation, the line sequence optimization calculation, and the calculation of the degree of achievement of the aforementioned indicator are repeated until the degree of achievement of the aforementioned indicator reaches the target value. A computer program that executes something.

Images