Simulation system and simulation program

Abstract

To achieve an improvement for a simulation model of a production line with a shorter lead time.SOLUTION: Execution processing of simulations using a simulation model set for an improvement object, evaluation processing of the improvement object using simulation results obtained from the execution processing, adjustment processing of the improvement object using evaluation results obtained from the evaluation processing, and setting processing for setting the improvement object adjusted by the adjustment processing as a new improvement object are repeatedly performed. During the evaluation processing, if an evaluation index for a latest improvement object is determined to exceed a predetermined standard, a positive evaluation result is output for the latest improvement object. If the positive evaluation result is output for the latest improvement object during the evaluation processing, processing is performed to send a proposal concerning the latest improvement object to a terminal of a creator of the improvement object.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present disclosure relates to a system and a program for simulating a production line. [Background technology]

[0002] Japanese Patent Application Laid-Open Publication No. 2007-041950 discloses a production simulation management device. This management device is equipped with a database. The database stores a simulation model used in the production simulation, data used in the production simulation, simulation results, performance data on the production line, and a comparison between the simulation results and the performance data. When search conditions are input, the management device refers to the database and selects a simulation model that most closely matches the search conditions. The management device also outputs the selected simulation model, simulation results related to this simulation model, and a comparison between the simulation results and the performance data.

[0003] In addition to JP 2007-041950 A, JP 2023-045978 A can be exemplified as documents showing the state of the art in the technical field related to the present disclosure. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2007-041950 [Patent Document 2] Japanese Patent Application Publication No. 2023-045978 Summary of the Invention [Problem to be solved by the invention]

[0005] The comparison of the simulation results with the actual data is useful for evaluating the simulation model used in the production simulation and further for correcting the simulation model using the evaluation results. By repeating the process of evaluating and correcting the simulation model and performing production simulation using the corrected simulation model, it becomes possible to build a simulation model that will obtain the desired output.

[0006] However, the execution of this series of improvement processes is left to the discretion of the users of the production simulation, including the creators of the simulation models, and the improvement process for building an ideal simulation model takes a significant amount of time from start to completion. Therefore, there is a demand for the development of technology that can improve simulation models with a shorter lead time.

[0007] One object of the present disclosure is to provide a technology that can improve a simulation model of a production line with a shorter lead time. [Means for solving the problem]

[0008] A first aspect of the present disclosure is a system for executing a simulation of a production line using a simulation model, and has the following features. The system executes a simulation of a production line using a simulation model. The simulation system includes a storage device and a processor. The storage device stores the simulation model. The processor is configured to repeatedly perform the following steps: executing the simulation using the simulation model set as an improvement target; evaluating the improvement target based on the simulation results obtained by the execution process; adjusting the improvement target based on the evaluation results obtained by the evaluation process; and setting the improvement target adjusted by the adjustment process as a new improvement target. In the evaluation process, the processor determines whether an evaluation index for the latest improvement target exceeds a predetermined standard, and if it is determined that the evaluation index exceeds the predetermined standard, outputs a positive evaluation result for the latest improvement target. The processor is further configured to, if the positive evaluation result for the latest improvement target is output in the evaluation process, transmit a proposal for the latest improvement target to a terminal of a creator of the improvement target model.

[0009] A second aspect of the present disclosure is a program for causing a computer to perform a simulation of a production line using a simulation model, and has the following features. The program causes the computer to repeatedly perform the following processes: executing the simulation using a simulation model set as an improvement target; evaluating the improvement target based on the simulation results obtained by the execution process; adjusting the improvement target based on the evaluation results obtained by the evaluation process; and setting the improvement target adjusted by the adjustment process as a new improvement target. The evaluation process includes determining whether an evaluation index for the latest improvement target exceeds a predetermined standard, and outputting a positive evaluation result for the latest improvement target if it is determined that the evaluation index exceeds the predetermined standard. The program further causes the computer to perform a process of sending a proposal for the latest improvement target to a terminal of a creator of the improvement target if the positive evaluation result is output for the latest improvement target in the evaluation process. [Effects of the Invention]

[0010] According to the present disclosure, the setting of an improvement target, the execution of a simulation of a production line using the improvement target, the evaluation of the improvement target using the simulation results, and the adjustment of the improvement target using the evaluation results are automatically repeated. Also, according to the present disclosure, when a positive evaluation result is output in the evaluation of the latest improvement target, a proposal for the latest improvement target is automatically sent to the terminal of the manufacturer of the improvement target.

[0011] When a positive evaluation result is output, the latest proposal for improvement is automatically sent to the terminal of the creator of the improvement target, which means that the improvement process for the simulation model is performed automatically, and the improvement information obtained through this improvement process is automatically proposed to the creator of the improvement target. Therefore, according to the present disclosure, it is possible to improve the simulation model with a shorter lead time. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a diagram illustrating an overview of a simulation system according to an embodiment. [Figure 2] FIG. 1 is a diagram illustrating an example of the configuration of a digital twin platform for realizing an automatic improvement process for a digital twin simulation model. [Figure 3] FIG. 1 is a diagram illustrating an example of the configuration of digital twin simulation data. [Figure 4] 1 is a flowchart illustrating an example of a computer process associated with operating an automated improvement process. [Figure 5] 10A and 10B are diagrams illustrating examples of images displayed on a display of a main computer. [Figure 6] 10A and 10B are diagrams illustrating examples of images displayed on a display of a main computer. DETAILED DESCRIPTION OF THE INVENTION

[0013] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In each drawing, the same or corresponding parts are denoted by the same reference numerals, and the description thereof will be simplified or omitted.

[0014] 1. Simulation System Fig. 1 is a diagram illustrating an overview of a simulation system according to an embodiment of the present disclosure. The simulation system 1 illustrated in Fig. 1 is a system that performs a simulation related to a production line LN. The production line LN is a series of lines that coordinate the execution of various tasks related to the production of products. The production line LN is installed, for example, on the premises of a factory that mass-produces products.

[0015] A plurality of workers are deployed on the production line LN. All of these workers are actual workers who are involved in tasks that are performed cooperatively on the production line LN. Workers WK1, WK2, WK3, WK4, WK5, and WK6 shown in FIG. 1 are examples of actual workers. Hereinafter, when workers WK1 to WK6 are not specified, these actual workers will be collectively referred to as "workers WK."

[0016] Workers WK1 and WK2 are in charge of task TS1. Workers WK3 and WK4 are in charge of task TS2. Workers WK5 and WK6 are in charge of task TS3. For example, if a production line LN is installed in a production factory, tasks TS1, TS2, and TS3 would include receiving, inspecting, and storing various parts, assembling various parts, transporting and combining intermediate products, and inspecting and shipping the final product. Tasks TS1, TS2, and TS3 are processed manually by workers WK or by robot operation by workers WK.

[0017] In the simulation system 1, a simulation using a digital space (hereinafter also referred to as "digital twin simulation" or "DTS") is performed. This digital space is reproduced based on the real space where the production line LN is installed. The DT (digital twin) platform 2 shown in FIG. 1 is configured to perform the DTS.

[0018] As a configuration for implementing the DTS, the DT platform 2 includes at least one processor and at least one storage device. The at least one processor executes various processes. Examples of the at least one processor include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), and an FPGA (Field-Programmable Gate Array). Various data are stored in the at least one storage device. Examples of the at least one storage device include a volatile memory, a non-volatile memory, an HDD (Hard Disk Drive), and an SSD (Solid State Drive). The at least one storage device also stores various programs. The various programs may be stored in a computer-readable recording medium.

[0019] Various computers are connected to the DT platform 2, through which the DT platform 2 and DTS personnel exchange information. These computers include a main computer MC and sub-computers SC1, SC2, and SC3. The basic configuration of these various computers is the same as that of the DT platform 2. In other words, each of these various computers includes at least one processor and at least one storage device.

[0020] The main computer MC is the terminal of the producer PR, who is in charge of producing the DTS for the production line LN. The producer PR is a system user who oversees the entire production line LN, including the content of the work performed on the production line LN and the order in which the tasks that make up this work are processed. The producer PR sets DTS tasks in association with the tasks that make up the work performed on the production line LN (i.e., tasks TS1, TS2, and TS3), and enters information for the design of the DTS.

[0021] The design of a DTS includes the design of a model for the DTS (hereinafter also referred to as the "DTS model"). The design of the DTS model is performed, for example, using an application that creates a three-dimensional model that mimics the production line LN. The program for executing such an application is stored in at least one storage device of the main computer MC.

[0022] In response to or to assist in input by the creator PR regarding the design of the DTS model, various requests REQ and proposals PRO are sent from the DT platform 2 to the main computer MC. The requests REQ and proposals PRO sent from the DT platform 2 are output, for example, from the display of the main computer MC as a function of a 3D modeling application executed on the main computer MC. This allows the creator PR to consider the requests REQ and input information regarding the design and adjustment of the DTS model as a result of the consideration into the main computer MC. The creator PR can also consider the proposals PRO and input information approving or rejecting the proposals PRO as a result of the consideration into the main computer MC.

[0023] To implement DTS, parameter information specific to the DTS model must be input, taking into account the actual conditions of each task, TS1, TS2, and TS3. Furthermore, to improve DTS, review information, such as comments and suggestions for the DTS model, must be input based on the various insights gained from the production line LN. The computers used to input such parameter information and review information are the subcomputers SC1, SC2, and SC3. The subcomputers SC1, SC2, and SC3 are the terminals of the production collaborators PC1, PC2, and PC3, respectively.

[0024] Like producer PR, collaborators PC1, PC2, and PC3 are system users. Collaborators PC1, PC2, and PC3 are selected from workers WK who are involved in tasks TS1, TS2, and TS3. In the example shown in Figure 1, collaborator PC1 is worker WK1 or WK2, collaborator PC2 is worker WK3 or WK4, and collaborator PC3 is worker WK5 or WK6.

[0025] Collaborators PC1, PC2, and PC3 input parameter information specific to the tasks they are responsible for and review information for the DTS model based on various requests REQ sent to the sub-computers SC1, SC2, and SC3, respectively, from the DT platform 2. The requests REQ sent from the DT platform 2 are output from the displays of the sub-computers SC1, SC2, and SC3, for example, as functions of the DTS applications executed by these sub-computers.

[0026] 2.Automating the DTS model improvement process In this embodiment, the improvement process for the DTS model is performed in the DT platform 2. Fig. 2 is a diagram illustrating an example of the configuration of the DT platform 2 for realizing the improvement process. Fig. 2 illustrates the following functional blocks of the DT platform 2: a model setting unit 21, a simulation unit 22, a model evaluation unit 23, a model proposal unit 24, a model adjustment unit 25, a model review unit 26, and a communication unit 27. These functional blocks are realized, for example, by the processor of the DT platform 2 executing an automatic improvement program stored in a storage device of the platform 2.

[0027] FIG. 2 also illustrates two types of databases 28 and 29 of the DT platform 2. The databases 28 and 29 are formed, for example, in a storage device of the platform 2. The database 28 stores data DLN relating to the production line LN (hereinafter also referred to as "production line data"). Examples of the production line data DLN include production plan data for the production line LN, operation history data for the production line LN, and production performance data for the production line LN. The database 29 stores data DDT relating to DTS (hereinafter also referred to as "DTS data"). An example of the DTS data DDT will be described with reference to FIG. 3 (described later).

[0028] The model setting unit 21 sets a DTS model to be improved (hereinafter also referred to as "improvement target model MDt"). The DTS models used for DTS are stored in the storage device of the DT platform 2. The DTS models include a DTS model of a single production line LN. The DTS model of a single production line LN is constructed by combining each DTS model of the components of this production line LN (equipment such as conveyors, robots in charge of tasks, routes of workers or AGVs (Automatic Guided Vehicles), etc.). The DTS model may include a model that combines DTS models of two or more production lines LN that operate in coordination.

[0029] The setting of the improvement target model MDt is performed by the DT platform 2 itself arbitrarily selecting the production line LN and its components that the DT platform 2 focuses on. The setting of the improvement target model MDt may also be performed by specification from the system user (for example, the producer PR). The total number of the improvement target models MDt to be set is not particularly limited, and one or more DTS models can be set.

[0030] The simulation unit 22 performs DTS. In this DTS, first, a worker WK is assigned to a task that constitutes a work currently being performed on the production line LN. A worker WK may also be assigned to a task that constitutes a work that is scheduled to be performed on the production line LN. The work currently being performed or scheduled to be performed on the production line LN and the tasks that constitute this work are identified from the production plan data of the production line LN. The production plan data is included in the production line data DLN.

[0031] Once the allocation of the workers WK is complete, the DTS is executed. In the DTS, for example, the future state of the production line LN is predicted. Examples of this future state include the availability rate and availability rate of the components of the production line LN, the overall efficiency of the production line LN, the defect rate or yield rate of the products produced on the production line LN (the ratio of future predicted values ​​to planned values), and the production lead time. These future states are set in advance as corresponding to the key performance indicators (KPIs) of the production line LN (hereinafter simply referred to as "indicator KPIs"). The simulation unit 22 sends the results of the DTS (prediction results of the future state) to the model evaluation unit 23.

[0032] The model evaluation unit 23 evaluates the improvement target model MDt based on the DTS results (hereinafter also referred to as "DTS results") received from the simulation unit 22. In the evaluation of the improvement target model MDt, first, those DTS results related to the improvement target model MDt are extracted. Then, an index KPI (i.e., KPI(MDt)) for the extracted DTS results is compared with a predetermined standard TH.

[0033] Here, the predetermined standard TH is set in advance for each indicator KPI. The predetermined standard TH may be a standard value acceptable for the production line LN, or an ideal value that is generally higher than the standard value. When production performance data for the production line LN has been collected, the indicator KPI calculated based on the production performance data may be set as the predetermined standard TH. Since DTS is performed using a digital space reproduced based on the real space where the production line LN is installed, it is desirable to set the predetermined standard TH based on the production performance data. The production performance data DFL is included in the production line data DLN.

[0034] If the indicator KPI(MDt) exceeds the predetermined standard TH, the model evaluation unit 23 outputs a positive evaluation result for the improvement target model MDt. The positive evaluation result includes three-dimensional model information of the improvement target model MDt and difference information between the indicator KPI(MDt) and the predetermined standard TH (e.g., improvement rate). The positive evaluation result is transmitted to the model proposing unit 24. If the indicator KPI(MDt) is below the predetermined standard TH, the model evaluation unit 23 outputs a negative evaluation result for the improvement target model MDt. The negative evaluation result is transmitted to the model adjustment unit 25.

[0035] Based on the positive evaluation result received from the model evaluation unit 23, the model proposal unit 24 generates a proposal PRO for the improvement target model MDt for which this positive evaluation result was obtained. The proposal PRO includes, for example, three-dimensional model information of the improvement target model MDt. By generating the three-dimensional model information of the improvement target model MDt, the improvement target model MDt can be output from the display of the main computer MC when a three-dimensional modeling application is executed. The proposal PRO may also include difference information between the improvement target model MDt and the DTS model currently adopted by the creator PR (hereinafter also referred to as the "current model"). This difference information will be described later.

[0036] The model adjustment unit 25 adjusts the DTS model. The adjustment of the DTS model is performed, for example, based on parameter information specific to the DTS model received from the sub-computers SC1, SC2, and SC3. In another example, the adjustment of the DTS model is performed by arbitrarily changing adjustable values ​​among the numerical values ​​related to the components of the improvement target model MDt. Examples of adjustable values ​​include the position (including height) of the component of the production line LN, the area of ​​the buffer zone set around this component, and the distance between adjacent components. In a more advanced example, the size and number of the components of the production line LN, the position (including height), length, and shape of the production line LN can also be considered as adjustable values.

[0037] When a negative evaluation result is received from the model evaluation unit 23, the model adjustment unit 25 adjusts the improvement target model MDt. For example, the model adjustment unit 25 analyzes the negative evaluation result to extract bottleneck information, and adjusts the improvement target model MDt for the extracted bottleneck location (component of the production line LN). In another example, the model adjustment unit 25 analyzes the negative evaluation result to estimate the factor that causes the indicator KPI(MDt) to fall below the predetermined standard TH, and adjusts the improvement target model MDt for the location (component of the production line LN) related to the estimated factor. Note that an example of adjustment of the improvement target model MDt is the same as that of the DTS model described above.

[0038] When the knowledge data DKH is received from the model review unit 26, the model adjustment unit 25 adjusts the improvement target model MDt. The knowledge data DKH includes numerical data generated based on review information for the DTS model. When the knowledge data DKH is received, the model adjustment unit 25 adjusts the improvement target model MDt by combining the knowledge data DKH with the negative evaluation result received from the model evaluation unit 23. The knowledge data DKH is provided by a production collaborator who is also a worker WK. Therefore, when the knowledge data DKH is received, it is desirable to adjust the improvement target model MDt by prioritizing the knowledge data DKH over the negative evaluation result.

[0039] The model review unit 26 collects review information for the current model. To collect the review information, the model review unit 26 generates a request REQ prompting the provision of review information for the current model and transmits it to the sub-computers SC1, SC2, and SC3. When the model review unit 26 receives review information in response to the request REQ, it generates knowledge data DKH based on the review information.

[0040] If the review information is a numerical value, the knowledge data DKH can be generated directly from this numerical value. However, the review information may also be a character string or a language. If the review information is a character string, the model review unit 26 converts the character string into a numerical value related to the component of the DTS model. Then, if the converted numerical value related to the component can be adjusted by the model adjustment unit 25, the model to be improved MDt can be adjusted using this numerical value. If the review information is a language, the language is converted into a character string, and then the above-mentioned conversion from the character string to a numerical value is performed. When the knowledge data DKH is generated, the model review unit 26 transmits it to the model adjustment unit 25.

[0041] The communication unit 27 communicates with various computers (in FIG. 1, the main computer MC and sub-computers SC1, SC2, and SC3) through which the DT platform 2 and the parties involved in the DTS exchange information. In communicating with the sub-computers SC1, SC2, and SC3, the communication unit 27 sends requests REQ to these sub-computers to prompt them to provide parameter information specific to the DTS. The communication unit 27 also sends requests REQ to these sub-computers to prompt them to provide review information for the DTS. When the communication unit 27 receives parameter information and review information from the sub-computers SC1, SC2, and SC3 in response to the requests REQ, it sends them to the model adjustment unit 25 or the model review unit 26.

[0042] In communication with the main computer MC, the communication unit 27 transmits a request REQ to the main computer MC in response to input from the main computer MC of information related to DTS design and adjustment, or to assist in this input. When a proposal PRO related to the improvement target model MDt has been received from the model proposing unit 24, the communication unit 27 transmits this proposal PRO to the main computer MC. The communication unit 27 also receives information related to the design and adjustment of the DTS model from the main computer MC in response to the request REQ, and transmits this information to the model adjustment unit 25. The communication unit 27 further receives approval or rejection information from the main computer MC in response to the proposal PRO, and transmits this information to the model adjustment unit 25.

[0043] 3 is a diagram illustrating an example of the configuration of DTS data DDT. In the example shown in FIG. 3, the DTS data DDT includes model data DMD, parameter data DPM, result data DRS, and knowledge data DKH. The model data DMD is data of a DTS model. There can be as many model data DMDs as there are combinations of DTS model types (MD1, . . . , MDi) and model versions (ver. 1, . . . , ver. j) (i, j>1).

[0044] The parameter data DPM, result data DRS, and knowledge data DKH are associated with the type of DTS model and its version. The parameter data DPM is data on the parameters of the DTS model. The number of parameter data DPMs is equal to the number of combinations of parameter types (PM1,...,PMx) and DTS model types and their versions (x>1). The result data DRS is data on the DTS results. The number of result data DRSs is equal to the number of combinations of DTS results (RS1,...,RSy) and the type and version of the DTS model when the DTS results were obtained (y>1). The knowledge data DKH includes parameter data generated based on review information such as comments and suggestions for the DTS. Therefore, the number of knowledge data DKHs is equal to the number of combinations of DTS model types and their versions when knowledge (KH1,...,KHz) was obtained from review information such as comments and suggestions for the DTS (z>1).

[0045] 3.Automating the improvement process In the embodiment, the setting of the improvement-target model MDt, the DTS, the evaluation of the improvement-target model MDt, and the adjustment of the improvement-target model MDt are performed as described with reference to Fig. 2. Furthermore, if a positive evaluation result is obtained in the evaluation of the improvement-target model MDt, a proposal PRO for the improvement-target model MDt is generated and transmitted to the main computer MC. In this way, in the embodiment, the improvement process of the DTS model is automated, and 3D model information for the latest improvement-target model MDt for which a positive evaluation result has been obtained as a result of the improvement process is transmitted to the main computer MC.

[0046] 4 is a flowchart showing an example of computer processing related to the processing of the automatic improvement process. The routine shown in FIG. 4 is repeatedly executed at a predetermined period by, for example, at least one processor included in the DT platform 2.

[0047] In the routine shown in Fig. 4, first, the improvement target model MDt is set (step S11). The processing of step S11 is realized by the function of the model setting unit 21 shown in Fig. 2. As described above, the improvement target model MDt is set by the DT platform 2 itself arbitrarily selecting the production line LN and its components that the DT platform 2 focuses on. The improvement target model MDt may also be set by a specification from the system user (for example, the producer PR).

[0048] Following the processing of step S11, the DTS is executed (step S12). The processing of step S12 is realized by the function of the simulation unit 22 shown in Fig. 2. As described above, the DTS predicts the future state of the production line LN that corresponds to the key performance indicators KPI of the production line LN.

[0049] Following the process of step S12, the improvement target model MDt is evaluated (steps S13 to S15). The processes of steps S13 to S15 are realized by the function of the model evaluation unit 23 shown in FIG. 2. As described above, in the evaluation of the improvement target model MDt, first, an indicator KPI (i.e., KPI(MDt)) for the results related to the improvement target model MDt is extracted from the DTS results (step S13). Next, it is determined whether or not the indicator KPI(MDt) exceeds a predetermined standard TH (step S14). Then, if the indicator KPI(MDt) exceeds the predetermined standard TH, a positive evaluation result is output for the improvement target model MDt (step S15).

[0050] If the indicator KPI(MDt) is below the predetermined standard TH, a negative evaluation result is output for the improvement target model MDt, and the process of step S16 is performed. The process of step S16 is realized by the function of the model adjustment unit 25 shown in FIG. 2. As described above, in adjusting the DTS model, adjustable values ​​of the components of the DTS model (specifically, the improvement target model MDt) are arbitrarily changed. For convenience of explanation, the improvement target model MDt before change will also be referred to as the "improvement target model MDt(k)," and the improvement target model MDt after change will also be referred to as the "improvement target model MDt(k+1)" (k≧1).

[0051] Following the processing of step S16, the improvement target model MDt(k+1) is set as the improvement target model MDt (step S17). The processing of step S17 is realized by the function of the model setting unit 21 shown in FIG. 2. After the processing of step S17 is performed, the process returns to step S12 and DTS is performed. In this way, if the evaluation of the current improvement target model MDt is negative, the current improvement target model MDt (i.e., the improvement target model MDt(k)) is changed, and the changed improvement target model MDt (i.e., the improvement target model MDt(k+1)) is set as the new improvement target model MDt, and the processing of steps S12 to S14 is performed. In this case, the new improvement target model MDt is treated as the current improvement target model MDt.

[0052] Then, if the indicator KPI(MDt) for the current improvement target model MDt exceeds the predetermined standard TH, a positive evaluation result for this current improvement target model MDt is output (step S15). Otherwise, the current improvement target model MDt (i.e., the improvement target model MDt(k)) is changed (step S16), and the changed improvement target model MDt (i.e., the improvement target model MDt(k+1)) is set as the new improvement target model MDt. In this way, if the negative evaluation result for the current improvement target model MDt is denied, the processes of steps S12 to S14 and the processes of steps S16 and S17 are repeated.

[0053] Following the processing of step S15, a proposal PRO is generated based on the positive evaluation result output in step S15 (step S18). The processing of step S18 is realized by the function of the model proposing unit 24 shown in Fig. 2. As described above, the proposal PRO includes three-dimensional model information of the improvement target model MDt for which a positive evaluation result was obtained.

[0054] Following the processing of step S18, the proposal PRO generated in step S18 is transmitted to the main computer MC (step S19). The processing of step S19 is realized by the function of the communication unit 27 shown in Fig. 2. When the proposal PRO is transmitted to the main computer MC by the processing of step S19, an image IMG generated based on this proposal PRO is output from the display of the main computer MC.

[0055] 4. Example of image display on the main computer MC As described in the explanation of the processing of step S19 in Fig. 4, when the proposal PRO is transmitted to the main computer MC, an image IMG generated based on this proposal PRO is output from the display of the main computer MC. Figs. 5 and 6 are diagrams illustrating a display example of the image IMG output from the display of the main computer MC. Note that the explanation of Figs. 5 and 6 is based on the premise that the proposal PRO, which includes three-dimensional model information of the current model and the improvement target model MDt and difference information between these models, has been transmitted to the main computer MC.

[0056] An image IMG_MDt(k) generated based on the three-dimensional model information of the current model is shown in Fig. 5, and an image IMG_MDt(k+1) generated based on the three-dimensional model information of the improvement target model MDt is shown in Fig. 6. The images IMG_MDt(k) and IMG_MDt(k+1) are output, for example, separately from two displays of the main computer MC, or separately to divided areas of a single display.

[0057] 5 and 6 illustrate a DTS model including three production lines LN1, LN2, and L3. These figures also illustrate robot RB1 for production line LN1, robot RB2 for production line LN2, and robot RB3 for production line LN3, as well as robot RB4 common to these production lines. These figures also illustrate aisles PW1, PW2, PW3, and PW4 for pedestrians (workers WK) and aisles VW1 and VW2 for AGVs.

[0058] As can be seen by comparing Figures 5 and 6, the distance MA1 between robot RB1 and robot RB4 is different between image IMG_MDt(k) depicting the current model and image IMG_MDt(k+1) depicting the improvement target model MDt. Furthermore, the distance MA2 between robot RB2 and robot RB3 is also different between these images. Furthermore, the width of passage PW2 is different between these images, and the orientations of passages PW3 and PW4 are also different. Therefore, by looking at images IMG_MDt(k) and IMG_MDt(k+1), the creator PR can easily understand the differences between the current model and the improvement target model MDt.

[0059] Image IMG_MDt(k+1) in Figure 6 depicts icons IC that draw attention to the distances MA1 and MA2 and the vicinity of passages PW2, PW3, and PW4. These icons IC are examples of difference information between the current model and the improvement target model MDt, and help the creator PR understand the difference information. For example, by operating the input device of the main computer MC and pointing the pointer over the icon IC, detailed difference information is displayed. Examples of detailed difference information include numerical information such as the distance indicated by the icon IC, the type of indicator KPI used to evaluate the improvement target model MDt, and the improvement rate for that indicator KPI. The display of detailed difference information is realized, for example, by a function of a 3D modeling application on the main computer MC. [Explanation of symbols]

[0060] 1...Simulation system, 2...Digital twin platform, 21...Model setting section, 22...Simulation section, 23...Model evaluation section, 24...Model proposal section, 25...Model adjustment section, 26...Model review section, 27...Communication section, 28, 29...Database, LN, LN1, LN2, LN3...Production line, MC...Main computer, SC...Sub-computer, PR...Producer, WK1, WK2, WK3, WK4, WK5, WK6...Workers, MDt...Model to be improved, DDT...Data related to digital twin simulation, DFL...Production performance data, DLN...Production line data, DMD...Model data, DPM...Parameter data, DRS...Result data, PRO...Proposal, REQ...Request

Claims

1. A system for performing a production line simulation, comprising: a storage device in which a simulation model of the production line is stored; a processor, the processor: The system is configured to repeatedly perform a process of executing the simulation using a simulation model set as an improvement target, a process of evaluating the improvement target based on the simulation results obtained by the execution process, a process of adjusting the improvement target based on the evaluation results obtained by the evaluation process, and a process of setting the improvement target adjusted by the adjustment process as a new improvement target, In the evaluation process, the processor: Determine whether the evaluation index for the latest improvement target exceeds a predetermined standard; If it is determined that the evaluation index exceeds the predetermined standard, a positive evaluation result is output for the latest improvement target; The processor is further configured to perform a process of transmitting a proposal for the latest improvement target to a terminal of a creator of the improvement target when the positive evaluation result is output for the latest improvement target in the evaluation process. A simulation system comprising:

2. 10. The system of claim 1, The storage device further stores production results for the production line, In the evaluation process, the processor: The predetermined standard is set based on the production results. A simulation system comprising:

3. 3. The system according to claim 1 or 2, The proposal regarding the latest improvement target includes difference information between the latest improvement target and a current model that corresponds to the latest improvement target and indicates a simulation model currently adopted by the creator. A simulation system comprising:

4. 3. The system according to claim 1 or 2, The processor further comprises: a process of transmitting a review request for the current model, which indicates the simulation model currently adopted by the creator, to a terminal of a collaborator in the creation of the simulation model; When input information for the current model is received from the collaborator's terminal in response to the review request, the current model is adjusted in the adjustment process based on the input information and the evaluation result obtained in the evaluation process. A simulation system comprising:

5. A program that causes a computer to perform a production line simulation, The program, causing the computer to repeatedly perform a process of executing the simulation using a simulation model set as an improvement target, a process of evaluating the improvement target based on the simulation results obtained by the execution process, a process of adjusting the improvement target based on the evaluation results obtained by the evaluation process, and a setting process of setting the improvement target adjusted by the adjustment process as a new improvement target; The evaluation process determining whether the evaluation index for the latest improvement target exceeds a predetermined standard; outputting a positive evaluation result for the latest improvement target when it is determined that the evaluation index exceeds the predetermined standard; Including, The program further causes the computer to perform a process of transmitting a proposal for the latest improvement target to a terminal of a creator of the improvement target when the positive evaluation result is output for the latest improvement target in the evaluation process. A simulation program characterized by:

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