Production simulation device

Abstract

The production simulation device of the present invention includes one or more processors and one or more storage devices. The one or more storage devices store: production performance information, which includes information on the performance start time and performance completion time of each process of the production operation; and a simulation model, which includes the process time of each process, the production resource groups that can be allocated to each process, the operation time of each production resource in each production resource group, and information on the production control rules of the production line. The one or more processors perform a simulation using the production performance information and the simulation model, and calculate a simulation error by comparing the production performance information with the result of the simulation.

Description

[0001] This application claims the benefit of Japanese patent application No. 2019-209009, filed on November 19, 2019, the contents of which are hereby incorporated by reference into this application. Technical Field

[0002] The present invention relates to production simulation. Background Art

[0003] Production simulation is a method of estimating the future production progress in factories, etc., and is useful for making production plans and developing countermeasures when production problems occur. Production simulation requires process information that defines the processing time and necessary production resources (equipment, operators, etc.) in each process for each item, production resource information that defines the number of production resources and future operating time, and production control rule information that determines the order of starting items in each process and the use of production resources.

[0004] Here, in order to effectively apply production simulation, it is important to make the production simulation highly accurate, and in order to make the production simulation highly accurate, it is important to make the above-mentioned information highly accurate. For example, if there is a deviation between the process time used in the simulation and the actual process time, the error of the simulation relative to the production performance increases.

[0005] However, it is difficult to manually define all information correctly, especially in multi-variety production. In view of this, there is a method of defining various information based on past production performance data. For example, as shown in Document 1, there is a method of generating benchmark data of the number of equipment and process time based on production performance data.

[0006] Prior art literature

[0007] Patent Literature

[0008] Patent Document 1: Japanese Patent Application Publication No. 2008-234526 Summary of the invention

[0009] Technical problem to be solved by the invention

[0010] Document 1 is a method of generating information such as the number of equipment and process time from production performance data and using it to perform production simulation. However, when it is assumed that production simulation is used to formulate production plans, it is not sufficient to simply generate each information, and it is necessary to evaluate the error of the simulation itself using this information. Then, when the error is large, it is necessary to identify the cause of the error and take measures to solve the cause.

[0011] Here, production simulation has the characteristics of being complexly and mutually related with the above-mentioned process time information, production resource information, production control rule information, etc. For example, when the process time in a certain process group deviates from the performance, the arrival time of the item at the subsequent processes of the process group also deviates from the performance. Then, assuming that the start order in the subsequent processes is determined by the arrival order of the items, the deviation of the arrival time of the items causes a deviation in the start order.

[0012] In this way, in production simulation, it has the property that an error in a certain process spreads to other processes, and this property makes it difficult to determine the cause of the production simulation error. According to the above, in order to improve the accuracy of production simulation, it is important to determine the main cause of the simulation error.

[0013] Technical solution for solving the technical problem

[0014] In order to solve the above technical problem, one aspect of the present disclosure is a production simulation device for estimating the progress of processes in a production line, which includes one or more processors and one or more storage devices. The one or more storage devices store: production performance information, which includes information on the performance start time and performance completion time of each process of the production operation; and a simulation model, which includes the process time of each process, the production resource groups that can be allocated to each process, the operation time of each production resource in each production resource group, and information on the production control rules of the production line. The one or more processors use the production performance information and the simulation model to perform a simulation, and compare the production performance information with the result of the simulation to calculate the simulation error.

[0015] Invention effect

[0016] According to one aspect of the present disclosure, high-precision production simulation can be achieved. Brief description of the drawings

[0017] Figure 1A It is a functional block diagram of the production simulation device.

[0018] Figure 1B It is a structure diagram of the hardware and software of the production simulation device.

[0019] Figure 2 It is a schematic diagram of the production performance data table.

[0020] Figure 3 It is a schematic diagram of the production process data table.

[0021] Figure 4 It is a schematic diagram of the equipment data table.

[0022] Figure 5 It is a schematic diagram of the operator data table.

[0023] Figure 6 It is a schematic diagram of the construction sequence rule data table.

[0024] Figure 7 It is a schematic diagram of the equipment allocation rule data table.

[0025] Figure 8 It is a schematic diagram of the operator allocation rule data table.

[0026] Figure 9 It is a schematic diagram of the simulation result data table.

[0027] Figure 10 It is a processing flow chart of the control unit of the production simulation device.

[0028] Figure 11A It is a schematic diagram showing an example of the display screen.

[0029] Figure 11B It is a schematic diagram showing an example of the display screen.

[0030] Figure 12 It is a schematic diagram showing an example of the implementation mode of the production simulation system. Detailed Implementation Mode

[0031] The following describes the implementation mode with reference to the accompanying drawings. It should be noted that this implementation mode is only an example for implementing the present invention and does not limit the technical scope of the present invention.

[0032] When production simulation is used for formulating production plans, etc., it is important to improve the accuracy of production simulation. In this regard, there is a method of deriving information required for simulation such as the processing time of each process based on production performance data. When there is a large error in the simulation using the derived information, it is necessary to determine the cause of the error and take countermeasures to solve the cause.

[0033] In production simulation, process information, production resource information, production control rule information, etc. are complexly interrelated and have the property that an error in one process propagates to other processes. In a production simulation with such a property, it is required to determine the main cause of the error. The system described below calculates the simulation error by comparing production performance with simulation results. Thereby, the cause of the error can be determined, and high-precision production simulation can be achieved. Thereby, a production plan can be formulated using production simulation to improve the feasibility and optimality of the production plan.

[0034] Figure 1A It is a functional block diagram of the production simulation device 100. As shown in the figure, the production simulation device 100 includes an input unit 110, a storage unit 120, a control unit 130, and a display unit 140.

[0035] The input unit 110 receives the input of various information from outside the production simulation device 100. The display unit 140 displays the information in the storage unit on the screen. The storage unit 120 includes a production performance data storage area 121, a production process data storage area 122, a production resource data storage area 123, a production control rule data storage area 124, and a simulation result data storage area 125.

[0036] The production performance data storage area 121 stores information on the past processing performance in the specified production process. The production process data storage area 122 stores information for determining information such as the process time of each process. The production resource data storage area 123 stores information for determining the operation time of production resources such as equipment and operators. The production control rule data storage area 124 stores information for determining production control rules such as the start order rule. The simulation result data storage area 125 stores information for determining the simulation result.

[0037] The control unit 130 includes a performance data extraction unit 131, a simulation model division unit 132, a performance reflection unit 133, a simulation execution unit 134, and a simulation error calculation unit 135.

[0038] Figure 1B Shows an example of the hardware and software structure of the production simulation device 100. Figure 1B In the example, the production simulation device 100 is composed of a single computer. The production simulation device 100 includes a processor 310, a memory 320, an auxiliary storage device 330, a network (NW) interface 340, an I / O interface 345, an input device 351, and an output device 352. The above components are connected to each other through a bus. The memory 320, the auxiliary storage device 330, or a combination thereof is a storage device including a non-transitory storage medium and can correspond to the storage unit 120.

[0039] The memory 320 is composed of a semiconductor memory, for example, and is mainly used to hold programs and data. The programs stored in the memory 320 include, in addition to an operating system (not shown), a performance data extraction program 321, a simulation model division program 322, a performance reflection program 323, a simulation execution program 324, a simulation error calculation program 325, and a user interface program 326.

[0040] The processor 310 executes various processes according to the programs stored in the memory 320. The processor 310 realizes various functional units by operating according to the programs. For example, the processor 310 functions as the control unit 130, specifically, the performance data extraction unit 131, the simulation model division unit 132, the performance reflection unit 133, the simulation execution unit 134, and the simulation error calculation unit 135 according to the above programs. The processor 310 functions as the input unit 110 and the display unit 140 by operating according to the user interface program 326.

[0041] The auxiliary storage device 330 is composed of a large-capacity storage device such as a hard disk drive and a solid-state drive, and is used to permanently store programs and data. The auxiliary storage device 330 stores the production performance data table 210, the production process data table 220, the equipment data table 230, the operator data table 240, the start order rule model data table 250, the equipment allocation rule data table 260, the operator allocation rule data table 270, and the simulation result data table 280.

[0042] The production performance data table 210 is an example of the information stored in the production performance data storage area 121. The production process data table 220 is an example of the information stored in the production process data storage area 122. The equipment data table 230 and the operator data table 240 are examples of the information stored in the production resource data storage area 123.

[0043] The start order rule model data table 250, the equipment allocation rule data table 260, and the operator allocation rule data table 270 are examples of the information stored in the production control rule data storage area 124. The simulation result data table 280 is an example of the information stored in the simulation result data storage area 125.

[0044] For the sake of convenience of explanation, the programs 321 to 326 are stored in the memory 320, and the tables 210, 220, 230, 240, 250, 260, 270, and 280 are stored in the auxiliary storage device 330. However, the storage location of the data of the production simulation device 100 is not limited. For example, the programs and data stored in the auxiliary storage device 330 are loaded into the memory 320 at startup or when necessary, and various processes of the production simulation device 100 are executed by the processor 310 executing the programs. Thus, the subjects of the following functional units, programs, the processor 310, or the processes performed by the production simulation device 100 can be interchanged.

[0045] The network interface 340 is an interface for connecting to a network. The production simulation device 100 communicates with other devices within the system via the network interface 340. The input device 351 is a hardware device for a user to input instructions and information, etc., and includes, for example, a keyboard and a pointing device. The output device 352 is a hardware device for presenting various images for input and output, and is, for example, a display device.

[0046] The production simulation device 100 includes one or more processors and one or more storage devices. Each processor can include a single or multiple arithmetic units or processing cores. The processor can be implemented, for example, as a central processing unit, a microprocessor, a microcomputer, a microcontroller, a digital signal processor, a state machine, a logic circuit, a graphics processing unit, a system-on-chip, and / or any device that operates on signals based on control instructions.

[0047] The functions of the production simulation device 100 can also be implemented by distributed processing performed by a computer system including a plurality of computers. The plurality of computers communicate with each other via a network and thereby collaboratively execute processing.

[0048] Figure 2 Shows a structural example of the production performance data table 210. The production performance data table 210 has a job ID column 211, an item ID column 212, a process number column 213, a process ID column 214, a start time column 215, a completion time column 216, an equipment column ID 217, an operator ID column 218, and an attribute information column 219. Each row of the production performance data table 210 is identified by a job ID and a process number.

[0049] The job ID column 211 stores information for identifying each production job (also simply referred to as a job). A job represents an object to be processed in a production process. The item ID column 212 stores information for specifying the item of the job. The process number column 213 stores information for specifying the order of the process for which the item should be processed. The process ID column 214 stores information for specifying the process of the item with this process number.

[0050] In addition, in the present embodiment, the process ID is unique with respect to the combination of the item ID and the process number, and the combination of the item ID and the process number is unique with respect to the process ID. In addition, each process in each job is referred to as a task. That is, one row in the production performance data table 210 corresponds to one task.

[0051] The start time column 215 and the completion time column 216 store information on the performance start time and the performance completion time of the process, respectively. The equipment ID column 217 and the operator ID column 218 store information for specifying the equipment and the operator for processing the process of the job, respectively. The attribute information column 219 stores attribute information related to the job and the process, such as the product name, size, delivery date of the job, and the required completion time of the process of the job.

[0052] Figure 3 Shows a structural example of the production process data table 220. The production process data table 220 has a process ID column 221, a process time column 222, one or more assignable equipment ID columns 223, and one or more assignable operator ID columns 224.

[0053] Each row of the production process data table 220 is specified by a process ID. The process ID column 221 stores information for identifying the process. The process time column 222 stores information representing the time required for processing the process. The assignable equipment ID column 223 and the assignable operator ID column 224 store information for identifying the equipment and the operator capable of processing the process, respectively.

[0054] Figure 4Shows an example of the structure of the equipment data table 230. The equipment data table 230 has an equipment ID column 231, a start time of operation column 232, and an end time of operation column 233. The equipment ID column 231 stores information for identifying the equipment. The start time of operation column 232 and the end time of operation column 233 store the start and end times of the operation of the equipment, respectively.

[0055] Figure 5 Shows an example of the structure of the operator data table 240. The operator data table 240 has an operator ID column 241, a start time of operation column 242, and an end time of operation column 243. The operator ID column 241 stores information for identifying the operator. The start time of operation column 242 and the end time of operation column 243 store the start and end times of the operation of the operator, respectively.

[0056] Stores as Figure 6 、 Figure 7 、 Figure 8 shown start order rule data table, equipment allocation rule data table, operator allocation rule data table.

[0057] Figure 6 Shows an example of the structure of the start order rule model data table 250. The start order rule model data table 250 has an equipment ID column 251 and a start order rule ID column 252. The equipment ID column 251 stores information for identifying the equipment. The start order rule ID column 252 stores information for identifying the start order rule in the equipment. The start order rule is a rule for determining the next process to be processed from the jobs waiting for processing in a certain equipment. As a representative rule, there are first in first out, in order of delivery date, etc.

[0058] Figure 7 Shows an example of the structure of the equipment allocation rule data table 260. The equipment allocation rule data table 260 has a process ID column 261 and an equipment allocation rule ID column 262. The process ID column 261 stores information for identifying the process. The equipment allocation rule ID column 262 stores information for identifying the equipment allocation rule in the process. The equipment allocation rule is a rule for determining which equipment to allocate to each task corresponding to the process when multiple allocable equipment are defined for one process in the production process data table 220.

[0059] Figure 8Shows an example of the structure of the operator assignment rule data table 270. The operator assignment rule data table 270 has a process ID column 271 and an operator assignment rule ID column 272. The process ID column 271 stores information for identifying a process. The operator assignment rule ID column 272 stores information for identifying the operator assignment rule in that process. The operator assignment rule is a rule for determining which operator to assign to each task corresponding to a process when multiple assignable operators are defined for one process in the production process data table 220.

[0060] Figure 9 Shows an example of the structure of the simulation result data table 280. The simulation result data table 280 has a simulation model ID column 281 and a simulation error column 282. The simulation model ID column 281 stores information for identifying a simulation model. The simulation error column 282 stores information representing the error of the simulation performed based on that simulation model.

[0061] In Figure 10 shows a flowchart of a series of processes in the control unit 130. Hereinafter, following this flowchart, the processes of the present embodiment will be described.

[0062] Steps S100 to S200 are processes performed by the performance data extraction unit 131. First, in step S100, the performance data extraction unit 131 obtains the start time and end time of the simulation period input by the user through the input unit 110. Let the start time and end time of the simulation period be t s and t f .

[0063] Next, in step S200, the performance data extraction unit 131 extracts the production performance data of the job group processed during the simulation period from the production performance data table 210. Thereafter, the production performance data extracted through this process is referred to as the target performance data.

[0064] Step S300 is a process of the simulation execution unit 134 and the simulation error calculation unit 135. The simulation execution unit 134 performs the simulation for the simulation period t s to t f using the information stored in the storage unit 120 and the above target performance data. The simulation error calculation unit 135 calculates the simulation error by comparing the simulation result with the target performance data. Thereafter, the simulation model used for the simulation during the period t s to t f is referred to as the overall simulation model M whole .

[0065] When performing the simulation, it is necessary to determine the simulation start time t sThe state of the production line at that time (hereinafter referred to as the initial state). Here, the state of the production line represents information on work groups waiting for process processing, as well as information on work groups, allocated equipment, and operators during the process processing. This information can be determined based on the above object performance information. In addition, when performing the simulation, the simulation period t s ~t f and information on the operations input to the production line during this period and their input times. This information can also be determined based on the above object performance data.

[0066] In addition, in this embodiment, the simulation error E is calculated by the following formula 1.

[0067] [Mathematical formula 1]

[0068]

[0069] Here, N task represents the total number of tasks in this simulation. t act k and t sim k respectively represent the performance of the k-th task and the completion time in the simulation. After that, the overall simulation error is referred to as E whole .

[0070] Steps S400 to S500 are processes performed by the simulation model division unit 132. In this embodiment, the simulation model division unit 132 divides the overall simulation model into two stages from the time perspective and the production resource perspective to obtain a plurality of sub-models.

[0071] Hereinafter, the model division process from the time perspective will be described. First, in step S400, the simulation model division unit 132 obtains the number N T of time perspective divisions of the simulation model through the input unit. Then, in step S500, the simulation model division unit 132 equally divides the simulation period t s ~t f into N T periods.

[0072] In addition, the division method is not limited. Here, the start time and end time of each divided period are set as t s i and t f i (i = 1, 2,..., N T ), and the model used for the simulation during the period t s i ~t f i is set as the sub-model M iBy such division, each sub-model only targets a part of the tasks in the overall simulation.

[0073] Specifically, sub-model M i only targets the tasks processed in the object performance data during period t s i ~t f i In addition, the information on the initial state of the production line at the simulation start time t s i and the information on the operations input to the production line during period t s i ~t f i and their input times can be determined based on the object performance data. Therefore, the simulations of each sub-model can be executed independently, and the sub-model with a relatively large simulation error can be determined.

[0074] Next, the division process from the perspective of production resources is described. In this process, each sub-model obtained by dividing through the above time perspective model is further divided into multiple sub-models from the perspective of production resources. The simulation model obtained by dividing sub-model M i from the perspective of resources is called sub-model M i,j (j = 1, 2,..., N R i , N R i is the number of divisions).

[0075] Here, during the division, the simulation model division unit 132 divides in such a way that production resources are not shared among multiple sub-models. In this embodiment, two division methods, namely division based on process data criteria and division based on production performance data criteria, are described.

[0076] In the division based on process data criteria, the simulation model division unit 132 first obtains the process group targeted by this sub-model from the task group targeted by sub-model M i . Then, the simulation model division unit 132 divides this process group into multiple sub-process groups. At this time, the sub-process groups are defined in such a way that any process X and any process Y belonging to a sub-process group different from process X do not share allocable equipment and operators. Then, the simulation model targeting the j-th sub-process group is set as sub-model M i,j .

[0077] In the division based on production performance data criteria, the simulation model division unit 132 divides sub-model M iThe task group used as an object is divided into multiple sub - task groups. At this time, the sub - task groups are defined in such a way that the equipment and operator in the production performance data of any task X are different from the equipment and operator in the production performance data of any task Y belonging to a sub - task group different from task X. Then, the simulation model for the j - th sub - task group is set as the sub - model M i,j .

[0078] Through the above - mentioned division, the simulation of each sub - model M i,j can be executed independently, and the sub - model with a relatively large simulation error can be determined. In addition, the two methods of process - data - based division and production - performance - data - based division can be switched according to the user's input, or the two methods can be automatically executed separately, and there is no specific limitation on their usage methods.

[0079] Step S600 is the processing of the simulation execution unit 134 and the simulation error calculation unit 135. In step S600, the simulation execution unit 134 executes the simulation of each sub - model M i obtained through the above - mentioned division of the time perspective and each sub - model M i,j obtained through the division of the production resource perspective.

[0080] For the processes in the sub - model that do not have a previous - level process or tasks that do not have a previous - level task, input operations according to the performance data. Between the sub - models divided based on production performance data, adjust the allocation rules of production resources (equipment and operators) as needed in such a way that production resources (equipment and operators) are not shared.

[0081] The simulation error calculation unit 135 calculates the error E i 、E i,j of each sub - model using Equation 1, and saves the calculation results in the simulation result data table 280.

[0082] Step S700 is the processing of the performance reflection unit 133. In this processing, for elements such as the process time and production control rules of each sub - model, reflect the information extracted from the production performance data table 210 to generate a new group of sub - models. In this embodiment, the methods for reflecting performance are described for the process time, start - work sequence rules, equipment allocation rules, and operator allocation rules. The elements reflecting performance information can be determined according to the design or user - specified requirements, for example.

[0083] Regarding the process time, the performance reflection unit 133 calculates the time from the start time to the completion time of the process in the production performance data as the process time of each task, and reflects it to the simulation model. That is, in the newly generated sub - model, the performance reflection unit 133 does not use the process time information defined in the production process data table 220, but uses the process time of each task calculated by the above - mentioned method.

[0084] Regarding the start order rule, the performance reflection unit 133 obtains the processing order of each task in each device from the production performance data table 210 and reflects it in the simulation model. That is, when selecting the next task to be processed from the group of tasks waiting to be processed in a certain device in the newly generated sub-model, the performance reflection unit 133 does not use the rule defined in the start order rule model data table 250, but selects the task with the earliest actual processing order from the group of tasks waiting to be processed.

[0085] Regarding the device allocation rule, the performance reflection unit 133 obtains the allocated device of each task from the production performance data table 210 and reflects it in the simulation model. That is, when selecting the allocated device of a certain task in the newly generated sub-model, the performance reflection unit 133 does not use the rule defined in the device allocation rule data table 260, but selects the actual allocated device of that task. Regarding the operator allocation rule, it is the same as the device allocation rule.

[0086] The processing in step S700 generates a new group of sub-models by switching between the cases of reflecting and not reflecting performance for the process time, start order rule, device allocation rule, and operator allocation rule of each sub-model. Thereafter, the sub-model M i,j The newly generated sub-model that reflects the performance information is called sub-model M a,b,c,d i,j . Here, a, b, c, and d are 0 or 1 indicating whether performance is reflected for the process time, start order rule, device allocation rule, and operator allocation rule respectively, and 1 indicates reflecting performance.

[0087] For example, M 1,0,0,0 i,j represents a model in which performance is reflected only for the process time in sub-model M i,j , and M 0,0,0,0 i,j is synonymous with M i,j . By comparing the errors of the multiple sub-models M a,b,c,d i,j obtained above, it is possible to determine the factors that have a greater impact on the error. For example, when the error of M 1,1,1,1 i,j is greater than the error of M 0,1,1,1 i,j , it can be interpreted that one of the main reasons for the error in M i,j is the process time.

[0088] Step S8000 is the processing of the simulation execution unit 134 and the simulation error calculation unit 135. In step S800, the simulation execution unit 134 executes each of the above sub-models M a,b,c,d i,jsimulation. The simulation error calculation unit 135 calculates the error E of each sub-model M using Equation 1 a,b,c,d i,j and saves the calculation result in the simulation result data table 280. a,b,c,d i,j In addition, the division of the overall simulation model based on the time perspective and / or the division of the overall simulation model based on the production resource perspective can be omitted. The performance information can be reflected in the overall simulation model to generate a new overall simulation model, or it can be reflected in the sub-model M of the time perspective

[0089] to generate a new sub-model. i The generation of the new overall simulation model or the new sub-model obtained by reflecting the performance information in the overall simulation model or the sub-model can also be omitted. The processing of S600 or S700 for a specific type of sub-model can also be omitted. For example, the processing of S600 for the sub-model divided based on the process data standard can be omitted, and the processing of S700 and 8000 can be executed.

[0090] As described above, by calculating the error between the production performance and the simulation result, the cause of the error can be determined, and high-precision production simulation can be achieved. Thus, a plan can be formulated using the production simulation to improve the feasibility and optimality of the production plan. In addition, like the time perspective division, the production resource perspective division, and the production performance reflection, by using the production performance instead of a part of the inferable information in the simulation during the simulation period, it is possible to more easily determine the factors that have a greater impact on the error.

[0091] In addition, like the time perspective division and the production resource perspective division, by dividing the production simulation model into multiple sub-models that can perform simulations independently of each other, and evaluating the simulation error for each sub-model, it is possible to determine the sub-model with a larger error. By reflecting the information extracted from the production performance data in the model elements such as the process time and production control rules in the overall simulation model or the sub-model, a new model group is generated. By comparing the errors in the case where the production performance information is reflected and the case where it is not reflected, it is possible to determine the model elements that have a greater impact on the error.

[0092] In

[0093] and Figure 11A and Figure 11B an example of the display screen of the information of the storage unit 120 displayed by the display unit 140 is shown. Figure 11A and Figure 11B respectively represent a part of a display screen. As Figure 11AAs shown, the screen displayed by the display unit 140, for example, includes an overall simulation result display area 141, a time viewpoint segmented sub-model simulation result display area 142, a pre-segmentation model selection area 143, and a production resource viewpoint segmented sub-model simulation result display area 144. As Figure 11B shown, the screen also has a pre-performance reflection model selection area 145, a performance-reflected sub-model simulation result display area 146, and a sub-model element evaluation result display area 147.

[0094] In the overall simulation result display area 141, the simulation result of the overall simulation model M whole is displayed. In the time viewpoint segmented sub-model simulation result display area 142, the simulation results of the sub-models M i segmented by time viewpoint are displayed. In the production resource viewpoint segmented sub-model simulation result display area 144, the simulation results of the sub-models M i selected in the pre-segmentation model selection area 143 and segmented by production resource viewpoint are displayed. i,j

[0095] In the performance-reflected sub-model simulation result display area 146, the simulation results of the sub-models M i,j selected in the pre-performance reflection model selection area 145 and with performance information reflected are displayed. a,b,c,d i,j In the sub-model element evaluation result display area, information indicating the degree of influence of each model element such as process time on the simulation error is displayed.

[0096] For example Figure 11B in the example shown, the comparison results of the errors between the cases where the performance of each model element of the sub-model M i,j is reflected and not reflected are displayed. Here, for example Figure 11B in the sub-model element evaluation result display area 147, the "average error with performance reflected" and "average error without performance reflected" in the "process time" row are the values calculated by the following formulas 2 and 3 respectively.

[0097] [Mathematical formula 2]

[0098]

[0099] [Mathematical formula 3]

[0100]

[0101] That is, the average error (without) reflecting performance represents the average of the errors of all sub-models for which the performance information of the target model element is (not) reflected. The comparison of these two average error values is used to determine for the sub-model M i,jIt is useful when there are elements that have a great impact on the error.

[0102] Figure 12 It is a schematic diagram of the production simulation system of this embodiment. As shown in the figure, the production simulation system includes a production simulation device 100, a production performance information management device 200, and a production condition information management device 300, which can send and receive information via a network 400. The production performance information management device 200 sends production performance data to the production simulation device 100. In addition, the production condition information management device 300 sends process data, production resource data, production control rule data, etc. to the production simulation device 100.

[0103] In addition, the present invention is not limited to the above-described embodiments and includes various modification examples. For example, the above-described embodiments are described in detail for easy understanding of the present invention and are not limited to having all the structures described. In addition, a part of the structure of a certain embodiment can be replaced with the structure of another embodiment, and the structure of another embodiment can also be added to the structure of a certain embodiment. In addition, for a part of the structure of each embodiment, other structures can be added, deleted, or replaced.

[0104] In addition, for the above-described various structures, functions, processing units, etc., for example, a part or all of them can be implemented in hardware by designing in an integrated circuit. In addition, the above-described various structures, functions, etc. can also be implemented in software by a processor interpreting and executing a program that realizes each function. Information such as a program, a table, and a file that realizes each function can be stored in a recording device such as a memory, a hard disk, an SSD (Solid State Drive), or a recording medium such as an IC card or an SD card. In addition, the control lines and information lines show those considered necessary for explanation and do not necessarily show all the control lines and information lines on the product. In fact, it can also be considered that almost all the structures are interconnected.

Claims

1. A production simulation device for estimating the progress of processes in a production line, characterized in that: It includes one or more processors and one or more storage devices, The one or more storage devices store: Production performance information, which includes information on the performance start time and performance completion time of each process of the production operation; And A simulation model, which includes the process time of each process, the production resource groups that can be allocated to each process, the operation time of each production resource in each of the production resource groups, and information on the production control rules of the production line, The one or more processors divide the simulation model into multiple sub-models that can be executed independently of each other, perform simulations using the production performance information and each of the multiple sub-models, and calculate the simulation error by comparing the production performance information with the results of each of the multiple sub-models, The processes of the production operation constitute tasks, The multiple sub-models take multiple sub-task groups as objects respectively, The production resources in the production performance information of any task in the multiple sub-task groups are different from the production resources in the production performance information of any task in a sub-task group different from the any task.

2. The production simulation device according to claim 1, characterized in that: The one or more processors use the information extracted from the production performance information to replace a part of the information that can be estimated in the simulation.

3. The production simulation device according to claim 1, characterized in that: The one or more processors, Generate new multiple simulation models by reflecting at least a part of the information extracted from the production performance information in the process time, the production resource groups that can be allocated, the operation time of the producible production resources, and the production control rules in the simulation model, Calculate the simulation error by comparing the production performance information with the simulation results of each of the new multiple simulation models.

4. The production simulation device according to claim 1, characterized in that: The one or more processors display the simulation error.

5. A production simulation device for estimating the progress of processes in a production line, characterized in that: It includes one or more processors and one or more storage devices, The one or more storage devices store: Production performance information, which includes information on the performance start time and performance completion time of each process of the production operation; And A simulation model, which includes the process time of each process, the production resource groups that can be allocated to each process, the operation time of each production resource in each of the production resource groups, and information on the production control rules of the production line, The one or more processors divide the simulation model into multiple sub-models that can be executed independently of each other, perform simulations using the production performance information and each of the multiple sub-models, and calculate the simulation error by comparing the production performance information with the results of each of the multiple sub-models, The multiple sub-models take multiple sub-process groups as objects respectively, The producible resources that can be allocated in the simulation model of any process in the multiple sub-process groups are different from the producible resources that can be allocated in the simulation model of any process in a sub-process group different from the any process.

6. The production simulation device according to claim 5, wherein: The one or more processors use the information extracted from the production performance information to replace a part of the information that can be deduced in the simulation.

7. The production simulation device according to claim 5, wherein: The one or more processors, generate a new plurality of simulation models by reflecting at least a part of the information extracted from the production performance information in the process time, the producible resource groups that can be allocated, the running time of the producible resources, and the production control rules in the simulation model, calculate the simulation error by comparing the production performance information with the simulation results of each of the new plurality of simulation models.

8. The production simulation device according to claim 5, wherein: The one or more processors display the simulation error.

9. A production simulation method executed by a device for estimating the progress of a process in a production line, wherein: The device stores: production performance information including information on the performance start time and performance completion time of each process of the production operation; and a simulation model including information on the process time of each process, the producible resource groups that can be allocated to each process, the running time of each producible resource in each producible resource group, and the production control rules of the production line, In the method, the device divides the simulation model into a plurality of sub-models that can be executed independently of each other, performs a simulation using the production performance information and each of the plurality of sub-models, and the device calculates a simulation error by comparing the production performance information with the results of each of the plurality of sub-models, The processes of the production operation constitute tasks, The plurality of sub-models target a plurality of sub-task groups respectively, The producible resources in the production performance information of any task in the plurality of sub-task groups are different from the producible resources in the production performance information of any task in a sub-task group different from the any task.

10. A production simulation method executed by a device for estimating the progress of a process in a production line, wherein: The device stores: production performance information including information on the performance start time and performance completion time of each process of the production operation; and a simulation model including information on the process time of each process, the producible resource groups that can be allocated to each process, the running time of each producible resource in each producible resource group, and the production control rules of the production line, In the method, the device divides the simulation model into a plurality of sub-models that can be executed independently of each other, performs a simulation using the production performance information and each of the plurality of sub-models, and the device calculates a simulation error by comparing the production performance information with the results of each of the plurality of sub-models, The plurality of sub-models target a plurality of sub-process groups respectively, The producible resources that can be allocated in the simulation model of any process among the multiple sub-process groups are different from the producible resources that can be allocated in the simulation model of any process of a sub-process group different from the any process.

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