Simulation Evaluation Methods for Reentrant Complex Manufacturing Systems

Abstract

This invention provides a simulation evaluation method for reentrant complex manufacturing systems, belonging to the field of manufacturing system simulation. This invention fully considers the complexity characteristics of manufacturing systems, including reentrancy, random order arrival, order splitting, and mixed machine types, thus improving the applicability of the simulation method. For the two key stages of job release and job dispatch, this invention proposes multiple rules for calculating job priority values ​​and designs a combined strategy using predicted influence functions and constraint programming. This comprehensively considers the influence of job nature and shop floor status, effectively controlling the work-in-process inventory while improving the on-time delivery rate of orders. The multiple performance testing objectives employed in this invention can effectively characterize the production system state and measure the effectiveness of the proposed strategies. In summary, the simulation method proposed in this invention can assist enterprise managers in evaluating the rationality of production plans without changing the actual production state, thereby reducing decision-making costs.

Description

Technical Field

[0001] This invention relates to the field of manufacturing system simulation, and more specifically to a simulation evaluation method for reentrant complex manufacturing systems. Background Technology

[0002] A reentrant complex manufacturing system refers to a manufacturing system in which workpieces return multiple times to the same work center (i.e., the workshop) during the production process to perform different operations. Its complexity stems from the strong coupling between the re-entrant flow and system dynamics (such as resource contention and disturbance propagation). For example: See [link to documentation]. Figure 1 Wafer manufacturing is a typical reentrant complex manufacturing system, with work centers including diffusion, photolithography, etching, ion implantation, metallization, and testing. Figure 1 This demonstrates that a job may visit a work center multiple times, i.e., it contains cyclic or reentrant process characteristics.

[0003] In related technologies, manufacturing system simulation work mostly simplifies production processes, including simplifying the production environment and order characteristics. For example, the paper (Kasper TAA, Land MJ, Teunter R H. Non-hierarchical work-in-progress control in manufacturing[J]. International Journal of Production Economics, 2023, 257: 108768.) only considers production scheduling optimization at bottleneck points, does not consider multiple visits to the same workshop by jobs, or assumes that each workshop can only process one job at a time and that each order corresponds to one job. These assumptions greatly reduce the complexity of the model and give the research priority in theory, but also increase the distance between the results and practical application. In addition, in enterprise practice, managers mostly use spreadsheets for production planning and control. Although the operation is simple, it also limits the integration and expansion of functions.

[0004] Therefore, it is necessary to provide a new simulation evaluation method for manufacturing systems, especially reentrant complex manufacturing systems, to effectively control shop floor load while assessing the feasibility and efficiency of production decisions, thereby improving the company's on-time delivery capability. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a simulation evaluation method for reentrant complex manufacturing systems, which solves the technical problem of effectively controlling workshop load while evaluating the feasibility and efficiency of production decisions.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A simulation evaluation method for reentrant complex manufacturing systems includes:

[0010] Step 1: Determine the configuration of order parameters and workshop parameters;

[0011] Step 2: Create the workshop environment as a basic component of the simulator;

[0012] Step 3: If the specified simulation duration is reached, calculate the performance indicators and output the simulation results; otherwise, proceed to step 4.

[0013] Step 4: Obtain the earliest arriving event from the event queue; if the event type is "order arrival", proceed to step 5; if the event type is "job completion", proceed to step 6; if the event type is "process completion", proceed to step 7; wherein the arrival of the order is random.

[0014] Step 5: Split the order into jobs and release the jobs, create the next order arrival event, and jump to step 8;

[0015] Step 6: Perform the job release operation and the job dispatch operation on the unprocessed job in the first workshop, and create a process completion event, then jump to step 8;

[0016] Step 7: Perform job dispatch operations on the job queues of the current workshop and the next workshop, and create a process completion event, then proceed to step 8;

[0017] Step 8: Add the new event to the event queue and jump to step 3.

[0018] Preferably, the randomness of order arrival means that the arrival time of the order follows an exponential distribution.

[0019] Preferably, the order parameters include the demand quantity, and the order splitting refers to:

[0020] Calculate the ratio of the demand to a fixed number of jobs and round up to split the order into an integer number of jobs.

[0021] Preferably, the job release operation refers to:

[0022] Based on the job release rules, calculate the overall priority value of the job; wherein the job release rules are one or any combination of the following: shortest total processing time priority, minimum capacity slack ratio priority, minimum corrected capacity slack ratio priority, and earliest planned release time priority;

[0023] Jobs are released sequentially based on their overall priority value.

[0024] Preferably, the job dispatch operation refers to:

[0025] Based on the job dispatch rules, the comprehensive priority value of a single job processed by the parallel machine is calculated; wherein the job dispatch rules are one or a combination of any of the following: short job priority rule, earliest process delivery date priority rule, improved process delivery date rule, shorter relaxation time priority rule, and shorter average relaxation time priority rule; the improved process delivery date rule refers to taking the larger value between the earliest process delivery date and the process delivery date.

[0026] Iterate through the waiting jobs in the job queue processed by the batch processor, create batches of jobs that are of the same type and have the same processing position, and calculate the overall priority value of the same batch of jobs based on the same job dispatch rules; the processing time of the same batch of jobs is the same as that of a single job.

[0027] Job assignments are performed sequentially based on the overall priority value of a single job or a batch of jobs.

[0028] Preferably, the performance index is one or a combination of any of the following: average production time of an order, average lead time, average delay time, and delay percentage.

[0029] A simulation and evaluation device for a reentrant complex manufacturing system, comprising:

[0030] The parameter configuration module is used to perform: Step 1, determine the configuration of order parameters and workshop parameters;

[0031] The environment creation module is used to perform: Step 2, creating the workshop environment as a basic component of the simulator;

[0032] The duration determination module is used to execute: Step 3: If the specified simulation duration is reached, calculate the performance indicators and output the simulation results; otherwise, proceed to step 4.

[0033] The type determination module is used to execute: Step 4, obtain the earliest arriving event from the event queue; if the event type is order arrival, proceed to step 5; if the event type is job completion, proceed to step 6; if the event type is process completion, proceed to step 7.

[0034] The order splitting module is used to perform: Step 5, split the order into jobs and release the jobs, create the next order arrival event, and jump to Step 8;

[0035] The job release module is used to perform: Step 6, execute the job release operation and perform the job dispatch operation on the unprocessed job in the first workshop, and create a process completion event, then jump to step 8;

[0036] The job dispatch module is used to perform: Step 7, perform job dispatch operations on the job queues of the current workshop and the next workshop, create a process completion event, and proceed to Step 8;

[0037] The queue update module is used to execute: Step 8, add the new event to the event queue, and then jump to Step 3.

[0038] A storage medium storing a computer program that causes a computer to execute the simulation evaluation method for a reentrant complex manufacturing system as described above.

[0039] An electronic device, comprising:

[0040] One or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including methods for performing the simulation evaluation method for reentrant complex manufacturing systems as described above.

[0041] (III) Beneficial Effects

[0042] This invention provides a simulation and evaluation method for reentrant complex manufacturing systems. Compared with existing technologies, it has the following advantages:

[0043] This invention is based on the concept of discrete event simulation. It simulates the production process of splitting orders, releasing jobs, and dispatching jobs after orders arrive randomly. This is more in line with enterprise practice. The simulation method provided can help managers evaluate the performance of decision-making schemes and optimize production plans without affecting actual production decisions, thereby achieving effective control of workshop load. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This invention provides a schematic diagram of a process flow for a reentrant complex manufacturing system.

[0046] Figure 2 A flowchart of a simulation evaluation method for a reentrant complex manufacturing system provided in an embodiment of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] The technical solution in this application is to solve the above-mentioned technical problems, and the general idea is as follows:

[0049] This invention addresses the load control problem in reentrant complex manufacturing systems (such as wafer fabrication). The problem can be briefly described as follows: the manufacturer first receives an order... And confirm the process menu (process flow) and order. demand ,Order Delivery period And other information. Then, the order is split into fixed-size orders. of One assignment, These operations are the smallest control units in the workshop, among which This indicates rounding up. Taking wafer manufacturing as an example, 25 wafers are typically considered a single job. Planners usually track the flow of the entire job, not individual wafers. To effectively control the total number of work-in-process (WIP) items circulating in the workshop, i.e., the total workload, reasonable job release and job dispatch strategies are needed. Specifically, job release determines whether a job enters the factory to begin processing, while dispatch determines the next job to begin processing in a particular workshop.

[0050] The main objective of this invention is to improve a company's on-time delivery capability by designing an improved discrete event simulation method and using it for production simulation in reentrant manufacturing systems. This method effectively controls shop floor load while evaluating the feasibility and efficiency of production decisions. In addition to the aforementioned discrete event simulation concept, the technical improvements also include:

[0051] 1) Design effective job release strategies to control the inflow rate of jobs, thereby improving the efficiency of controlling workshop load. Unlike the traditional approach of using a single strategy to determine job priority, this invention uses a combination of strategies to evaluate the nature of jobs from multiple perspectives, thereby providing better feedback on job urgency.

[0052] 2) Design effective job dispatch strategies to control the scheduling of jobs within the workshop. Unlike traditional dispatch rules that rely solely on order delivery times, this invention integrates multiple dispatch rules to optimize production efficiency.

[0053] 3) The embodiments of the present invention test the balance efficiency of job release strategy and job dispatch strategy between on-time delivery and work-in-process level of manufacturing system through multiple performance indicators.

[0054] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0055] Example 1:

[0056] like Figure 2 As shown, a simulation evaluation method for reentrant complex manufacturing systems includes:

[0057] Step 1: Determine the configuration of order parameters and workshop parameters;

[0058] Step 2: Create the workshop environment as a basic component of the simulator;

[0059] Step 3: If the specified simulation duration is reached, calculate the performance indicators and output the simulation results; otherwise, proceed to step 4.

[0060] Step 4: Obtain the earliest arriving event from the event queue; if the event type is "order arrival", proceed to step 5; if the event type is "job completion", proceed to step 6; if the event type is "process completion", proceed to step 7; wherein the arrival of the order is random.

[0061] Step 5: Split the order into jobs and release the jobs, create the next order arrival event, and jump to step 8;

[0062] Step 6: Perform the job release operation and the job dispatch operation on the unprocessed job in the first workshop, and create a process completion event, then jump to step 8;

[0063] Step 7: Perform job dispatch operations on the job queues of the current workshop and the next workshop, and create a process completion event, then proceed to step 8;

[0064] Step 8: Add the new event to the event queue and jump to step 3.

[0065] This invention addresses the load control problem in reentrant manufacturing systems, aiming to minimize average production time, average lead time, average delay time, and the percentage of order delays while ensuring stable production system load. To effectively simulate the manufacturing process, this invention uses discrete event simulation to model state changes in the production system as events, constructing an event queue in non-decreasing order of occurrence. More specifically, this invention also designs effective combined control strategies for the job release and job dispatch stages, as detailed below.

[0066] First, to clarify, some symbols will be introduced. When making decisions, let's assume the set of orders that have arrived but not yet entered the workshop is denoted as . For any order The split job set is ,in For orders The number of jobs split. Therefore, the total set of jobs to be released is: ,in Represents the union. The set of job types is... And the same type of operation has the same technological process. This is an index for job types. Taking wafer manufacturing as an example, it is a set of work centers (i.e., workshops). Among them, workshop Let 1 represent the diffusion center, and the rest represent photolithography, etching, ion implantation, metallization, and testing, respectively. Workshop The set of machines is , For machine indexing. Due to differences in machine functionality, the workshop Only some machines Capable of processing from type The assignment. (Set) In the workshop A collection of tasks in a queue. Indicates that they are in the workshop A set of processing tasks. Assign tasks. process set ,in For homework The number of processes, Indicate homework The Each process has a corresponding processing time of [number] steps. The corresponding workshop is represented as Because of the process The same process menu is used on the machines, so in the workshop Processing time It is the only certainty.

[0067] Based on the above supplementary information, in an optional implementation, such as Figure 2 As shown, the order parameters may include the unit time completion rate, process requirements, order type, demand quantity and delivery period, etc., and the workshop parameters may include the number of machines, machine capacity and buffer capacity, etc.

[0068] In an alternative implementation, the randomness of order arrival specifically means that the arrival time of the order follows an exponential distribution.

[0069] Furthermore, the average interval of order arrival time can be defined as λ, which can be set according to the characteristics of the process flow and the order characteristics. Consider setting a bottleneck workshop. The number of machines is m, the processing capacity of each machine is μ units per unit time, the average number of splittable jobs per order is n, the average number of re-entries is τ, and the machine utilization rate is ρ. Then... Bottleneck workshops refer to workshops that frequently experience wafer buildup due to insufficient production capacity.

[0070] After receiving an order, manufacturers typically determine the delivery date and production start date based on the workshop's production status (such as daily output, machine status, and staff scheduling). Due to machine capacity limitations, an order often needs to be broken down into multiple tasks, requiring managers to meticulously control the production start time of each task.

[0071] In an optional implementation, the order splitting specifically refers to:

[0072] Calculate the ratio of the demand to a fixed number of jobs and round up to split the order into an integer number of jobs.

[0073] Specifically, let's assume the size of the task is... For demand is orders It can be broken down into Each job. Taking wafer manufacturing as an example, assuming an order requires 100 wafers, it generally needs to be divided into 4 jobs, each with a size of 25.

[0074] Job release is a process of making production decisions based on workshop capacity and order delivery dates. In an optional implementation, the job release operation specifically refers to:

[0075] Based on the job release rules, the overall priority value of the job is calculated; the job release rules are one or any combination of the following: shortest total processing time priority, minimum capacity slack ratio priority, minimum corrected capacity slack ratio priority, and earliest planned release time priority.

[0076] Jobs are released sequentially based on their overall priority value.

[0077] Specifically, the four job release rules mentioned above are as follows:

[0078] (a) Shortest total processing time priority rule.

[0079] Calculation work Total processing time ,in Indicate homework The remaining set of processes is prioritized by the rule to shorten the average production time of the order by scheduling operations with shorter total processing time.

[0080] (b) Minimum capacity relaxation ratio takes precedence.

[0081] Calculation work capacity ratio ,in and Representing workshops The load limit and the current load and . The smaller the value, the higher the priority. If At this time, the workshop The load limit has been violated, and the job is now in a state of emergency. of It is given a sufficiently large positive value.

[0082] (c) The minimum corrected capacity relaxation ratio takes precedence.

[0083] Calculation work Corrected capacity relaxation ratio This ratio is similar to the capacity slack ratio, but it takes into account the location of the work process to reduce the impact of more distant processes on the current decision.

[0084] (d) The earliest planned release time takes priority.

[0085] Calculation work Planned release time ,in For the delivery period of the task, The work is done in the workshop. circulation time, Historical data averages can be used for assessment. The shorter the planned release time, the more urgent the operation, and the more priority it needs to be given.

[0086] Furthermore, in order to compress the above priority values ​​to between 0 and 1 and comprehensively consider multiple release rules, a predicted impact function is now introduced:

[0087]

[0088] in, This is the expected impact function. It is any priority value calculated using the above release rules. Let the set of preference values ​​be represented, then the assignment... Overall priority value It can be determined using the following formula:

[0089]

[0090] in, Corresponding to single rule priority value The weighting coefficients, and .

[0091] Once a job enters the workshop, it circulates between different workshops to fulfill processing requirements. Due to capacity limitations (such as the number of machines), some workshop buffer areas often have a large number of jobs waiting to be processed. The process of assigning priority to waiting jobs and allocating them to machines is called job dispatch. In an optional implementation, the job dispatch operation specifically refers to:

[0092] Considering the capacity difference between batch processing machines and parallel processing machines, it is first necessary to clarify that the job dispatch operation for parallel processing machines refers to:

[0093] Based on the job dispatch rules, the comprehensive priority value of a single job processed by the parallel machine is calculated; wherein the job dispatch rules are one or a combination of any of the following: short job priority rule, earliest process delivery date priority rule, improved process delivery date rule, shorter relaxation time priority rule, and shorter average relaxation time priority rule; the improved process delivery date rule refers to taking the larger value between the earliest process delivery date and the process delivery date.

[0094] Iterate through the waiting jobs in the job queue processed by the batch processor, create batches of jobs that are of the same type and have the same processing position, and calculate the overall priority value of the same batch of jobs based on the same job dispatch rules; the processing time of the same batch of jobs is the same as that of a single job.

[0095] Job assignments are performed sequentially based on the overall priority value of a single job or a batch of jobs.

[0096] Specifically, the five job dispatch rules mentioned above are as follows:

[0097] (a) Shortest job priority rule.

[0098] This rule only considers jobs waiting to be processed within the same workshop buffer area. If the job... and They are all in the workshop Queuing, and the corresponding processing time Therefore, processing operations should be given priority. process .

[0099] (b) The earliest process delivery date takes priority.

[0100] Operation process The formula for calculating the delivery period is as follows:

[0101]

[0102] in, Indicate process In the workshop The circulation time is equal to the sum of the waiting time and processing time of the operation, and is generally taken as the average value of historical data. It's homework. Delivery period.

[0103] (c) Improved process delivery time rules.

[0104] Unlike the above-mentioned job dispatch rule (b), this rule considers both the estimated completion time and the delivery date of the operation. process Delivery time of the corrected process ,in This indicates the current simulation time. To avoid order delays, orders with tighter delivery deadlines have higher priority.

[0105] (d) Shorter relaxation time is preferred.

[0106] Relaxation time is the remaining adjustable time for the task, and its calculation formula is: ,in It's homework. Delivery period, This is the time required to complete the remaining steps. The shorter the relaxation time, the less buffer time the planner can adjust, and the more urgent the task.

[0107] (d) Shorter average relaxation time is preferred.

[0108] Calculation work Mean relaxation time This variable represents the adjustable time for each remaining process step, where Indicate homework The remaining process set The number of processes.

[0109] Understandably, since batch processing machines can process multiple jobs simultaneously, full batch processing is necessary to avoid wasting output.

[0110] In an optional implementation, based on the description of job dispatching for parallel machines, job dispatching on parallel batch processing machines is divided into two steps:

[0111] First, create a batch and schedule it onto the machine. Note that batching is possible only if the job is batch-compatible. and If the two operations belong to the same type and have the same processing steps, that is... The embodiments of this invention employ a simple batching procedure: iterating through the waiting jobs in the queue and determining the batch size. No more than machine capacity ,Right now This is to ensure that the processing time for the entire batch is the same as the processing time for a single job.

[0112] Next, using the same job dispatch rules as the parallel machine (the only difference being that it applies to batches rather than individual jobs, and batch priority is determined based on the average value), the priority values ​​for each individual rule used by the batch processor are calculated. Taking the aforementioned short job priority rule as an example, Smaller batches have higher priority. In the workshop The first in the queue Processing time for each batch.

[0113] After determining the single rule priority value for a task or a batch of tasks, the expected impact function needs to be introduced again to compress the priority value to between 0 and 1, and the overall priority value is determined using the following formula:

[0114]

[0115] in, Corresponding to single rule priority value The weighting coefficients, and .

[0116] Specifically, if the process Since multiple machines are available, this embodiment of the invention preferentially selects the machine with lower versatility. The lower the versatility, the fewer types of jobs it can process.

[0117] To evaluate the effectiveness of the simulation method and to balance the relationship between work-in-process and timely delivery in the production system, in an optional implementation, the performance metric is one or a combination of any of the following: average production time, average lead time, average delay time, and delay percentage.

[0118] Specifically, the four performance indicators mentioned above are as follows:

[0119] (a) Average production time for an order.

[0120] Order Average production time Equal to the maximum completion time of the jobs included in the order Subtract the earliest release time ,Right now ,in Indicates the completion time of the task. This represents the job release time, with `max` and `min` being the maximization and minimization functions, respectively. The average production time is the mean of the production times for all orders within a specified simulation time period, which is passed as a parameter to the simulator.

[0121] (b) Average lead time of orders.

[0122] Order average lead time Equal to the maximum completion time of the jobs included in the order Subtract the order arrival time , that is .

[0123] (c) Average order delay time.

[0124] Order Average delay time ,in Indicates order Delivery period.

[0125] (d) Percentage of orders delayed.

[0126] The percentage of delayed orders out of the total number of orders within a specified simulation duration represents the on-time delivery rate.

[0127] It is understandable that the average values ​​of the above four indicators are statistical results within a specified simulation period, and all of them show that the smaller the average value, the better the simulation result.

[0128] Example 2:

[0129] This invention provides a simulation and evaluation device for a reentrant complex manufacturing system, comprising:

[0130] The parameter configuration module is used to perform: Step 1, determine the configuration of order parameters and workshop parameters;

[0131] The environment creation module is used to perform: Step 2, creating the workshop environment as a basic component of the simulator;

[0132] The duration determination module is used to execute: Step 3: If the specified simulation duration is reached, calculate the performance indicators and output the simulation results; otherwise, proceed to step 4.

[0133] The type determination module is used to execute: Step 4, obtain the earliest arriving event from the event queue; if the event type is order arrival, proceed to step 5; if the event type is job completion, proceed to step 6; if the event type is process completion, proceed to step 7.

[0134] The order splitting module is used to perform: Step 5, split the order into jobs and release the jobs, create the next order arrival event, and jump to Step 8;

[0135] The job release module is used to perform: Step 6, execute the job release operation and perform the job dispatch operation on the unprocessed job in the first workshop, and create a process completion event, then jump to step 8;

[0136] The job dispatch module is used to perform: Step 7, perform job dispatch operations on the job queues of the current workshop and the next workshop, create a process completion event, and proceed to Step 8;

[0137] The queue update module is used to execute: Step 8, add the new event to the event queue, and then jump to Step 3.

[0138] Example 3:

[0139] This invention provides a storage medium storing a computer program that causes a computer to execute the simulation evaluation method for a reentrant complex manufacturing system as described in Embodiment 1.

[0140] Example 4:

[0141] This invention provides an electronic device, comprising:

[0142] One or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including methods for performing simulation evaluation of reentrant complex manufacturing systems as described in Example 1.

[0143] It is understood that the reentrant complex manufacturing system simulation evaluation device, storage medium and electronic device provided in the embodiments of the present invention correspond to the reentrant complex manufacturing system simulation evaluation method provided in the embodiments of the present invention. The explanation, examples and beneficial effects of the relevant contents can be referred to the corresponding parts of the method, and will not be repeated here.

[0144] In summary, compared with existing technologies, it has the following beneficial effects:

[0145] 1. The embodiments of the present invention fully consider the complexity characteristics of manufacturing systems, including reentrancy, random order arrival, order splitting, and mixed machine types, thereby improving the applicability of the simulation method.

[0146] 2. This invention proposes multiple rules for calculating job priority values ​​for the two key stages of job release and job dispatch. It also adopts a combination strategy using the expected influence function and constraint programming to comprehensively consider the influence of job nature and workshop status. This effectively controls the amount of work-in-process in the manufacturing system while improving the on-time delivery rate of orders for enterprises.

[0147] 3. The multiple performance testing targets adopted in the embodiments of the present invention can effectively characterize the state of the production system and measure the effectiveness of the strategy proposed in the present invention.

[0148] 4. The simulation method proposed in this embodiment of the invention can assist enterprise administrators in evaluating the rationality of production plans without changing the actual production status, thereby reducing decision-making costs.

[0149] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0150] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A simulation evaluation method for a reentrant complex manufacturing system, characterized in that, include: Step 1: Determine the configuration of order parameters and workshop parameters; Step 2: Create the workshop environment as a basic component of the simulator; Step 3: If the specified simulation duration is reached, calculate the performance indicators and output the simulation results; Otherwise, proceed to step 4; Step 4: Obtain the earliest arriving event from the event queue; If the event type is "order arrived", proceed to step 5; if the event type is "job completed", proceed to step 6; if the event type is "process completed", proceed to step 7. The arrival of the orders mentioned above is random; Step 5: Split the order into jobs and release the jobs, create the next order arrival event, and jump to step 8; Step 6: Perform the job release operation and the job dispatch operation on the unprocessed job in the first workshop, and create a process completion event, then jump to step 8; Step 7: Perform job dispatch operations on the job queues of the current workshop and the next workshop, and create a process completion event, then proceed to step 8; Step 8: Add the new event to the event queue, then go back to step 3; The job release operation refers to: Based on the job release rules, the overall priority value of the job is calculated; wherein the job release rules are any combination of several of the following: shortest total processing time priority, minimum capacity slack ratio priority, minimum corrected capacity slack ratio priority, and earliest planned release time priority. Among them, assignments The capacity relaxation ratio is expressed as ,Operation The corrected capacity relaxation ratio is expressed as , For homework The number of processes, Indicate homework The remaining set of processes, Indicate homework The Processing time for each step and Representing workshops The load limit and the current load and , Indicate homework The The workshop corresponding to each process; Based on the overall priority value of the jobs, the jobs are released sequentially. The job dispatch operation refers to: Based on the job dispatch rules, calculate the comprehensive priority value of a single job processed by the parallel machine; wherein the job dispatch rules are any combination of the following: short job priority rule, earliest process delivery date priority rule, improved process delivery date rule, shorter relaxation time priority rule, and shorter average relaxation time priority rule. The improved process delivery date rule refers to taking the larger value between the earliest process delivery date and the process delivery date; relaxation time refers to the remaining adjustable time of the operation; and average relaxation time refers to the adjustable time of each remaining process. Iterate through the waiting jobs in the job queue processed by the batch processor, create batches of jobs that are of the same type and have the same processing position, and calculate the overall priority value of the same batch of jobs based on the same job dispatch rules; the processing time of the same batch of jobs is the same as that of a single job. Job assignments are performed sequentially based on the overall priority value of a single job or a batch of jobs.

2. The simulation and evaluation method for reentrant complex manufacturing systems as described in claim 1, characterized in that, The randomness of order arrivals means that the arrival time of orders follows an exponential distribution.

3. The simulation and evaluation method for reentrant complex manufacturing systems as described in claim 1, characterized in that, The order parameters include the demand quantity, and the order splitting refers to: Calculate the ratio of the demand to a fixed number of jobs and round up to split the order into an integer number of jobs.

4. The simulation and evaluation method for reentrant complex manufacturing systems as described in claim 1, characterized in that, The performance metrics are one or a combination of any of the following: average production time, average lead time, average delay time, and delay percentage.

5. A simulation and evaluation device for a reentrant complex manufacturing system, characterized in that, The method for performing simulation evaluation of a reentrant complex manufacturing system as described in any one of claims 1 to 4 includes: The parameter configuration module is used to perform: Step 1, determine the configuration of order parameters and workshop parameters; The environment creation module is used to perform: Step 2, creating the workshop environment as a basic component of the simulator; The duration determination module is used to execute: Step 3: If the specified simulation duration is reached, calculate the performance index and output the simulation results; otherwise, proceed to step 4. The type determination module is used to execute: Step 4, obtain the earliest arriving event from the event queue; if the event type is order arrival, proceed to step 5; if the event type is job completion, proceed to step 6; if the event type is process completion, proceed to step 7. The order splitting module is used to perform: Step 5, split the order into jobs and release the jobs, create the next order arrival event, and jump to Step 8; The job release module is used to perform: Step 6, execute the job release operation and perform the job dispatch operation on the unprocessed job in the first workshop, and create a process completion event, then jump to step 8; The job dispatch module is used to perform: Step 7, perform job dispatch operations on the job queues of the current workshop and the next workshop, create a process completion event, and proceed to Step 8; The queue update module is used to execute: Step 8, add the new event to the event queue, and then jump to Step 3.

6. A storage medium, characterized in that, It stores a computer program that causes a computer to perform the simulation evaluation method for a reentrant complex manufacturing system as described in any one of claims 1 to 4.

7. An electronic device, characterized in that, include: One or more processors; Memory; And one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including methods for performing simulation evaluation of reentrant complex manufacturing systems as described in any one of claims 1 to 4.

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