Finite buffer production line machine selection buffer distribution method and system based on Petri net

Abstract

The invention discloses a finite buffer production line machine selection buffer distribution method and system based on a Petri net. The method comprises the following steps: establishing a random Petri net model of a finite buffer production line according to a production demand and a to-be-optimized production line; determining an optimization target and obtaining production parameters according to existing production conditions and a production line structure; establishing a mixed integer linear programming problem model in combination with an optimization target and constraint conditions, performing initial machine selection and buffer allocation on a random Petri network model of the limited buffer production line, performing performance evaluation by solving the mixed integer linear programming problem model, and performing calculation to obtain a current minimum cost machine selection and buffer allocation scheme; performing a heuristic search method on the current minimum-cost machine selection and buffer allocation scheme to obtain a new machine selection and buffer allocation scheme; and solving a mixed integer linear programming problem and a heuristic search loop optimization strategy, and obtaining limited buffer production line machine selection and buffer distribution results when a search termination condition is reached.

Description

Technical Field

[0001] The present invention belongs to the technical field of high-end equipment manufacturing planning and layout, and in particular relates to a method and system for selecting and allocating buffers for machines in a finite buffer production line based on Petri nets. Background Art

[0002] In modern manufacturing, the design and optimization of manufacturing systems are the key to improving the competitiveness of enterprises. Limited buffer production lines are often used in manufacturing systems. By setting up buffers in the production line to store work-in-progress, it can effectively handle fluctuations in the production process and ensure the continuity and stability of the production process, becoming an important research direction in manufacturing system design. In the process of manufacturing system optimization, the rational allocation of resources is an inevitable problem. Insufficient resources may lead to production bottlenecks and increase the waiting time of work-in-progress; excessive resource allocation will cause resource redundancy and increase unnecessary costs. Unbalanced resource allocation, whether it is a lack of resources or redundancy, will have a negative impact on the flexibility and market responsiveness of the manufacturing system.

[0003] The optimal allocation of resources is the key to ensure the efficient operation of manufacturing systems, which needs to be achieved through performance evaluation and resource allocation cycle optimization. Petri nets are widely used in modeling and performance evaluation analysis of manufacturing systems because they can accurately describe and analyze the concurrency and synchronization characteristics in manufacturing systems. However, in the manufacturing process, a simple system needs to calculate the performance indicators of the production system and quickly and effectively allocate resources under the constraints of the performance indicators. Similar processes are very large and complex. For large-scale manufacturing systems such as limited buffer production lines that consider the joint configuration of buffers, machines and other resources, there are often problems such as state explosion and difficulty in rationally allocating resources. Summary of the invention

[0004] The purpose of the present invention is to overcome the problems of state explosion and difficulty in reasonable configuration when considering joint resource configuration in large-scale manufacturing systems, and propose a machine selection buffer allocation method and system for a finite buffer production line based on Petri nets.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme: The method for selecting buffer allocation of machines in a finite buffer production line based on Petri nets includes the following steps: S1. Randomly establish a limited buffer production line based on production demand and the production line to be optimized Petri Network model; S2, determine the optimization target and obtain production parameters according to the existing production conditions and production line structure; S3. Combine the optimization objectives and constraints to establish a mixed integer linear programming problem model. PetriThe network model is used to perform initial machine selection and buffer allocation, and the performance is evaluated by solving the mixed integer linear programming problem model and calculating the current minimum cost machine selection and buffer allocation scheme; S4, performing a heuristic search method on the current cost-minimizing machine selection and buffer allocation scheme to obtain a new machine selection and buffer allocation scheme; S5. Solve the mixed integer linear programming problem and heuristic search loop optimization strategy according to the new machine selection and buffer allocation scheme, and obtain the machine selection and buffer allocation results of the finite buffer production line by meeting the search termination conditions.

[0006] Furthermore, S1 is specifically as follows: according to the production demand and the finite buffer production line to be optimized, the processing situation, the number of processes and the connection relationship between each process are obtained, a finite buffer production line is established, and a random Petri The network model constructs the library set and transition set based on the processing situation and number of processes of the finite buffer production line; and constructs the pre-association matrix and post-association matrix according to the connection relationship of each process.

[0007] Furthermore, the limited buffer production line described in S1 is random Petri The network model is as follows:

[0008] in, Random for finite buffer production line Petri Network model, , P For the library collection, T For the transition set, Pre is the pre-incidence matrix, Post is the post-incidence matrix; is the average triggering delay of the transition component vector, where represents the set of non-negative real numbers.

[0009] Furthermore, the optimization goal described in S2 is to minimize the cost of configuring the buffer and the machine while satisfying the steady-state operation productivity requirements of the production line; The production parameters described in S2 include the number of materials in the raw material warehouse, the types of machines available for selection, the upper and lower capacity limits of the buffer zone, and the productivity requirements of the production line; The parameters of the selectable types of each machine include a selection vector of the selectable machine types in each machine library, a cost vector of different types of machines, and a processing time vector of different types of machines.

[0010] Furthermore, the mixed integer linear programming problem model established in S3 in combination with the optimization objective and constraints is as follows:

[0011] Among them, the formula is the objective function, ,Mode ,Mode ,Mode ,Mode ,Mode is a constraint condition; Objective Function represents the optimal optimization goal; constraint According to the law of Lute, Petri The performance evaluation method is obtained by converting in the network to make the steady-state productivity of the production line meet the productivity requirements through machine selection and buffer allocation; constraint Indicates machine selection and buffer allocation; constraint Indicates the machine type selected for each machine library and its machine cost and processing time; constraint Represents the capacity constraints in each buffer; For a random production line with a finite buffer Petri The initial identification in the network model is represented in terms of machine selection and buffer allocation; For each machine The type selected determines the cost, For each machine The type selected determines the processing time, For a random production line with a finite buffer Petri The network model performs initial machine type configuration; is the number of materials in the raw material warehouse, It is a warehouse for raw materials; For each machine library A selection vector of machine types available for selection in Type of machine , other machine types select The elements in the vector are 0; is the machine cost vector corresponding to different types; is the machine processing time vector corresponding to different types; The remaining capacity place in the buffer place The upper capacity limit in The remaining capacity place in the buffer place The lower capacity limit in ; is the buffer unit capacity cost; The productivity that the production line needs to meet.

[0012] Furthermore, the optimal optimization goal is to minimize the total resource cost of the final buffer total cost plus the total machine cost by configuring the buffer capacity and the machine type and quantity; The buffer storage place is composed of a pair of control storage places, namely, a storage place of remaining buffer capacity and a storage place of used buffer capacity.

[0013] Furthermore, the heuristic search method described in S4 is a random perturbation heuristic method. The random perturbation heuristic method specifically includes: randomly selecting a machine under the current machine type selection scheme and perturbing the current machine type.

[0014] Machine selection buffer allocation system for finite buffer production line based on Petri net, including random Petri The network model building module is used to establish a random network of limited buffer production lines according to production needs and production lines to be optimized. Petri Network model; The production parameter acquisition module is used to determine the optimization target and obtain the production parameters according to the existing production conditions and the production line structure; The current minimization cost solution acquisition module is used to establish a mixed integer linear programming problem model based on the optimization objectives and constraints, and to perform random Petri The network model is used to perform initial machine selection and buffer allocation, and the performance is evaluated by solving the mixed integer linear programming problem model and calculating the current minimum cost machine selection and buffer allocation scheme; A new solution acquisition module is used to obtain a new machine selection and buffer allocation solution by performing a heuristic search method on the current minimum cost machine selection and buffer allocation solution; The machine selection and buffer allocation result acquisition module is used to solve the mixed integer linear programming problem and the heuristic search loop optimization strategy according to the new machine selection and buffer allocation scheme, and obtain the machine selection and buffer allocation results of the finite buffer production line by reaching the search termination condition.

[0015] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for selecting buffer allocation of a finite buffer production line machine based on Petri net is implemented.

[0016] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for selecting a buffer allocation for a finite buffer production line machine based on Petri net is implemented.

[0017] Compared with the prior art, the present invention has the following beneficial technical effects: The Petri net-based finite buffer production line machine selection and buffer allocation method provided by the present invention can accurately describe the state of the production line, resources such as machines, the flow of buffers, and the interaction between them by establishing a random Petri net model step S1 of the finite buffer production line. The Petri net model, with its intuitiveness and powerful description ability, enables the complex dynamic process of the production line to be accurately presented. The Petri net model not only describes the static structure of the production line, but also reflects the dynamic operation process of the production line through the relationship between the transition representative operation or event and the state or resource represented by the library. This helps to deeply understand the operation law of the production line and provide a basis for optimization. In step S4, a heuristic search is performed on the current cost-minimizing machine selection and buffer allocation scheme to find a new and better solution. The heuristic search method, with its flexibility and efficiency, can effectively explore in the solution space and improve the possibility of finding a better solution. Through the loop optimization strategy in step S5, the loop optimization method can gradually approach the optimal solution or suboptimal solution, ensuring the accuracy and reliability of the optimization result. The present invention has the advantages of accurate modeling, clear optimization objectives, efficient solution, heuristic search and loop optimization. These benefits work together to optimize the production line, enabling it to meet production needs while minimizing costs and maximizing efficiency. This solves the problem of state explosion and difficulty in reasonable configuration when considering joint resource configuration in large-scale manufacturing systems.

[0018] Furthermore, according to the production demand and the production line to be optimized, the three key parameters of the number, type and buffer capacity of the machine are considered at the same time, and the type and quantity of raw materials, the type and related parameters of the machine available for selection in each process, the buffer capacity limit, the productivity requirements of the production line and other information are obtained to construct a random Petri net model of the finite buffer production line, and a heuristic search and solution mixed integer linear programming problem model loop optimization strategy is used to output the machine selection and buffer allocation plan and the corresponding minimum cost. Therefore, for the finite buffer production line, considering the problem that the state space of the joint configuration of the number, type and buffer capacity of machines increases exponentially with the increase in scale, the present invention can effectively avoid the state explosion exploration such as enumeration and random configuration, and efficiently solve the optimal (near-optimal) machine selection and buffer allocation plan that meets the productivity requirements of the finite buffer production line through the loop optimization strategy, so that the required cost is greatly reduced, and the benefits and enterprise competitiveness are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are for explanation purposes only and are not intended to limit the scope of the present invention in any way. In addition, the shapes and proportional dimensions of the components in the drawings are only for illustration purposes to help understand the present invention, and are not intended to specifically limit the shapes and proportional dimensions of the components of the present invention. In the drawings: Figure 1 It is a flowchart of a method for selecting buffer allocation of a machine of a finite buffer production line based on Petri nets of the present invention; Figure 2 It is a stochastic Petri net model of a finite buffer production line of a Petri net-based finite buffer production line machine selection buffer allocation method provided in Embodiment 6 of the present invention; Figure 3 It is a flow chart of a loop optimization strategy for a method for selecting a buffer allocation for a finite buffer production line machine based on Petri nets provided in Embodiment 6 of the present invention; Figure 4 It is a structural diagram of a machine selection buffer allocation system of a finite buffer production line based on Petri net provided by the present invention; Figure 5 It is an electronic device diagram of a finite buffer production line machine selection buffer allocation based on Petri net of the present invention; Figure 6 This is a schematic diagram of a machine selection buffer allocation device for a finite buffer production line based on Petri nets provided in Example 6 of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0021] Embodiment 1 See also Figure 1 , a machine selection buffer allocation method for a finite buffer production line based on Petri nets includes the following steps: S1. Randomly establish a limited buffer production line based on production demand and the production line to be optimized Petri Network model; S2, determine the optimization target and obtain production parameters according to the existing production conditions and production line structure; S3. Combine the optimization objectives and constraints to establish a mixed integer linear programming problem model. PetriThe network model is used to perform initial machine selection and buffer allocation, and the performance is evaluated by solving the mixed integer linear programming problem model and calculating the current minimum cost machine selection and buffer allocation scheme; S4, performing a heuristic search method on the current cost-minimizing machine selection and buffer allocation scheme to obtain a new machine selection and buffer allocation scheme; S5. Solve the mixed integer linear programming problem and heuristic search loop optimization strategy according to the new machine selection and buffer allocation scheme, and obtain the machine selection and buffer allocation results of the finite buffer production line by meeting the search termination conditions.

[0022] The present invention can effectively solve the problem of exponential growth of the state space of a finite buffer production line as the scale increases, considering the joint configuration of the number, type and buffer capacity of machines. It can avoid explosive exploration of states such as enumeration, and efficiently solve the machine selection and buffer allocation scheme that meets the productivity requirements of the finite buffer production line.

[0023] Embodiment 2 See also Figure 1 , a machine selection buffer allocation method for a finite buffer production line based on Petri nets includes the following steps: S1. Randomly establish a limited buffer production line based on production demand and the production line to be optimized Petri Network model; S2, determine the optimization target and obtain production parameters according to the existing production conditions and production line structure; S3. Combine the optimization objectives and constraints to establish a mixed integer linear programming problem model. Petri The network model is used to perform initial machine selection and buffer allocation, and the performance is evaluated by solving the mixed integer linear programming problem model and calculating the current minimum cost machine selection and buffer allocation scheme; S4, performing a heuristic search method on the current cost-minimizing machine selection and buffer allocation scheme to obtain a new machine selection and buffer allocation scheme; S5. Solve the mixed integer linear programming problem and heuristic search loop optimization strategy according to the new machine selection and buffer allocation scheme, and obtain the machine selection and buffer allocation results of the finite buffer production line by meeting the search termination conditions.

[0024] S1 is specifically as follows: according to the production demand and the finite buffer production line to be optimized, the processing situation, the number of processes and the connection relationship between each process are obtained, a finite buffer production line is established, and a random Petri The network model constructs the library set and transition set based on the processing situation and number of processes of the finite buffer production line; and constructs the pre-association matrix and post-association matrix according to the connection relationship of each process.

[0025] S1 limited buffer production line random Petri The network model is as follows:

[0026] in, Random for finite buffer production line Petri Network model, , P For the library collection, T For the transition set, Pre is the pre-incidence matrix, Post is the post-incidence matrix; is the average triggering delay of the transition component vector, where represents the set of non-negative real numbers.

[0027] The optimization goal described in S2 is to minimize the cost of configuring the buffer and the machine while meeting the production line's steady-state operating productivity requirements; The production parameters described in S2 include the number of materials in the raw material warehouse, the types of machines available for selection, the upper and lower capacity limits of the buffer zone, and the productivity requirements of the production line; The parameters of the selectable types of each machine include a selection vector of the selectable machine types in each machine library, a cost vector of different types of machines, and a processing time vector of different types of machines.

[0028] The mixed integer linear programming problem model established in S3 in combination with the optimization objectives and constraints is as follows:

[0029] Among them, the formula is the objective function, ,Mode ,Mode ,Mode ,Mode ,Mode is a constraint condition; Objective Function represents the optimal optimization goal; constraint According to the law of Lute, Petri The performance evaluation method is obtained by converting in the network to make the steady-state productivity of the production line meet the productivity requirements through machine selection and buffer allocation; constraint Indicates machine selection and buffer allocation; constraint Indicates the machine type selected for each machine library and its machine cost and processing time; constraint Represents the capacity constraints in each buffer; For a random production line with a finite buffer Petri The initial identification in the network model is represented in terms of machine selection and buffer allocation; For each machine The type selected determines the cost, For each machine The type selected determines the processing time, For a random production line with a finite buffer Petri The network model performs initial machine type configuration; is the number of materials in the raw material warehouse, It is a warehouse for raw materials; For each machine library A selection vector of machine types available for selection in Type of machine , other machine types select The elements in the vector are 0; is the machine cost vector corresponding to different types; is the machine processing time vector corresponding to different types; The remaining capacity place in the buffer place The upper capacity limit in The remaining capacity place in the buffer place The lower capacity limit in ; is the buffer unit capacity cost; The productivity that the production line needs to meet.

[0030] The optimal optimization goal is to minimize the total resource cost of the final buffer total cost plus the total machine cost by configuring the buffer capacity and the machine type and quantity; The buffer storage place is composed of a pair of control storage places, namely, a storage place of remaining buffer capacity and a storage place of used buffer capacity.

[0031] The heuristic search method described in S4 is a random perturbation heuristic method. Specifically, the random perturbation heuristic method includes: randomly selecting a machine under the current machine type selection scheme and perturbing the current machine type.

[0032] Embodiment 3 See also Figure 4 , a machine selection buffer allocation system for a finite buffer production line based on Petri nets, including random Petri The network model building module is used to establish a random network of limited buffer production lines according to production needs and production lines to be optimized. Petri Network model; The production parameter acquisition module is used to determine the optimization target and obtain the production parameters according to the existing production conditions and the production line structure; The current minimization cost solution acquisition module is used to establish a mixed integer linear programming problem model based on the optimization objectives and constraints, and to perform random Petri The network model is used to perform initial machine selection and buffer allocation, and the performance is evaluated by solving the mixed integer linear programming problem model and calculating the current minimum cost machine selection and buffer allocation scheme; A new solution acquisition module is used to obtain a new machine selection and buffer allocation solution by performing a heuristic search method on the current minimum cost machine selection and buffer allocation solution; The machine selection and buffer allocation result acquisition module is used to solve the mixed integer linear programming problem and the heuristic search loop optimization strategy according to the new machine selection and buffer allocation scheme, and obtain the machine selection and buffer allocation results of the finite buffer production line by reaching the search termination condition.

[0033] Embodiment 4 See also Figure 5 , an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the Petri net-based finite buffer production line machine selection buffer allocation method is implemented.

[0034] Embodiment 5 A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for selecting a buffer allocation for a finite buffer production line machine based on Petri net is implemented.

[0035] Embodiment 6 The present invention provides Figure 1 The method for selecting buffer allocation of machines in a finite buffer production line based on Petri nets is shown, and the main steps include: Step S1: Establish a random flow of limited buffer production lines according to production demand and production lines to be optimized. Petri Network model; Optionally, randomly Petri The network is a random network, each transition Both with a trigger delay Related, among which is a non-negative continuous random variable with an exponential distribution function : ,and It is a change The trigger rate of the transition is .

[0036] The finite buffer production line is random Network Model : ,in It is a collection of places, consisting of raw material places , Machine Library and buffers (a pair of control locations and )composition; For the transformation set, by the machine transformation composition, is the pre-correlation function, is the post-correlation function, and the overall correlation matrix of the network structure , which specifies how the directed arcs in the network are connected. is the average triggering delay of the transition -component vector, where Represents a set of non-negative real numbers. In this example, its actual meaning is the average processing time of the machine.

[0037] In a specific embodiment, according to random Definition of network structure and implementation examples Actual production requirements and production lines to be optimized are established as follows Figure 2 The randomness of the finite buffer production line shown Network model and generate the pre-correlation matrix , post-incidence matrix 、 Incidence Matrix .in , It is a warehouse for raw materials; , , , , For machine storage; and , and , and , and It is a buffer storage place; , , , , For machine transformation, is the machine processing time vector corresponding to the selected machine type.

[0038] step :Determine the optimization target and obtain relevant parameters such as the number of raw materials, the types of each machine available, the upper and lower capacity limits of the buffer zone, and the productivity requirements that the production line needs to meet based on the existing production conditions and the production line structure; In a specific embodiment, , is the raw material warehouse, and the number of raw materials waiting to be processed is initially set to 1000, that is, ; buffer (consisting of a pair of control libraries and Composition) , , , Indicates the remaining buffer capacity, its upper limit , lower limit ; Buffer unit capacity cost for Ten thousand yuan; the production line needs to meet the productivity ; Each machine has different machine types to choose from. Different types of machines have different processing times and costs. The specific selection options are shown in Table 1 below Table 1 Each machine has different machine type selection options

[0039] step : Randomization of a finite buffer production line The network model is used to perform initial machine selection and buffer allocation, and the performance is evaluated by solving the corresponding mixed integer linear programming problem to obtain the minimum cost machine selection and buffer allocation that currently meets the productivity requirements; Randomization of a finite buffer production line Network model for initial machine type configuration , so each machine can The type of selection determines its cost and the processing time vector is respectively and .

[0040] The mixed integer linear programming problem in this example combined with the optimization objective and constraints is:

[0041] in .

[0042] Solving the corresponding mixed integer linear programming problem can obtain a complete machine selection and buffer allocation plan that meets the productivity requirements and minimizes the total cost under the current machine type selection plan. and minimum cost Ten thousand yuan.

[0043] step :According to the current machine selection and buffer allocation scheme, a new machine selection and buffer allocation scheme is obtained by heuristic search; Select the machine type Solve the mixed integer linear programming problem to obtain the minimum cost that meets the production line productivity requirements After that, through the current plan A random perturbation heuristic method is used under the constraint that the machine can choose the solution (the current solution Randomly select a machine and randomly select from the available machine types) to generate a new machine type selection scheme The specific disturbance method is to select a solution for the current machine type. Next, randomly select a machine and perturb its current machine type. In this example, , after the disturbance, , indicating that the machine Perturb the current machine type to generate a new machine type selection scheme .

[0044] step :By solving mixed integer linear programming problems and heuristic search loop optimization strategies, the reasonable machine selection and buffer allocation results of the finite buffer production line can be completed when the search termination conditions are met.

[0045] Choose a plan based on the new machine type , solving the corresponding mixed integer linear programming problem to obtain a new complete machine selection and buffer allocation scheme and minimum cost Ten thousand yuan.

[0046] By solving the mixed integer linear programming problem and the heuristic search loop optimization strategy, the machine selection and buffer allocation scheme of the finite buffer production line can be obtained by reaching the search termination condition. Select one of the multiple execution programs of this example to perform the detailed optimization process for specific steps such as Figure 3 The loop optimization strategy shown is described, and the taboo table of the execution process is Some of the contents are shown in Table 2: Table 2 Taboo table of loop optimization strategy execution process Part of the content

[0047] First, the initial machine type selection scheme is generated through steps 1-4 , enter the taboo table And get a complete machine selection and buffer allocation solution And the corresponding minimum cost If the iteration termination condition is not reached, execute step 5 to obtain a new machine type selection scheme after perturbation. , determine the current machine type selection scheme through step 6 Is it on the taboo list? If there is, execute step 11 to select the machine type before the disturbance Alternative new machine type selection scheme Select the solution as the current machine type; otherwise, execute step 7 to get the solution Complete machine selection and buffer allocation scheme under And the corresponding minimum cost , execute step 8 to determine the minimum cost Is it smaller? If yes, execute step 10 to configure the machine type after the disturbance. as the current machine type; otherwise, execute step 9 to configure the perturbed machine type. Deposit taboo table and execute step 11 to configure the machine type before the disturbance As the current machine type solution. After executing the above series of steps, return to step 4 to determine whether the iteration termination condition is met, and repeat the above steps until the iteration termination condition is met to output the final complete machine selection and buffer allocation solution. And the corresponding minimum cost .

[0048] It is worth noting that: on the one hand, in the above taboo table The cost of the solution 1 to solution 2 is reduced, and the reduced solution is still recorded in the taboo table The reason is that there are actually additional solutions in the process of changing from solution 1 to solution 2. After the disturbance of solution 1, it is not directly transformed into solution 2, but into several other solutions that can achieve lower costs. After using this solution as the current machine type selection solution and then perturbing it, the minimum cost of solution 2 is obtained, which is not as good as this solution. Therefore, solution 2 is entered into the taboo table. The same is true for the transition from Scheme 4 to Scheme 5 and from Scheme 5 to Scheme 6; On the other hand: The above table only shows the taboo table in the actual iteration process Part of the content, plan 6 is the minimum cost finally achieved One of the corresponding solutions, taboo table The optimal solution is found before the iteration is terminated, which shows that for manufacturing systems of different scales, the setting of the number of iterations has a certain impact on the results.

[0049] In this example, the optimal configuration results obtained after the iteration termination conditions are met are shown in Table 3: Table 3 The optimal configuration results obtained after this example meets the iteration termination conditions

[0050] ; Ten thousand yuan.

[0051] The embodiment of the present invention aims to provide a machine selection and buffer allocation method for a finite buffer production line based on Petri nets, which is used to solve the problem that the state space of a finite buffer production line that considers the joint configuration of the number of machines, types and buffer capacity grows exponentially as the scale increases. The present invention can reduce the complexity of the state space from the joint configuration complexity of the number of machines, types and buffer capacity to the complexity of considering the machine type by solving the mixed integer linear programming problem model, and can avoid state explosive exploration such as enumeration and random configuration, and efficiently solve the optimal (near-optimal) machine selection and buffer allocation scheme that meets the productivity requirements of the finite buffer production line through a loop optimization strategy.

[0052] This embodiment also provides a finite buffer production line machine selection and buffer allocation device 500 based on Petri net, such as Figure 6 As shown, the device includes a memory 501, which inherits a high-speed random access memory (RAM) and a non-volatile memory (such as a disk, a flash memory, etc.), and is divided into a program storage area and a data storage area, which are respectively used for computer programs and data generated during operation; a processor 502, which is used to load and execute data, instructions, programs, etc. stored in the memory, and can implement the steps of the limited buffer production machine selection and buffer allocation method described in the above embodiment.

[0053] The types of limited buffer production lines mentioned in this embodiment are not all types that can be solved by this method, and the method of the present invention is applicable to many types of manufacturing systems; the scale of the limited buffer production line in the embodiment of the present invention is not the maximum scale that can be solved by the present invention, and the method of the present invention also has advantages for examples derived from large-scale manufacturing systems (with extremely large state space).

[0054] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0055] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0056] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0057] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention is described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation of the present invention can still be modified or replaced by equivalents, and any modification or equivalent replacement that does not deviate from the spirit and scope of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A finite buffer production line machine selection buffer allocation method based on Petri net, characterized in that: The following steps are involved: S1. Randomly establish a limited buffer production line based on production demand and the production line to be optimized Petri Network model; S2, determine the optimization target and obtain production parameters according to the existing production conditions and production line structure; S3. Combine the optimization objectives and constraints to establish a mixed integer linear programming problem model. Petri The network model is used to perform initial machine selection and buffer allocation, and the performance is evaluated by solving the mixed integer linear programming problem model and calculating the current minimum cost machine selection and buffer allocation scheme; S4, performing a heuristic search method on the current cost-minimizing machine selection and buffer allocation scheme to obtain a new machine selection and buffer allocation scheme; S5. Solve the mixed integer linear programming problem and heuristic search loop optimization strategy according to the new machine selection and buffer allocation scheme, and obtain the machine selection and buffer allocation results of the finite buffer production line by meeting the search termination conditions.

2. The method for selecting buffer allocation for a finite buffer production line machine based on Petri net according to claim 1, characterized in that: S1 is specifically as follows: according to the production demand and the finite buffer production line to be optimized, the processing situation, the number of processes and the connection relationship between each process are obtained, a finite buffer production line is established, and a random Petri The network model constructs the library set and transition set based on the processing situation and number of processes of the finite buffer production line; and constructs the pre-association matrix and post-association matrix according to the connection relationship of each process.

3. The method for selecting buffer allocation for a finite buffer production line machine based on Petri net according to claim 1, characterized in that: S1 limited buffer production line random Petri The network model is as follows: in, Random for finite buffer production line Petri Network model, , P For the library collection, T For the transition set, Pre is the pre-incidence matrix, Post is the post-incidence matrix; is the average triggering delay of the transition component vector, where represents the set of non-negative real numbers.

4. The method for selecting buffer allocation for a finite buffer production line machine based on Petri net according to claim 1, characterized in that: The optimization goal described in S2 is to minimize the cost of configuring the buffer and the machine while meeting the production line's steady-state operating productivity requirements; The production parameters described in S2 include the number of materials in the raw material warehouse, the types of machines available for selection, the upper and lower capacity limits of the buffer zone, and the productivity requirements of the production line; The parameters of the selectable types of each machine include a selection vector of the selectable machine types in each machine library, a cost vector of different types of machines, and a processing time vector of different types of machines.

5. The method for selecting buffer allocation for a finite buffer production line machine based on Petri net according to claim 4, characterized in that: The mixed integer linear programming problem model established in S3 in combination with the optimization objectives and constraints is as follows: Among them, the formula is the objective function, ,Mode ,Mode ,Mode ,Mode ,Mode is a constraint condition; Objective Function represents the optimal optimization goal; constraint According to the law of Lute, Petri The performance evaluation method is obtained by converting in the network to make the steady-state productivity of the production line meet the productivity requirements through machine selection and buffer allocation; constraint Indicates machine selection and buffer allocation; constraint Indicates the machine type selected for each machine library and its machine cost and processing time; constraint Represents the capacity constraints in each buffer; For a random production line with a finite buffer Petri The initial identification in the network model is represented in terms of machine selection and buffer allocation; For each machine The type selected determines the cost, For each machine The type selected determines the processing time, For a random production line with a finite buffer Petri The network model performs initial machine type configuration; is the number of materials in the raw material warehouse, It is a warehouse for raw materials; For each machine library A selection vector of machine types available for selection; when selecting Type of machine , other machine types select The elements in the vector are 0; is the machine cost vector corresponding to different types; is the machine processing time vector corresponding to different types; The remaining capacity place in the buffer place The upper capacity limit in The remaining capacity place in the buffer place The lower capacity limit in ; is the buffer unit capacity cost; The productivity that the production line needs to meet.

6. The method for selecting buffer allocation for a machine of a finite buffer production line based on Petri net according to claim 5, characterized in that: The optimal optimization goal is to minimize the total resource cost of the final buffer total cost plus the total machine cost by configuring the buffer capacity and the machine type and quantity; The buffer storage place is composed of a pair of control storage places, namely, a storage place of remaining buffer capacity and a storage place of used buffer capacity.

7. The method for selecting buffer allocation for a finite buffer production line machine based on Petri net according to claim 1, characterized in that: The heuristic search method described in S4 is a random perturbation heuristic method. Specifically, the random perturbation heuristic method includes: randomly selecting a machine under the current machine type selection scheme and perturbing the current machine type.

8. A finite buffer production line machine selection buffer allocation system based on Petri net, characterized in that: Include random Petri The network model building module is used to establish a random network of limited buffer production lines according to production needs and production lines to be optimized. Petri Network model; The production parameter acquisition module is used to determine the optimization target and obtain the production parameters according to the existing production conditions and the production line structure; The current minimization cost solution acquisition module is used to establish a mixed integer linear programming problem model based on the optimization objectives and constraints, and to perform random Petri The network model is used to perform initial machine selection and buffer allocation, and the performance is evaluated by solving the mixed integer linear programming problem model and calculating the current minimum cost machine selection and buffer allocation scheme; A new solution acquisition module is used to obtain a new machine selection and buffer allocation solution by performing a heuristic search method on the current minimum cost machine selection and buffer allocation solution; The machine selection and buffer allocation result acquisition module is used to solve the mixed integer linear programming problem and the heuristic search loop optimization strategy according to the new machine selection and buffer allocation scheme, and obtain the machine selection and buffer allocation results of the finite buffer production line by reaching the search termination condition.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the Petri net-based finite buffer production line machine selection buffer allocation method described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the Petri net-based finite buffer production line machine selection buffer allocation method described in any one of claims 1 to 7.

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