Regional layout optimization method and device, equipment and medium

Abstract

The invention provides a regional layout optimization method and device, equipment and a medium, and the method comprises the steps: initializing layout limitation information, product combination information and product technological process information, and randomly generating an initialized layout scheme; optimizing the initialized layout scheme by using a genetic algorithm to obtain an optimal layout scheme; in each round of iteration process, the following steps are executed: calculating the fitness of the first layout schemes, and performing genetic selection from the first layout schemes according to the fitness to obtain a second layout scheme; performing crossover variation on the second layout scheme to obtain a third layout scheme; judging whether an iteration stopping condition is met or not, and if yes, outputting an optimal layout scheme; and otherwise, carrying out the next round of iteration until an iteration stopping condition is met. The initial group is automatically generated by using the genetic algorithm, the subjectivity of manual drawing of the position correlation graph is eliminated, the subjectivity of optimization actions and the limitation of the number of optimization samples in the manual calculation process are eliminated, time and manpower are saved, and efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of semiconductor technology, and particularly to a method, device, equipment and medium for optimizing regional layout. Background Art

[0002] In a clean room in a semiconductor factory, various types of products can be prepared. For each product, it needs to go through many steps to be completed. For example, it successively goes through steps such as diffusion (Diffusion, Diff), deposition, and etching (Etch). Then, the wafers need to be successively transported to the areas where diffusion equipment, thin film deposition equipment, and etching equipment are located for processing to obtain the final products. With the booming development of the semiconductor foundry industry, how to design the optimal clean room layout has become an important topic. A reasonable clean room layout can greatly reduce the pressure on the automated transportation system and help shorten the production cycle of products.

[0003] The clean room layout in the industry uses the systematic layout design mode, that is, a logistics relationship table is calculated based on the determined product portfolio and process flow. The logistics relationship table includes the number of transports between two regions. Then, a location correlation diagram is drawn according to the logistics relationship table. As shown in the reference Figure 1 In the figure, the serial numbers represent different regions. The logistics relationship table can determine the relative positions of each region to obtain the initial layout, and then manual optimization attempts are made to achieve regional layout optimization. However, this requires a large amount of manual calculation, wasting time and manpower, and the optimization scheme is highly subjective. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method, device, equipment and medium for optimizing regional layout, which can eliminate the subjectivity of the optimization actions and the limitation of the number of optimization samples in the manual calculation process, and then find the optimal solution to determine the optimal layout scheme, which can avoid manual calculation, save time and manpower, and improve efficiency. The specific scheme is as follows:

[0005] This application provides a method for optimizing regional layout, including:

[0006] Initialize the layout constraint information, product portfolio information, and product process flow information, and randomly generate an initial layout scheme; the initial layout scheme includes the location information of each equipment storage area;

[0007] Use the genetic algorithm to optimize the initial layout scheme to obtain the optimal layout scheme; in each round of iteration, the following steps are executed:

[0008] Calculate the fitness for the first layout plan, and perform genetic selection from the first layout plan according to the fitness to obtain a second layout plan; in the first iteration process, the first layout plan is the initialized layout plan;

[0009] Perform crossover and mutation on the second layout plan to obtain a third layout plan;

[0010] Determine whether the iteration stop condition is satisfied. If it is satisfied, output the optimal layout plan; otherwise, use the third layout plan as the first layout plan and perform the next round of iteration until the iteration stop condition is satisfied.

[0011] Specifically, the fitness is the total material flow intensity, the total material flow intensity is the sum of n material flow intensities, n is the number of pairwise combinations of the equipment storage areas, and n is a positive integer.

[0012] Specifically, the material flow intensity is determined according to the transportation times between two equipment storage areas and the transportation distance between the two equipment storage areas.

[0013] Specifically, the performing crossover and mutation on the second layout plan to obtain a third layout plan includes:

[0014] Perform gene crossover processing on two second layout plans to obtain a fourth layout plan;

[0015] Perform random mutation on the fourth layout plan to obtain the third layout plan.

[0016] Specifically, the layout restriction information includes at least one of factory building information, size restrictions of the equipment storage areas, and process restrictions.

[0017] Specifically, the iteration stop condition includes that the number of iterations reaches a preset number or the fitness is greater than a preset fitness.

[0018] Specifically, the method further includes:

[0019] Compare the initialized layout plan with the optimal layout plan and output the comparison result.

[0020] This application also provides a regional layout optimization device, including:

[0021] An initialization unit, configured to initialize layout restriction information, product combination information, and product process flow information, and randomly generate an initialized layout plan; the initialized layout plan includes the position information of each equipment storage area;

[0022] An optimization unit for optimizing the initialized layout scheme using a genetic algorithm to obtain an optimal layout scheme; in each round of iteration, the following steps are executed:

[0023] Calculate the fitness of the first layout scheme, and perform genetic selection from the first layout scheme according to the fitness to obtain a second layout scheme; in the first iteration process, the first layout scheme is the initialized layout scheme;

[0024] Perform crossover and mutation on the second layout scheme to obtain a third layout scheme;

[0025] Judge whether the iteration stop condition is satisfied. If it is satisfied, output the optimal layout scheme; otherwise, use the third layout scheme as the first layout scheme and perform the next round of iteration until the iteration stop condition is satisfied.

[0026] On the other hand, an embodiment of the present application provides a computer device, which includes a processor and a memory:

[0027] The memory is used to store program codes and transmit the program codes to the processor;

[0028] The processor is used to execute the method described in the above aspect according to the instructions in the program codes.

[0029] On the other hand, an embodiment of the present application provides a computer-readable storage medium, which is used to store a computer program, and the computer program is used to execute the method described in the above aspect.

[0030] The embodiments of the present application provide a method, apparatus, device and medium for optimizing regional layout, which initialize layout constraint information, product portfolio information and product process flow information, and randomly generate an initial layout plan; the initial layout plan includes the location information of each equipment storage area; use the genetic algorithm to optimize the initial layout plan to obtain the optimal layout plan; in each round of iteration, the following steps are executed: calculate the fitness of the first layout plan, and perform genetic selection from the first layout plan according to the fitness to obtain the second layout plan; in the first iteration process, the first layout plan is the initial layout plan; perform crossover and mutation on the second layout plan to obtain the third layout plan; determine whether the iteration stop condition is satisfied, if satisfied, output the optimal layout plan; otherwise, use the third layout plan as the first layout plan and perform the next round of iteration until the iteration stop condition is satisfied. In the embodiments of the present application, the genetic algorithm can be used to automatically generate the initial population, eliminating the subjectivity of manually drawing the location-related diagram. Through operations such as crossover and mutation in the algorithm, a large number of samples are generated, and through multiple iterations, the subjectivity of the optimization actions and the limitation of the number of optimization samples in the manual calculation process can be eliminated, thereby finding the optimal solution and determining the optimal layout plan, which can avoid manual calculation, save time and manpower, and improve efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0032] Figure 1 FIG. shows a schematic diagram of a location-related diagram in the prior art;

[0033] Figure 2 FIG. shows a schematic structural diagram of a method for optimizing regional layout provided by an embodiment of the present application;

[0034] Figure 3 FIG. shows a schematic diagram of a regional layout provided by an embodiment of the present application;

[0035] Figure 4 FIG. shows a schematic diagram of the logistics intensity table of an initial layout plan provided by an embodiment of the present application;

[0036] Figure 5 FIG. shows a schematic structural diagram of a method for optimizing regional layout provided by an embodiment of the present application;

[0037] Figure 6 FIG. shows a schematic diagram of an optimal layout plan provided by an embodiment of the present application;

[0038] Figure 7 A schematic diagram of a transportation intensity table showing an optimal layout solution provided by an embodiment of the present application;

[0039] Figure 8 A schematic structural diagram showing an area layout optimization provided by an embodiment of the present application;

[0040] Figure 9 A schematic structural diagram of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0041] To make the above objects, features, and advantages of the present application more obvious and understandable, the following will describe the detailed implementation manners of the present application with reference to the accompanying drawings.

[0042] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present application. Therefore, the present application is not limited by the specific embodiments disclosed below.

[0043] Secondly, the present application will be described in detail with reference to the schematic diagrams. When describing the embodiments of the present application in detail, for ease of explanation, the cross-sectional views showing the device structures will be enlarged locally out of the general scale, and the schematic diagrams are only examples and should not limit the scope of protection of the present application herein. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0044] For ease of understanding, the following will describe in detail an area layout optimization method, device, equipment, and medium provided by an embodiment of the present application with reference to the accompanying drawings.

[0045] Refer to Figure 2 As shown, it is a schematic structural diagram of an area layout optimization method provided by an embodiment of the present application, and the method includes the following steps.

[0046] S101, Initialize the layout constraint information, product portfolio information, and product process flow information, and randomly generate an initial layout solution.

[0047] In the embodiments of the present application, when arranging various devices, there is layout limit information, which are some restrictions during device layout. Specifically, the layout limit information may include at least one of factory building information, size limitations of the device storage area, and process limitations. The factory building information may be the size of the factory building, the structure of the factory building, such as whether the factory building is a multi-story design, etc. The size limitation of the device storage area may be the length limitation or width limitation of the device storage area. For example, the length of the device storage area needs to be less than the length of the factory building, and the width of the device storage area needs to be less than the width of the factory building. The process limitation may be that the devices for completing the front-end process and the devices for completing the back-end process need to be placed separately.

[0048] Specifically, the product portfolio information may be a combination of multiple product types that can be prepared according to the production capacity conditions of the factory building, as well as the quantity of each product. For example, the product portfolio information may be to prepare 10,000 pieces of product A, 15,000 pieces of product B, and 25,000 pieces of product C. The product process flow information may be, for each product, all the process steps required from the start of preparation to the completion of preparation.

[0049] Specifically, the layout limit information, product portfolio information, and product process flow information can be initialized, and multiple initial layout plans can be randomly generated. The initial layout plans are randomly generated without using the position-related diagram as a guide, and there is no need for manual drawing of the position-related diagram, which takes a lot of time. The initial layout plans are multiple feasible solutions that adapt to the layout limit information. Among them, each initial layout plan includes the position information of each device storage area. The position information of each device storage area may include the coordinate values of each vertex angle of the area, the coordinate value of the center point, etc.

[0050] Specifically, this method can be executed by using a regional layout optimization system, which may include a coordinate system establishment module, a limit information module, a process flow and product portfolio module, a genetic algorithm optimization module, and a layout evaluation module. The regional layout optimization system searches for the optimal solution by simulating the natural evolution process, can obtain an objective optimal layout, and reduces the labor cost.

[0051] Among them, the coordinate system establishment module can be used to establish a coordinate system. Refer to Figure 3 As shown, it is a schematic diagram of a regional layout provided by the embodiments of the present application. The figure includes a total of 11 regions A, B, C, D, E, F, G, H, I, J, and K. For region A, four vertex coordinates can be sequentially generated in the counterclockwise direction, denoted as (A x1 , A y1 ), (A x2 , A y1 ), (A x2 , A y2 ), and (A x1 , A y2) Denote the midpoint of the diagonal connection as the center point coordinates, which can be denoted as (Ax, Ay). Based on the center point coordinates, the distance between regions can be calculated. The transportation distance between region A and region H is denoted as Z AH =|H x -A x |+|H y -A y |, thus providing a data basis for optimizing the algorithm.

[0052] Specifically, the layout limit conditions can be expressed as:

[0053]

[0054] Among them, S n represents the area of the nth equipment storage area, S total is the total area of the workshop, L n represents the length of the nth equipment storage area, L total is the total length of the workshop, W n represents the width of the nth equipment storage area, W total is the total width of the workshop, A x1 represents the abscissa of the vertex of equipment storage area A, K x1 represents the abscissa of the vertex of equipment storage area K, and the two cannot coincide.

[0055] Specifically, the restriction information module can be used to input layout restriction information, and the process flow and product combination module can be used to input product combination information and product process flow information. After the pre-input is completed, the genetic algorithm optimization module can be used to initialize the basic data and generate an initial population, that is, initialize the layout plan.

[0056] S102, Use the genetic algorithm to optimize the initialized layout plan to obtain the optimal layout plan.

[0057] In the embodiment of the present application, through the genetic algorithm optimization module, the genetic algorithm can be used to optimize the initialized layout plan to obtain the optimal solution, that is, the optimal layout plan. The genetic algorithm can perform a large number of versions of optimization to find the optimal solution, and the mutation process can prevent premature entry into the local optimum. During the optimization process, multiple rounds of iteration are required. In each round of iteration, steps S1021 - S1023 need to be executed.

[0058] S1021, Calculate the fitness of the first layout plan, and perform genetic selection from the first layout plan according to the fitness to obtain the second layout plan.

[0059] Specifically, in the first iteration process, the first layout plan is the initialized layout plan. The fitness of the first layout plan can be calculated using the objective function. The fitness can measure the adaptability of the first layout plan. The greater the fitness, the stronger the adaptability of the corresponding first layout plan, and the more suitable the first layout plan is for the layout of the factory building. Probability selection can be performed according to the fitness of each individual (i.e., each first layout plan). Samples are selected through roulette wheel selection for inheritance, and better individuals are selected as the second layout plan as much as possible to ensure that good genes (i.e., the location information of the equipment storage area) are inherited.

[0060] In the embodiments of the present application, the fitness can be the total logistics intensity. The total logistics intensity can be the sum of n logistics intensities, where n is the number of pairwise combinations of equipment storage areas, and n is a positive integer. Specifically, for example, there are M equipment storage areas. In the process, products need to be transported from one equipment storage area to another to complete the next process content. Therefore, each pair of equipment storage areas corresponds to a logistics intensity, and the number of pairwise combinations of equipment storage areas is the number of logistics intensities.

[0061] Specifically, the logistics intensity can be determined according to the transportation times and transportation distance between two equipment storage areas, and can be the product of the transportation times and transportation distance. Further, it can also be calculated in combination with the transportation time. Refer to Figure 4 As shown, it is a schematic diagram of the logistics intensity table of an initialized layout plan provided by the embodiments of the present application. The logistics intensity between the CVD equipment storage area and the DIFF equipment storage area is 12, and the logistics intensity between the CVD equipment storage area and the PVD equipment storage area is 22.

[0062] S1022. Perform crossover and mutation on the second layout plan to obtain the third layout plan.

[0063] Specifically, the second layout plan can be crossed and mutated to obtain a third layout plan with better adaptability. The number of the third layout plans can be multiple, so as to improve the fitness of the layout plan and optimize the regional layout.

[0064] In a possible implementation manner, performing crossover and mutation on the second layout plan to obtain the third layout plan can be specifically that the genes of two individuals (the second layout plan) are subjected to gene crossover processing according to a crossover method to generate new individuals, that is, the fourth layout plan is obtained. Then, some genes in the fourth layout plan are randomly mutated to obtain the third layout plan to ensure the diversity of the population, prevent falling into a local optimal solution, and facilitate finding the true optimal solution.

[0065] S1023, determine whether the iteration stop condition is met. If it is met, output the optimal layout plan; otherwise, use the third layout plan as the first layout plan and perform the next round of iteration until the iteration stop condition is met.

[0066] In the embodiments of the present application, it can be determined whether the current condition meets the iteration stop condition. The iteration stop condition can be that the number of iterations reaches a preset number, or the fitness is greater than a preset fitness, which can be set according to the actual situation. If the iteration stop condition is met, the optimal layout plan can be output. If the iteration stop condition is not met, for example, the number of iterations has not reached the preset number, or the fitness value is less than the preset fitness value, then the next round of iteration can be entered, using the third layout plan as the first layout plan and continuing the iteration until the number of iterations or the fitness value meets the requirements, and then the optimal layout plan is output. Refer to Figure 5 shown in the figure, which is a schematic structural diagram of another regional layout optimization method provided by the embodiments of the present application.

[0067] It can be seen from this that the genetic algorithm can be used to automatically generate the initial population, eliminating the subjectivity of manually drawing the position-related diagram. Through operations such as crossover and mutation in the algorithm, a large number of samples are generated, and through multiple iterations, the subjectivity of the optimization actions and the limitation of the number of optimization samples in the manual calculation process can be eliminated, and then the optimal solution can be searched for to determine the optimal layout plan, which can avoid manual calculation, save time and manpower, and improve efficiency.

[0068] Specifically, when the fitness value is the total logistics intensity, the logistics intensity is the product of the number of transportation times x and the transportation distance Z. The iteration stop condition can be that the total logistics intensity reaches the minimum value. The total logistics intensity can be calculated using the objective function, and the objective function can be expressed as:

[0069]

[0070] In the embodiments of the present application, the initialized layout plan and the optimal layout plan can also be compared, and the comparison result can be output. The comparison result can include the legend of the initialized layout plan and the legend of the optimal layout plan, and can also include the transportation intensity table of the initial layout plan and the transportation intensity table of the optimal layout plan, so as to show the differences between the two to the user and reflect the optimization process. Refer to Figure 6 shown in the figure, which is a schematic diagram of an optimal layout plan provided by the embodiments of the present application. Refer to Figure 7 shown in the figure, which is a schematic diagram of the transportation intensity table of an optimal layout plan provided by the embodiments of the present application, and the transportation intensity has decreased.

[0071] An embodiment of the present application provides a method for optimizing regional layout, which initializes layout constraint information, product portfolio information, and product process flow information, and randomly generates an initial layout plan; the initial layout plan includes the location information of each equipment storage area; uses a genetic algorithm to optimize the initial layout plan to obtain an optimal layout plan; in each round of iteration, the following steps are executed: calculate the fitness of the first layout plan, and perform genetic selection from the first layout plan according to the fitness to obtain a second layout plan; in the first iteration process, the first layout plan is the initial layout plan; perform crossover and mutation on the second layout plan to obtain a third layout plan; determine whether the iteration stop condition is satisfied, if satisfied, output the optimal layout plan; otherwise, use the third layout plan as the first layout plan and perform the next round of iteration until the iteration stop condition is satisfied. In the embodiment of the present application, an initial population can be automatically generated using a genetic algorithm, eliminating the subjectivity of manually drawing location-related diagrams. By operations such as crossover and mutation in the algorithm, a large number of samples are generated. Through multiple iterations, the subjectivity of the optimization actions and the limitation of the number of optimization samples in the manual calculation process can be eliminated, and then the optimal solution can be searched for to determine the optimal layout plan, which can avoid manual calculation, save time and manpower, and improve efficiency.

[0072] Based on the above method for optimizing regional layout, an embodiment of the present application further provides a device for optimizing regional layout, as referred to Figure 8 shown in the structural block diagram of a device for optimizing regional layout provided by an embodiment of the present application. The device may include:

[0073] An initialization unit 201, configured to initialize layout constraint information, product portfolio information, and product process flow information, and randomly generate an initial layout plan; the initial layout plan includes the location information of each equipment storage area;

[0074] An optimization unit 202, configured to use a genetic algorithm to optimize the initial layout plan to obtain an optimal layout plan; in each round of iteration, the following steps are executed:

[0075] Calculate the fitness of the first layout plan, and perform genetic selection from the first layout plan according to the fitness to obtain a second layout plan; in the first iteration process, the first layout plan is the initial layout plan;

[0076] Perform crossover and mutation on the second layout plan to obtain a third layout plan;

[0077] Determine whether the iteration stop condition is satisfied, if satisfied, output the optimal layout plan; otherwise, use the third layout plan as the first layout plan and perform the next round of iteration until the iteration stop condition is satisfied.

[0078] An embodiment of the present application provides a device for optimizing regional layout. An initialization unit is used to initialize layout constraint information, product portfolio information, and product process flow information, and randomly generate an initial layout plan. The initial layout plan includes the location information of each equipment storage area. An optimization unit is used to optimize the initial layout plan by using a genetic algorithm to obtain an optimal layout plan. In each round of iteration, the following steps are executed: calculate the fitness of the first layout plan, and perform genetic selection from the first layout plan according to the fitness to obtain a second layout plan. In the first iteration process, the first layout plan is the initial layout plan. Perform crossover and mutation on the second layout plan to obtain a third layout plan. Determine whether the iteration stop condition is met. If it is met, output the optimal layout plan. Otherwise, use the third layout plan as the first layout plan and perform the next round of iteration until the iteration stop condition is met. In the embodiment of the present application, an initial population can be automatically generated by using a genetic algorithm, eliminating the subjectivity of manually drawing location-related diagrams. A large number of samples are generated through operations such as crossover and mutation in the algorithm. After multiple iterations, the subjectivity of the optimization actions and the limitation of the number of optimization samples in the manual calculation process can be eliminated, and then the optimal solution can be searched for to determine the optimal layout plan, which can avoid manual calculation, save time and manpower, and improve efficiency.

[0079] In another aspect, an embodiment of the present application provides a computer device. Refer to Figure 9 As shown, it is a structural diagram of a computer device provided by an embodiment of the present application. The computer device includes a processor 310 and a memory 320:

[0080] The memory 320 is used to store program code and transmit the program code to the processor 310;

[0081] The processor 310 is used to execute the method provided by the above embodiment according to the instructions in the program code.

[0082] This computer device may include a terminal device or a server, and the foregoing device may be configured in this computer device.

[0083] In another aspect, an embodiment of the present application further provides a storage medium. The storage medium is used to store a computer program, and the computer program is used to execute the method provided by the above embodiment.

[0084] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware through program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium can be at least one of the following media: read-only memory (English: Read-only Memory, abbreviation: ROM), RAM, magnetic disk, or optical disc, etc., various media that can store program codes.

[0085] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, reference can be made to the partial description of the method embodiments.

[0086] The above are only the preferred embodiments of the present application. Although the present application has been disclosed above with preferred embodiments, it is not intended to limit the present application. Any person skilled in the art can make many possible changes and modifications to the technical solution of the present application, or modify it into an equivalent embodiment with equivalent changes, without departing from the scope of the technical solution of the present application. Therefore, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application still fall within the scope of the protection of the technical solution of the present application.

Claims

1. A method for optimizing regional layout, characterized in that: include: Initializing layout restriction information, product combination information, and product process flow information, and randomly generating an initialization layout plan; the initialization layout plan includes location information of each equipment storage area; The initial layout scheme is optimized by using a genetic algorithm to obtain an optimal layout scheme; in each round of iteration, the following steps are performed: Calculating the fitness of the first layout scheme, and performing genetic selection from the first layout scheme according to the fitness to obtain a second layout scheme; in the first iteration process, the first layout scheme is the initialization layout scheme; Performing crossover mutation on the second layout scheme to obtain a third layout scheme; It is determined whether an iteration stop condition is met. If so, the optimal layout solution is output; otherwise, the third layout solution is used as the first layout solution to perform the next round of iteration until the iteration stop condition is met.

2. The method according to claim 1, characterized in that The fitness is the total logistics intensity, the total logistics intensity is the sum of n logistics intensities, n is the number of combinations of the equipment storage areas, and n is a positive integer.

3. The method according to claim 2, characterized in that The logistics intensity is determined according to the transportation times of the two equipment storage areas and the transportation distance of the two equipment storage areas.

4. The method according to claim 1, characterized in that: The cross-mutating the second layout scheme to obtain a third layout scheme includes: Perform gene crossover processing on the two second layout schemes to obtain a fourth layout scheme; The fourth layout scheme is randomly mutated to obtain the third layout scheme.

5. The method according to claim 1, characterized in that: The layout restriction information includes at least one of factory building information, size restriction of the equipment storage area, and process restriction.

6. The method according to any one of claims 1 to 5, characterized in that: The iteration stop condition includes that the number of iterations reaches a preset number or the fitness is greater than a preset fitness.

7. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: The initialization layout scheme and the optimal layout scheme are compared, and a comparison result is output.

8. A regional layout optimization device, characterized in that: include: An initialization unit, used to initialize layout restriction information, product combination information and product process flow information, and randomly generate an initialization layout plan; the initialization layout plan includes location information of each equipment storage area; The optimization unit is used to optimize the initialization layout scheme by using a genetic algorithm to obtain an optimal layout scheme; in each round of iteration, the following steps are performed: Calculating the fitness of the first layout scheme, and performing genetic selection from the first layout scheme according to the fitness to obtain a second layout scheme; in the first iteration process, the first layout scheme is the initialization layout scheme; Performing crossover mutation on the second layout scheme to obtain a third layout scheme; It is determined whether an iteration stop condition is met. If so, the optimal layout solution is output; otherwise, the third layout solution is used as the first layout solution to perform the next round of iteration until the iteration stop condition is met.

9. A computer device, characterized in that: The computer device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute the method according to any one of claims 1 to 7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and the computer program is used to execute the method according to any one of claims 1 to 7.

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