IPPS problem dimensionality reduction solving method and device, electronic equipment and storage medium

Abstract

The invention discloses an IPPS problem dimensionality reduction solving method and device, electronic equipment and a computer readable storage medium. The method comprises the steps that workpiece information of a to-be-produced product is acquired; according to the workpiece information, a production process problem of the to-be-produced product is subjected to dimensionality reduction to form a plurality of production process sub-problems arranged according to a production sequence, and a corresponding constraint condition is set for each production process sub-problem; according to each production process sub-problem and the corresponding constraint condition, the target process of each production process sub-problem is solved in a loop iteration mode, and finally the target production process of the to-be-produced product is obtained. According to the method, the production process problem is subjected to dimensionality reduction to form the multiple production process sub-problems, each production process sub-problem is solved independently, and the optimal solution is solved through loop iteration, so that the efficiency of solving the target production process of the to-be-produced product is improved, and meanwhile, the solving precision is guaranteed.

Description

Technical Field

[0001] The present application relates to the technical field of integrated process planning and workshop scheduling, and in particular to a method, device, electronic device and computer-readable storage medium for solving IPPS problem dimensionality reduction. Background Art

[0002] Integrated Process Planning and Scheduling (IPPS) is a key research area in modern manufacturing systems, aiming to solve the collaborative optimization problem between process planning and scheduling. Traditionally, process planning and scheduling are viewed as two independent stages, with process planning determining the processing steps and methods for manufacturing products, while scheduling is responsible for arranging the execution order of these processing steps on different processing equipment. However, this separate approach often leads to local optimality, where the choice of process planning may limit the flexibility of scheduling, and vice versa. Therefore, IPPS was proposed to better utilize the complementarity of the two, by integrating process planning and scheduling within a unified framework for optimization, thereby improving the overall operating efficiency and product quality of the manufacturing system.

[0003] The complexity of the IPPS problem stems primarily from its requirement to simultaneously optimize both process planning and shop scheduling. This requires selecting from a vast array of possible process and scheduling options to find an optimal or near-optimal combination. Due to the numerous and interrelated decision variables involved, the IPPS problem has been proven to be NP-complete, meaning no known algorithm can find an exact solution in polynomial time. Consequently, researchers and engineers have been exploring various effective solutions and strategies.

[0004] Existing Integrated Process Planning and Scheduling (IPPS) solutions incur high computational complexity in order to achieve optimal solutions. This can be prohibitively long, especially for large-scale production systems, limiting their efficiency and applicability in practical applications. However, existing algorithms struggle to guarantee solution quality and stability, leading to significant fluctuations in the solution. This makes it difficult to achieve stable, high-quality solutions, especially in scenarios requiring high precision. Summary of the Invention

[0005] The embodiments of the present application provide an IPPS problem-solving method, device, electronic device, and storage medium to solve the low solution efficiency of existing IPPS problem-solving methods and improve the solution efficiency.

[0006] This application provides a method for solving the IPPS problem by reducing its dimensionality, including:

[0007] Obtaining workpiece information of products to be produced;

[0008] Generate corresponding production process questions based on the workpiece information;

[0009] Performing dimensionality reduction on the production process problem to obtain a plurality of production process sub-problems arranged in production order, and setting corresponding constraints for each production process sub-problem;

[0010] The target process of each production process sub-problem is solved iteratively according to each production process sub-problem and the corresponding constraint conditions, and finally the target production process of the product to be produced is obtained.

[0011] Optionally, the process of reducing the dimension of the production process problem to obtain multiple production process sub-problems arranged in production order includes:

[0012] The production process problem is reduced in dimension into a process selection sub-problem, a workpiece process sequencing sub-problem for the same workpiece, a device process sequencing sub-problem for the same processing equipment, and a processing equipment selection sub-problem.

[0013] Optionally, the process of reducing the dimension of the production process problem to obtain the constraint conditions corresponding to each production process sub-problem includes:

[0014] The production process problem is reduced in dimension, and constraints corresponding to the independent variables in each production process sub-problem are set.

[0015] Optionally, the process of iteratively solving the target process of each production process subproblem according to each production process subproblem and the corresponding constraint conditions to finally obtain the target production process of the product to be produced includes:

[0016] Solve the process selection subproblem and select a target production process from multiple production processes for producing the product to be produced;

[0017] Solve the subproblem of workpiece process sequencing of the same workpiece of the product to be produced based on the target production process, and determine the target workpiece process sequencing of the same workpiece;

[0018] Solve the equipment process sequencing subproblem of the same processing equipment of the product to be produced based on the target workpiece process sequencing, and determine the target equipment process sequencing of the same processing equipment;

[0019] Solve the subproblem of selecting processing equipment for the product to be produced based on the target equipment process ranking, and determine the target processing equipment for the same process;

[0020] And the target process of each production process sub-problem is solved cyclically and iteratively, and finally the target production process of the product to be produced is obtained.

[0021] This application also provides an IPPS problem dimensionality reduction solution device, comprising:

[0022] A workpiece information acquisition module is used to obtain the workpiece information of the product to be produced;

[0023] A question generation module, configured to generate corresponding production process questions based on the workpiece information;

[0024] A problem dimensionality reduction module is used to reduce the dimensionality of the production process problem to obtain a plurality of production process sub-problems arranged in the production order, and constraints corresponding to each production process sub-problem;

[0025] The problem-solving module is used to iteratively solve the target process of each production process sub-problem according to each production process sub-problem and the corresponding constraint conditions, and finally obtain the target production process of the product to be produced.

[0026] Optionally, the problem dimensionality reduction module is specifically used to reduce the dimensionality of the production process problem, reducing the production process problem into process selection sub-problems, workpiece process sorting sub-problems of the same workpiece, equipment process sorting sub-problems of the same processing equipment, and processing equipment selection sub-problems.

[0027] Optionally, the problem dimensionality reduction module is specifically used to reduce the dimensionality of the production process problem and set constraints corresponding to the independent variables in each production process sub-problem.

[0028] Optionally, the problem-solving module is specifically used to solve the process selection sub-problem, select a target production process from multiple production processes for producing the product to be produced; solve the workpiece process sorting sub-problem of the same workpiece of the product to be produced based on the target production process, and determine the target workpiece process sorting of the same workpiece; solve the equipment process sorting sub-problem of the same processing equipment of the product to be produced based on the target workpiece process sorting, and determine the target equipment process sorting of the same processing equipment; solve the processing equipment selection sub-problem of the product to be produced based on the target equipment process sorting, and determine the target processing equipment of the same process; and iteratively solve the target process of each production process sub-problem, and finally obtain the target production process of the product to be produced.

[0029] The present application also provides an electronic device, which includes a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes the aforementioned method.

[0030] The present application also provides a computer-readable storage medium, comprising a program code. When the storage medium is run on an electronic device, the program code is used to enable the electronic device to execute the aforementioned method.

[0031] The present application provides an IPPS problem-solving method, apparatus, electronic device, and computer-readable storage medium. The IPPS problem-solving method reduces the dimension of a production process problem into multiple production process subproblems, solves each production process subproblem separately, and iterates to find the optimal solution. This improves the efficiency of solving the target production process for the product to be produced while ensuring the accuracy of the solution.

[0032] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. The purposes and other advantages of the present application can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0034] Figure 1 This is a flowchart of an implementation method for solving the IPPS problem dimensionality reduction in an embodiment of the present application;

[0035] Figure 2 This is a schematic diagram of a dimensionality reduction solution process for an IPPS problem in an embodiment of the present application;

[0036] Figure 3 This is a schematic diagram of a cyclic iterative solution process in an embodiment of the present application;

[0037] Figure 4 This is a schematic diagram of the structure of a device for solving the IPPS problem dimensionality reduction in an embodiment of the present application;

[0038] Figure 5 This is a schematic diagram of the hardware structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other in any way. In addition, although a logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in an order different from that here.

[0040] The terms "first," "second," and the like in the specification and claims of this application and in the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or sequential sequence. It should be understood that the terms used in this manner are interchangeable where appropriate, such that the embodiments of the invention described herein can be practiced in sequences other than those illustrated or described herein.

[0041] refer to Figure 1 FIG. 1 is a flowchart of an implementation method for solving an IPPS problem dimensionality reduction problem provided by an embodiment of the present application. The specific implementation process of the method is as follows:

[0042] S11: Obtain workpiece information of the product to be produced.

[0043] The workpiece information of the product to be produced includes the processing requirements of each workpiece, the required processes and other information required for production and processing. Based on the workpiece information, the available processes for producing each workpiece of the product to be produced, as well as the corresponding processing equipment, the processing time of each process and other basic production information can be determined, so that the relevant production processes of the product to be produced can be determined based on the workpiece information, so as to subsequently determine the optimal process from multiple production processes, that is, the process with the highest production efficiency.

[0044] S12: Generate corresponding production process questions based on the workpiece information.

[0045] Based on the workpiece information, the relevant production processes of the product to be produced can be determined, and then the problem of how to determine the most efficient production process for the product to be produced can be solved based on the relevant production processes required to produce the product to be produced.

[0046] S13: Reduce the dimension of the production process problem to obtain multiple production process sub-problems arranged in production order, and constraints corresponding to each production process sub-problem.

[0047] Combined with the actual application scenario, the original problem, that is, the production process problem of the product to be produced, is reduced in dimension according to the order that conforms to the actual production capacity.

[0048] Reduce the complex original problem into multiple production process sub-problems, that is, divide the original problem variable set into several subsets, each subset, that is, each production process sub-problem can correspond to a single independent variable. For example, the original problem can be divided based on the three types of manufacturing flexibility in the production process problem: process flexibility, process flexibility, and processing equipment flexibility, and the following is obtained: Figure 2 The four sub-problems shown in the figure are the process selection sub-problem, the workpiece process sorting sub-problem of the same workpiece, the equipment process sorting sub-problem of the same processing equipment, and the processing equipment selection sub-problem. irl ,X ij ,S ij ,C max},Y={Y ijj′ ,S ij ,C max},Z={Z iji′j′k ,S ij ,C max},M={Z ijk ,S ij ,C max Only a subset of variables is involved in each subproblem, which effectively reduces the dimension and complexity of each subproblem. Figure 2 The horizontal dimension reduction method in .

[0049] At the same time, set a separate corresponding constraint condition for each production process sub-problem. The constraint in each sub-problem is a restriction on the relevant variables of the current sub-problem, such as the independent variable, thereby reducing the complexity of the constraint condition. Figure 2 The vertical dimension reduction method in .

[0050] S14: Solve the target process of each production process sub-problem iteratively according to each production process sub-problem and the corresponding constraint conditions, and finally obtain the target production process of the product to be produced.

[0051] Based on the above-mentioned production process sub-problems, feasible solutions can be initialized, and respective variables can be initialized, so as to perform the first solution and obtain the basic production process framework. On the basis of the basic production process framework, the optimal solution of the first sub-problem is obtained, and then each production process sub-problem is solved. The solution of each production process sub-problem is optimized based on the solution of the previous production process sub-problem, and the problems of the subsequent stages are gradually considered. Different sets of variables are solved and optimized in turn. The solution results of each stage provide reference and constraints for the next stage, thereby ensuring the feasibility of the production process and improving the accuracy of the solution.

[0052] For example, the iterative solution process can be based on a metaheuristic algorithm, initially locating a feasible solution. Using an embedded solver, the optimal solution to the first subproblem is then obtained. Subsequent stages are then considered, each optimizing a different set of variables. The results of each stage provide reference and constraints for the next. Through this hierarchical solution process, the global optimal solution to the original problem is gradually approached, ensuring that the quality and stability of the solution are maintained at each stage.

[0053] Specifically, the specific process of step S14 may include S141 to S144, such as Figure 2 and 3 shown.

[0054] S141: Solve the process selection sub-problem and select a target production process from multiple production processes for the product to be produced.

[0055] To process a workpiece or product, multiple processes are required. Therefore, it is necessary to select a process from multiple production processes as the target production process. The selection criteria can be to select the process with the shortest time and highest efficiency while meeting quality requirements. This problem can be regarded as the branch selection problem of OR node in the heuristic algorithm, which is expressed in mathematical form as follows.

[0056] The objective function for solving the process selection subproblem is: obj = min C max , where C max Indicates the maximum completion time.

[0057] The constraint equations for solving the process selection subproblem include:

[0058] There are multiple routes for producing a product through different production processes, which form a process network diagram. A node that can correspond to multiple processes after a process is called an OR node in the process network diagram. An OR node can only select one process corresponding to it at a time, which is equivalent to a branch connecting the OR node:

[0059]

[0060] Where R irl When it is equal to 1, the lth branch of the rth OR node in the network graph of workpiece i is selected. When it is equal to 0, it means that the lth branch of the rth OR node in the network graph of workpiece i is not selected.

[0061] If process O ij The branch to which it belongs is not selected, so process O ij Will not be selected:

[0062]

[0063] Where, X ij When it is equal to 1, process O ij When selected, equal to 0, process O ij Not selected. The mathematical expression of the OR node's process selection control logic is:

[0064]

[0065] Between the processes of the same workpiece, according to the parameter Y ijj′ The values ​​are processed in the following order:

[0066]

[0067] Where S ij Indicates process O ij The start time of processing.

[0068] The processes processed on the same processing equipment should follow the priority relationship among the processes:

[0069]

[0070] Where, Represents the set of processes assigned to processing equipment k.

[0071] The maximum completion time constraint can be expressed as:

[0072]

[0073] S142: Solve the sub-problem of workpiece process sequencing of the same workpiece of the product to be produced based on the target production process, and determine the target workpiece process sequencing of the same workpiece.

[0074] In order to improve the production efficiency of workpieces, the processes of the same workpiece are solved to obtain the target workpiece process sorting, which is applicable to the processes of the same workpiece. The mathematical expression for solving the sub-problem of workpiece process sorting of the same workpiece is as follows.

[0075] The objective function for solving the subproblem of sequencing the workpiece process of the same workpiece is: obj = min C max .

[0076] The constraint equations include:

[0077] A 0-1 variable Y representing the priority relationship between processes of the same workpiece ijj′ , follow the priority relationship constraints in the process network diagram of the corresponding workpiece:

[0078]

[0079] Where U ijj′According to the process priority relationship in the process network diagram, if process O ij Should be in process O ij′ If it has been processed before, it is equal to 1, otherwise it is equal to 0; ijj′ Indicates process O ij In process O ij′ It is equal to 1 when the previous process is processed, and equal to 0 otherwise.

[0080] The processes of the same workpiece should be processed in a certain order, and the start and end processing times should be determined accordingly:

[0081]

[0082] Where S ij Indicates process O ij The start processing time, P ij Indicates the process O determined based on the current individual ij Processing time.

[0083] Between the processes on the same processing equipment, the parameters Z should be determined. iji′j′k Execute processing and determine the start and end processing times:

[0084]

[0085] Where Z iji′j′k If process O ij Before process O, the processing equipment k is installed ij′ If processing is in progress, it equals to 1; otherwise, it equals to 0.

[0086] The maximum completion time constraint can be expressed as:

[0087]

[0088] Where, It represents the process set of workpiece i after the process is selected according to the information of the current individual OR node.

[0089] S143: Solve the equipment process sequencing sub-problem of the same processing equipment to produce the product based on the target workpiece process sequencing, and determine the target equipment process sequencing of the same processing equipment.

[0090] The objective function of solving the equipment process sequencing subproblem for the same processing equipment to produce the product is expressed as: obj = min C max .

[0091] The constraint equations include:

[0092] Between the processes of the same workpiece, according to the parameter Y ijj′The values ​​are processed in the following order:

[0093]

[0094] Where, represents the set of processes assigned to processing equipment k; Y ijj′ If process O ij In process O ij′ If the previous process is processed immediately, it is equal to 1; otherwise, it is equal to 0.

[0095] The processes on the same processing equipment should be processed in a certain order, and the start and end processing times should be determined accordingly:

[0096]

[0097] Where, P ij Indicates the process O determined based on the current individual ij Processing time.

[0098]

[0099] The maximum completion time constraint can be expressed as:

[0100]

[0101] S144: Solve the sub-problem of selecting processing equipment for the product to be produced based on the target equipment process ranking, determine the target processing equipment for the same process; and iteratively solve the target process of each production process sub-problem, and finally obtain the target production process of the product to be produced.

[0102] The objective function for solving the subproblem of selecting processing equipment for production products can be expressed as: obj = minC max .

[0103] The constraint equations include:

[0104] Between the processes of the same workpiece, according to the parameter Y ijj′ The values ​​are processed in the following order:

[0105]

[0106] The processes on the same processing equipment should be processed in a certain order, and the start and end processing times should be determined accordingly:

[0107]

[0108] Where Z iji′j′k If process O ijBefore process O, the processing equipment k is installed ij′ If processing is done, it is equal to 1, otherwise it is equal to 0.

[0109]

[0110] The maximum completion time constraint can be expressed as:

[0111] It should be noted that each iterative solution process may result in different production processes due to the previous solution until the optimal solution is found.

[0112] Thus, the embodiments of the present application provide a method, apparatus, electronic device, and computer-readable storage medium for solving an IPPS problem by dimensionality reduction. The method for solving a production process problem includes reducing the dimensionality of the production process problem into multiple production process subproblems, solving each production process subproblem separately, and iterating to find the optimal solution. This improves the efficiency of solving the target production process for the product to be produced while ensuring the accuracy of the solution.

[0113] In a specific implementation scenario, the embodiments of the present application can construct the two problems of process planning and shop scheduling into an overall mixed integer programming model (MILP) by rationally combining mathematical programming models and metaheuristic algorithms. Using dimensionality reduction decomposition, the original problem is decomposed into smaller sub-problems. Metaheuristic algorithms are combined between different sub-problems to achieve solution transfer and continuous iteration, gradually improving the quality of the solution.

[0114] The process of reducing the dimensionality of the production process problem to obtain multiple production process sub-problems arranged in the production order is called horizontal dimensionality reduction. The horizontal dimensionality reduction method aims to decompose the original large-scale integer programming problem into multiple small-scale sub-problems. The specific implementation steps are as follows:

[0115] Variable set partitioning: Based on the actual scenario, the original problem is divided into several subsets according to a certain order. In each sub-problem, we only involve variables from one subset, effectively reducing the dimensionality and complexity of each sub-problem.

[0116] Among them, the process of reducing the dimension of the production process problem and obtaining the constraints corresponding to each production process sub-problem can be called the vertical dimensionality reduction method, which aims to divide the constraints. By analyzing the constraints in the original problem, the original problem is divided into the same problems. The constraints in each sub-problem are restrictions on the variables related to the current sub-problem.

[0117] The iterative solution process employs a metaheuristic algorithm to initialize a feasible solution. Using an embedded solver, the optimal solution to the first subproblem is obtained based on this initial solution. Subsequent stages are then considered, and different sets of variables are optimized in sequence. The results of each stage provide reference and constraints for the next stage. Through this hierarchical solution process, the global optimal solution to the original problem is gradually approached, ensuring that the quality and stability of the solution are maintained at each stage.

[0118] Based on the same inventive concept, the embodiment of the present application also provides a device for solving IPPS problem dimensionality reduction. Figure 4 As shown, it is a schematic diagram of the structure of the production process problem solving device, which may include:

[0119] Workpiece information acquisition module 101, used to obtain the workpiece information of the product to be produced;

[0120] A question generation module 102 is used to generate corresponding production process questions based on workpiece information;

[0121] The problem dimension reduction module 103 is used to reduce the dimension of the production process problem to obtain a plurality of production process sub-problems arranged in the production order and the constraint conditions corresponding to each production process sub-problem;

[0122] The problem solving module 104 is used to iteratively solve the target process of each production process sub-problem according to each production process sub-problem and the corresponding constraint conditions, and finally obtain the target production process of the product to be produced.

[0123] Thus, the embodiments of the present application provide a method, apparatus, electronic device, and computer-readable storage medium for solving a production process problem. The method includes reducing the dimension of a production process problem into multiple production process subproblems, solving each production process subproblem separately, and iterating to find the optimal solution. This improves the efficiency of solving the target production process for the product to be produced while ensuring the accuracy of the solution.

[0124] Among them, the problem dimension reduction module 102 can be specifically used to reduce the dimension of the production process problem, reducing the production process problem into process selection sub-problems, workpiece process sorting sub-problems of the same workpiece, equipment process sorting sub-problems of the same processing equipment, and processing equipment selection sub-problems.

[0125] The problem dimension reduction module 102 can be specifically used to reduce the dimension of the production process problem and set constraints corresponding to the independent variables in each production process sub-problem.

[0126] Among them, the problem-solving module 104 is specifically used to solve the process selection sub-problem, select a target production process from multiple production processes for the product to be produced; solve the workpiece process sorting sub-problem of the same workpiece of the product to be produced based on the target production process, and determine the target workpiece process sorting of the same workpiece; solve the equipment process sorting sub-problem of the same processing equipment to be produced based on the target workpiece process sorting, and determine the target equipment process sorting of the same processing equipment; solve the processing equipment selection sub-problem of the product to be produced based on the target equipment process sorting, and determine the target processing equipment for the same process; and iteratively solve the target process of each production process sub-problem, and finally obtain the target production process of the product to be produced.

[0127] In some possible implementations, the production process problem solving device according to the present application may include at least a processor and a memory. The memory stores program code, and when the program code is executed by the processor, the processor executes the steps of the production process problem solving method according to various exemplary embodiments of the present application described in this specification. For example, the processor may execute the following steps: Figure 1 Follow the steps shown in .

[0128] Based on the same inventive concept, an electronic device is also provided in the embodiment of the present application. The electronic device can realize the functions of the aforementioned production process problem solving method device, referring to Figure 5 , electronic equipment includes:

[0129] At least one processor 801, and a memory 802 connected to the at least one processor 801. The specific connection medium between the processor 801 and the memory 802 is not limited in the embodiment of the present application. Figure 5 In the example, the processor 801 and the memory 802 are connected via a bus 800. Figure 5 The bus 800 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, Figure 5 The diagram is represented by only one thick line, but this does not mean that there is only one bus or one type of bus. Alternatively, the processor 801 may also be referred to as a controller, without limitation to the name.

[0130] In the embodiment of the present application, the memory 802 stores instructions that can be executed by at least one processor 801. The at least one processor 801 can execute the production process problem solving method discussed above by executing the instructions stored in the memory 802. The processor 801 can implement Figure 4 The functions of each module in the device shown.

[0131] Among them, the processor 801 is the control center of the device, which can use various interfaces and lines to connect the various parts of the entire control device, and monitor the device as a whole by running or executing instructions stored in the memory 802 and calling data stored in the memory 802, the various functions of the device and processing data.

[0132] In one possible design, processor 801 may include one or more processing units. Processor 801 may integrate an application processor and a modem processor. The application processor primarily processes the operating system, user interface, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into processor 801. In some embodiments, processor 801 and memory 802 may be implemented on the same chip. In some embodiments, they may also be implemented on separate chips.

[0133] The processor 801 can be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the production process problem-solving method disclosed in the embodiments of the present application can be directly embodied as a hardware processor for execution, or can be executed by a combination of hardware and software modules in the processor.

[0134] The memory 802 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules. The memory 802 may include at least one type of storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (Random Access Memory, RAM), a static random access memory (Static Random Access Memory, SRAM), a programmable read-only memory (Programmable Read Only Memory, PROM), a read-only memory (Read Only Memory, ROM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), a magnetic memory, a disk, an optical disk, etc. The memory 802 is any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 802 in the embodiment of the present application can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.

[0135] By designing and programming the processor 801, the code corresponding to the production process problem solving method described in the above embodiment can be fixed into the chip, so that the chip can execute the code when running. Figure 1 The steps of the production process problem solving method of the embodiment shown are as follows: How to design and program the processor 801 is a technique well known to those skilled in the art and will not be described in detail here.

[0136] Based on the same inventive concept, an embodiment of the present application further provides a storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer executes the production process problem solving method discussed above.

[0137] In some possible implementations, various aspects of the production process problem-solving method provided in the present application can also be implemented in the form of a program product, which includes program code. When the program product is run on an apparatus, the program code is used to enable the control device to execute the steps of the production process problem-solving method according to various exemplary embodiments of the present application described above in this specification.

[0138] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0139] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 steps in the process. 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.

[0140] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0141] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0142] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for solving the IPPS problem by dimensionality reduction, characterized in that: include: Obtaining workpiece information of products to be produced; Generate corresponding production process questions based on the workpiece information; Performing dimensionality reduction on the production process problem to obtain a plurality of production process sub-problems arranged in production order, and setting corresponding constraints for each production process sub-problem; The target process of each production process sub-problem is solved iteratively according to each production process sub-problem and the corresponding constraint conditions, and finally the target production process of the product to be produced is obtained.

2. The method according to claim 1, wherein: The process of reducing the dimension of the production process problem to obtain multiple production process sub-problems arranged in production order includes: The production process problem is reduced in dimension into a process selection sub-problem, a workpiece process sequencing sub-problem for the same workpiece, a device process sequencing sub-problem for the same processing equipment, and a processing equipment selection sub-problem.

3. The method according to claim 1, wherein: The process of reducing the dimension of the production process problem to obtain the constraint conditions corresponding to each production process sub-problem setting includes: The production process problem is reduced in dimension, and constraints corresponding to the independent variables in each production process sub-problem are set.

4. The method according to claim 2, wherein: The process of iteratively solving the target process of each production process subproblem according to each production process subproblem and the corresponding constraint conditions, and finally obtaining the target production process of the product to be produced includes: Solve the process selection subproblem and select a target production process from multiple production processes for producing the product to be produced; Solve the subproblem of workpiece process sequencing of the same workpiece of the product to be produced based on the target production process, and determine the target workpiece process sequencing of the same workpiece; Solve the equipment process sequencing subproblem of the same processing equipment of the product to be produced based on the target workpiece process sequencing, and determine the target equipment process sequencing of the same processing equipment; Solve the subproblem of selecting processing equipment for the product to be produced based on the target equipment process ranking, and determine the target processing equipment for the same process; And the target process of each production process sub-problem is solved cyclically and iteratively, and finally the target production process of the product to be produced is obtained.

5. A device for solving IPPS problem dimensionality reduction, characterized in that: include: A workpiece information acquisition module is used to obtain the workpiece information of the product to be produced; A question generation module, configured to generate corresponding production process questions based on the workpiece information; A problem dimensionality reduction module is used to reduce the dimensionality of the production process problem to obtain a plurality of production process sub-problems arranged in the production order, and constraints corresponding to each production process sub-problem; The problem-solving module is used to iteratively solve the target process of each production process sub-problem according to each production process sub-problem and the corresponding constraint conditions, and finally obtain the target production process of the product to be produced.

6. The device according to claim 5, characterized in that The problem dimensionality reduction module is specifically used to reduce the dimensionality of the production process problem, reducing the dimensionality of the production process problem into process selection sub-problems, workpiece process sorting sub-problems of the same workpiece, equipment process sorting sub-problems of the same processing equipment, and processing equipment selection sub-problems.

7. The device according to claim 5, characterized in that The problem dimension reduction module is specifically used to reduce the dimension of the production process problem and set constraints corresponding to the independent variables in each production process sub-problem.

8. The device according to claim 6, characterized in that The problem-solving module is specifically used to solve the process selection sub-problem, select a target production process from multiple production processes for producing the product to be produced; solve the workpiece process sorting sub-problem of the same workpiece of the product to be produced based on the target production process, and determine the target workpiece process sorting of the same workpiece; solve the equipment process sorting sub-problem of the same processing equipment of the product to be produced based on the target workpiece process sorting, and determine the target equipment process sorting of the same processing equipment; solve the processing equipment selection sub-problem of the product to be produced based on the target equipment process sorting, and determine the target processing equipment for the same process; And the target process of each production process sub-problem is solved cyclically and iteratively, and finally the target production process of the product to be produced is obtained.

9. An electronic device, characterized in that: The device comprises a processor and a memory, wherein the memory stores program codes, and when the program codes are executed by the processor, the processor executes the method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that The storage medium comprises a program code, and when the storage medium is run on an electronic device, the program code is used to enable the electronic device to execute any one of the methods according to claims 1 to 4.

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