Flexible manufacturing workshop scheduling method and system based on improved wave optimization algorithm

Abstract

The invention relates to a flexible manufacturing workshop scheduling method and system based on an improved wave optimization algorithm. The method comprises the following steps: inputting the number of to-be-produced workpieces, the number of each to-be-produced workpiece, the number of machines in a flexible job shop, a process table of each to-be-produced workpiece and a processing time table of each process of each workpiece on the machines; the shortest machining time of each working procedure of each workpiece is determined; formulating a feasible processing scheme, and generating an initial solution and completion time; calling an improved wave optimization method to calculate an optimal scheduling scheme; according to the method, the production scheduling scheme making efficiency can be effectively improved, and the scheduling scheme can be flexibly adjusted according to the production process; the method can adapt to the flexible scheduling problem of different types, different numbers, different machine numbers and processing time, has very high universality, and can improve the production efficiency of enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic process scheduling of flexible manufacturing systems, and in particular to a flexible manufacturing workshop scheduling method and system based on an improved wave optimization algorithm. Background Art

[0002] Flexible production lines are based on digital technology and can automatically complete a variety of processing operations, greatly reducing the degree of manual intervention. Flexible processing is highly flexible and can quickly adjust production lines to meet the production needs of different products. This flexibility enables companies to respond quickly to market changes and meet the diverse needs of customers. Flexible manufacturing technology can achieve mixed-flow production with less floor space, less equipment, and less investment, thereby improving the economic benefits of the company. At the same time, it can also reduce the inventory of parts in the factory and reduce production costs.

[0003] In a flexible job shop, each job can be completed on different machines, and the processing time of each job on different machines varies significantly, and the processing order also varies, making the flexible job shop scheduling problem an NP-Hard problem. At present, the methods to solve this type of problem are mainly heuristic methods, including particle swarm optimization, genetic algorithm, ant colony algorithm and their various improvements and variants. However, the above methods still have certain shortcomings. Including: due to the complexity of the problem itself, the algorithm has general solving ability, long solving time, easy to fall into the local optimal solution and cannot jump out, and the algorithm has weak dynamic adjustment ability; the optimization goal is relatively single and cannot reflect the production needs of enterprises and workshops. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the above-mentioned problems existing in the prior art and provide a flexible manufacturing workshop scheduling method and system based on an improved wave optimization algorithm.

[0005] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0006] In order to solve the above technical problems, the present invention is implemented by adopting the following technical solutions:

[0007] A flexible manufacturing workshop scheduling method based on an improved wave optimization algorithm comprises the following steps:

[0008] Step 1: Input the number of types of workpieces to be produced (m) and the number of each type of workpieces to be produced (n) i , the number of machines k in the flexible job shop, the process table of each workpiece to be produced, and the processing schedule of each process of each workpiece on the machine;

[0009] Step 2: Using the process table of each workpiece and the processing time table of each process of each workpiece on the machine, determine the shortest processing time of each process of each workpiece;

[0010] Step 3: Using the shortest processing time of each workpiece, a feasible processing plan is developed based on the principles of giving priority to workpieces with shorter processing time, giving priority to workpieces with longer processing time, and randomly arranging the processing order, and an initial solution and completion time are generated;

[0011] Step 4: Call the improved wave optimization method to calculate the optimal scheduling plan.

[0012] Furthermore, the specific contents of step 2 are:

[0013] 2.1 Read the process table of m types of workpieces and determine the number of workpiece types;

[0014] 2.2 Read the processing time matrix of each process of m types of workpieces on the machine;

[0015] 2.3 According to the processing sequence number in the process table, read the corresponding row in the table of processing time of m types of workpieces, search for the shortest time in the row, and determine the shortest processing time for each process.

[0016] Furthermore, the specific contents of step 3 are:

[0017] 3.1 Sum the shortest processing time of each process and determine the shortest processing time of m types of workpieces;

[0018] 3.2 Arrange the m types of "shortest processing time" in ascending order, give priority to the workpieces with "short" processing time, formulate the "shortest" production plan, and generate the initial solution;

[0019] 3.3 Arrange the m types of “shortest processing time” in descending order, give priority to producing workpieces with “long” processing time, formulate the “longest” production plan, and generate the initial solution;

[0020] 3.4 Randomly sort all workpieces, produce them according to the sorting, formulate a "random" production plan, and generate an initial solution. The number of random sorting times is twice the total number of workpieces;

[0021] 3.5 Construct the initial population using the initial solutions generated in 3.2 to 3.4.

[0022] Furthermore, step 4 specifically includes:

[0023] Initialization parameters:

[0024] Number of particles n, maximum number of iterations T, space size [lb j ,ub j ],in

[0025] n w1 Indicates the number of particles emitted by simulated electromagnetic waves;

[0026] n w2 represents the number of particles simulating the reflection of electromagnetic waves;

[0027] n w3 Indicates the number of particles receiving the simulated electromagnetic wave;

[0028] ub j , lb j are the upper and lower bounds of the search for the j-th problem variable, respectively;

[0029] The algorithm starts:

[0030] Initialize the population using the improved Sobol sequence: W n =W max -r(W max -W min ), where the optimal solution range is [W min ,W max ],W min The minimum value of the optimal solution is equivalent to lb j The collection of W max The maximum value of the optimal solution is equivalent to ub j The set of random numbers r∈[0,1] generated by the Sobol sequence;

[0031] Calculate individual fitness values ​​and sort them;

[0032] According to the formula W i new =W min +r1·(W max -W min )(Formula 1) Update the group position;

[0033] W i new is the new group position, W min is a vector composed of the minimum values ​​of each dimension of W, W max is a vector composed of the maximum values ​​of each dimension of W, and r1 is a random number between 0 and 1;

[0034] When the number of iterations t is less than T

[0035] for i=1to n w1

[0036] According to the formula Update particle positions;

[0037] W best is the current optimal position; W l Press W and W best The position matrix after rearrangement according to the proximity; m is a column vector, the elements follow the normal distribution and are arranged in order;

[0038] for i=1to n w2

[0039] According to the formula W i new =-β·r2·cos(πi / n w2 )·(W best -W fi )+W fi (Formula 3) Update particle position;

[0040] β is the reflection intensity coefficient, r2 is a random value between 0 and 1, and W fi It is to rearrange the position matrix of W in order of fitness value from small to large;

[0041] for i=1to n w3

[0042] According to the formula Update particle positions;

[0043] δ is the acceptance coefficient, η is a random number that follows a normal distribution, λ is the correction coefficient, r3, r4, r5, r6 are random numbers between 0 and 1, It is the uniform optimal position obtained by convolving the historical optimal position;

[0044] Where f is the calculated fitness value;

[0045] for i=1to n

[0046] According to the formula g i =(f(W +εi )-f(W -εi )) / 2ε(Formula 5) and W i =W i -α·g i Update particle positions;

[0047] g is the gradient, W +εi =W i +ε,W -εi =Wi -ε , ε=10 -6 , α is compensation, f is the calculated fitness value;

[0048] Generate reverse solutions using a random reverse learning strategy;

[0049] t=t+1

[0050] Output the optimal solution W best ;

[0051] W best is the optimal solution, according to W i =W i -α·g i W i The fitness value is obtained, and the W with the smallest fitness value i That is W best .

[0052] Furthermore, a flexible manufacturing workshop scheduling method based on the improved wave optimization algorithm also includes step 5: calling a visualization function to display relevant scheduling tasks on company-level, workshop-level and workstation-level display screens.

[0053] A flexible manufacturing workshop scheduling system based on improved wave optimization algorithm, comprising:

[0054] An input parameter module is used to input the number of workpieces to be produced, the quantity of each workpiece to be produced, the number of machines in the flexible workshop, the process table of each workpiece to be produced, and the processing schedule of each process of each workpiece on the machine;

[0055] A module for determining the shortest processing time is used to determine the shortest processing time of each process of each workpiece by using the process table of each workpiece and the processing time table of each process of each workpiece on the machine;

[0056] The processing plan determination module is used to utilize the shortest processing time of each workpiece, formulate feasible processing plans based on the principles of giving priority to workpieces with shorter processing time, giving priority to workpieces with longer processing time, and randomly arranging the processing order, and generate the initial solution and completion time;

[0057] The module for calculating the optimal scheduling solution is used to call the improved wave optimization method to calculate the optimal scheduling solution.

[0058] Furthermore, a flexible manufacturing workshop scheduling system based on the improved wave optimization algorithm also includes:

[0059] The visualization module is used to call the visualization function and display related scheduling tasks on the company-level, workshop-level and workstation-level display screens.

[0060] An apparatus comprising one or more processors;

[0061] A memory for storing one or more programs;

[0062] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described above.

[0063] A computer-readable storage medium stores a computer program, which implements the method described above when executed by a processor.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] Aiming at the needs of a mold manufacturing company for flexible manufacturing in the body-in-white welding production workshop, the present invention proposes a flexible job shop scheduling method and system based on the improved wave optimization algorithm. The method can automatically solve the scheduling plan according to the processing time of N workpieces to be processed on M welding robots. Compared with the existing methods, the method has low computational complexity, strong global solution ability, and the optimization target fits the needs of the enterprise.

[0066] The present invention can effectively improve the efficiency of formulating production scheduling plans, and can flexibly adjust the scheduling plans according to the production process; through the visualization function of the system, the processing order and scheduling plans can be efficiently and intuitively displayed on the workshop-level panel; the present invention can adapt to flexible scheduling problems of different types, different quantities, different numbers of machines and processing times, has strong versatility, and can improve the production efficiency of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The present invention will be further described below in conjunction with the accompanying drawings:

[0068] Figure 1 The present invention provides a flowchart of a flexible manufacturing workshop scheduling method based on an improved wave optimization algorithm according to an embodiment of the present invention;

[0069] Figure 2 Flowchart for calculating the optimal scheduling solution by calling the improved wave optimization method. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical scheme and advantages of the implementation of the present invention clearer, the technical scheme in the embodiment of the present invention will be described in more detail below in conjunction with the drawings in the embodiment of the present invention. In the drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The described embodiments are part of the embodiments of the present invention, not all of the embodiments. The embodiments described below with reference to the drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limitations on the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The embodiments of the present invention are described in detail below in conjunction with the drawings.

[0071] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the scope of protection of the present invention.

[0072] The present invention is described in detail below in conjunction with the accompanying drawings:

[0073] See also Figure 1 , the present invention provides an embodiment, a flexible manufacturing workshop scheduling method based on an improved wave optimization algorithm, which mainly includes five steps.

[0074] Step 1: Input the number of types of workpieces to be produced (m) and the number of each type of workpieces to be produced (n) i , the number of machines in the flexible workshop k, the process table of each workpiece to be produced, and the processing schedule of each process of each workpiece on the machine; establish the process number vector and the process processing time matrix sequence; input the processing process table of the workpiece, set the equipment occupancy schedule to be empty, and enter the loop: set i = 1, retrieve and use the process processing time matrix sequence, search for the lowest processing time row by row, determine the shortest processing time of the i-th process of each workpiece and the completion time of process i of the processing task, update the equipment occupancy schedule, set i = i + 1, and return to the loop; use the loop result to record the final completion time of each workpiece and the processing machine number of each process, calculate the final completion time of the processing task, the cumulative working time of each equipment and the initial solution and the upper limit of the completion time;

[0075] Process table for each workpiece to be produced (unit: none)

[0076]

[0077] Example of the processing time matrix of each process of the i-th type of workpiece to be produced on the machine (unit: minutes)

[0078] Machine 1 Machine 2 Machine 3 … Process 1 1.5 3.0 1.0 … Process 2 1.7 1.1 2.2 … Process 3 1.2 1.0 1.3 … Process 4 2.0 1.7 2.4 … ┇ ┇ ┇ ┇ …

[0079] Step 2: Using the process table of each workpiece and the processing time table of each process of each workpiece on the machine, determine the shortest processing time of each process of each workpiece;

[0080] 2.1 Read the process table of m types of workpieces and determine the number of workpiece types;

[0081] 2.2 Read the processing time matrix of each process of m types of workpieces on the machine;

[0082] 2.3 According to the processing sequence number in the process table, read the corresponding row in the table of processing time of m types of workpieces, search for the shortest time in the row, and determine the shortest processing time for each process.

[0083] Step 3: Using the shortest processing time of each workpiece, a feasible processing plan is developed based on the principles of giving priority to workpieces with shorter processing time, giving priority to workpieces with longer processing time, and randomly arranging the processing order, and an initial solution and completion time are generated;

[0084] 3.1 Sum the shortest processing time of each process and determine the shortest processing time of m types of workpieces;

[0085] 3.2 Arrange the m types of "shortest processing time" in ascending order, give priority to the workpieces with "short" processing time, formulate the "shortest" production plan, and generate the initial solution;

[0086] 3.3 Arrange the m types of “shortest processing time” in descending order, give priority to producing workpieces with “long” processing time, formulate the “longest” production plan, and generate the initial solution;

[0087] 3.4 Randomly sort all workpieces, produce them according to the sorting, formulate a "random" production plan, and generate an initial solution. The number of random sorting times is twice the total number of workpieces;

[0088] 3.5 Construct the initial population using the initial solutions generated in 3.2 to 3.4.

[0089] Step 4: Call the improved wave optimization method to calculate the optimal scheduling solution;

[0090] Initialization parameters:

[0091] Number of particles n, maximum number of iterations T, space size [lb j ,ub j ],in

[0092] nw1 Indicates the number of particles emitted by simulated electromagnetic waves;

[0093] n w2 represents the number of particles simulating the reflection of electromagnetic waves;

[0094] n w3 Indicates the number of particles receiving the simulated electromagnetic wave;

[0095] ub j , lb j are the upper and lower bounds of the search for the j-th problem variable, respectively;

[0096] The algorithm starts:

[0097] Initialize the population using the improved Sobol sequence: W n =W max -r(W max -W min ), where the optimal solution range is [W min ,W max ],W min The minimum value of the optimal solution is equivalent to lb j The collection of W max The maximum value of the optimal solution is equivalent to ub j The set of random numbers r∈[0,1] generated by the Sobol sequence;

[0098] Calculate individual fitness values ​​and sort them;

[0099] According to the formula W i new =W min +r1·(W max -W min )(Formula 1) Update the group position;

[0100] W i new is the new group position, W min is a vector composed of the minimum values ​​of each dimension of W, W max is a vector composed of the maximum values ​​of each dimension of W, and r1 is a random number between 0 and 1;

[0101] When the number of iterations t is less than T

[0102] for i=1to n w1

[0103] According to the formula Update particle positions;

[0104] W best is the current optimal position; W l Press W and W bestThe position matrix after rearrangement according to the proximity; m is a column vector whose elements obey the normal distribution and are arranged in order;

[0105] for i=1to n w2

[0106] According to the formula W i new =-β·r2·cos(πi / n w2 )·(W best -W fi )+W fi (Formula 3) Update particle position;

[0107] β is the reflection intensity coefficient, r2 is a random value between 0 and 1, and W fi It is to rearrange the position matrix of W in order of fitness value from small to large;

[0108] for i=1to n w3

[0109] According to the formula Update particle positions;

[0110] δ is the acceptance coefficient, η is a random number that follows a normal distribution, λ is the correction coefficient, r3, r4, r5, r6 are random numbers between 0 and 1, It is the uniform optimal position obtained by convolving the historical optimal position;

[0111] W i It is the result of an improved greedy mechanism. When the group position fluctuates outward, the group position is not worse than the current group position. This greedy mechanism will have an operation after each updated particle position formula. It is the new particle obtained according to the updated particle formula and W i Compare the fitness values ​​of and keep the smaller one; the formula is Where f is the calculated fitness value;

[0112] for i=1to n

[0113] According to the formula g i =(f(W +εi )-f(W -εi )) / 2ε(Formula 5) and W i =W i -α·g i Update particle positions;

[0114] g is the gradient, W +εi =W i +ε,W -εi =W i -ε ,ε=10 -6 , α is compensation, f is the calculated fitness value;

[0115] Generate reverse solutions using a random reverse learning strategy;

[0116] t=t+1

[0117] Output the optimal solution W best ;

[0118] W best is the optimal solution, according to W i =W i -α·g i W i The fitness value is obtained, and the W with the smallest fitness value i That is W best The formula for calculating the fitness value varies depending on the actual problem.

[0119] Step 5: Call the visualization function to display relevant scheduling tasks on the company-level, workshop-level, and workstation-level display screens.

[0120] The invention discloses an initial feasible solution generation method and an initial population generation solution based on the shortest completion time, the longest completion time and the random sorting method, an initial population generation solution based on an improved Sobel sequence, and an optimization adjustment solution based on improved random reverse learning.

[0121] The present invention provides another embodiment, a flexible manufacturing workshop scheduling system based on an improved wave optimization algorithm, comprising:

[0122] An input parameter module is used to input the number of workpieces to be produced, the quantity of each workpiece to be produced, the number of machines in the flexible workshop, the process table of each workpiece to be produced, and the processing schedule of each process of each workpiece on the machine;

[0123] A module for determining the shortest processing time is used to determine the shortest processing time of each process of each workpiece by using the process table of each workpiece and the processing time table of each process of each workpiece on the machine;

[0124] The processing plan determination module is used to utilize the shortest processing time of each workpiece, formulate feasible processing plans based on the principles of giving priority to workpieces with shorter processing time, giving priority to workpieces with longer processing time, and randomly arranging the processing order, and generate the initial solution and completion time;

[0125] The module for calculating the optimal scheduling solution is used to call the improved wave optimization method to calculate the optimal scheduling solution.

[0126] The visualization module is used to call the visualization function and display related scheduling tasks on the company-level, workshop-level and workstation-level display screens.

[0127] Based on the flexible manufacturing workshop scheduling method based on the improved wave optimization algorithm, the present invention provides another device. The device includes but is not limited to: one or more processors and memories.

[0128] The memory, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions corresponding to the flexible manufacturing workshop scheduling method based on the improved wave optimization algorithm in the embodiment of the present invention. The processor executes various functional applications and data processing of the vehicle by running the software programs, instructions and modules stored in the memory, that is, realizing the flexible manufacturing workshop scheduling method based on the improved wave optimization algorithm.

[0129] The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0130] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, a flexible manufacturing workshop scheduling method based on an improved wave optimization algorithm is implemented. The flexible manufacturing workshop scheduling method based on the improved wave optimization algorithm comprises the following steps:

[0131] Step 1: Input the number of types of workpieces to be produced (m) and the number of each type of workpieces to be produced (n) i , the number of machines k in the flexible job shop, the process table of each workpiece to be produced, and the processing schedule of each process of each workpiece on the machine;

[0132] Step 2: Using the process table of each workpiece and the processing time table of each process of each workpiece on the machine, determine the shortest processing time of each process of each workpiece;

[0133] Step 3: Using the shortest processing time of each workpiece, a feasible processing plan is developed based on the principles of giving priority to workpieces with shorter processing time, giving priority to workpieces with longer processing time, and randomly arranging the processing order, and an initial solution and completion time are generated;

[0134] Step 4: Call the improved wave optimization method to calculate the optimal scheduling solution;

[0135] Step 5: Call the visualization function to display relevant scheduling tasks on the company-level, workshop-level, and workstation-level display screens.

[0136] The computer-readable storage medium provided by the present invention has computer executable instructions that are not limited to the method operations described above, but can also execute related operations in the flexible manufacturing workshop scheduling method based on the improved wave optimization algorithm provided by any embodiment of the present invention.

[0137] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0138] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loading and executing computer program instructions on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (for example, coaxial cable, optical fiber, digital subscriber line DSL) or wireless (for example, infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, a data center, etc. that contains one or more available media integrated. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid-state hard disk Solid State Disk, SSD), etc.

[0139] The above is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any modification, equivalent replacement and improvement made by any person skilled in the art within the technical scope disclosed by the present invention and within the spirit and principle of the present invention shall be covered within the protection scope of the present invention. At the same time, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

Claims

1. A flexible manufacturing workshop scheduling method based on improved wave optimization algorithm, characterized in that: The following steps are involved: Step 1: Input the number of types of workpieces to be produced (m) and the number of each type of workpieces to be produced (n) i , the number of machines k in the flexible job shop, the process table of each workpiece to be produced, and the processing schedule of each process of each workpiece on the machine; Step 2: Using the process table of each workpiece and the processing time table of each process of each workpiece on the machine, determine the shortest processing time of each process of each workpiece; Step 3: Using the shortest processing time of each workpiece, a feasible processing plan is developed based on the principles of giving priority to workpieces with shorter processing time, giving priority to workpieces with longer processing time, and randomly arranging the processing order, and an initial solution and completion time are generated; Step 4: Call the improved wave optimization method to calculate the optimal scheduling plan.

2. The flexible manufacturing workshop scheduling method based on the improved wave optimization algorithm according to claim 1 is characterized in that: The specific contents of step 2 are: 2.1 Read the process table of m types of workpieces and determine the number of workpiece types; 2.2 Read the processing time matrix of each process of m types of workpieces on the machine; 2.3 According to the processing sequence number in the process table, read the corresponding row in the table of processing time of m types of workpieces, search for the shortest time in the row, and determine the shortest processing time for each process.

3. The flexible manufacturing workshop scheduling method based on improved wave optimization algorithm according to claim 1 is characterized in that: The specific contents of step 3 are: 3.1 Sum the shortest processing time of each process and determine the shortest processing time of m types of workpieces; 3.2 Arrange the m types of "shortest processing time" in ascending order, give priority to the workpieces with "short" processing time, formulate the "shortest" production plan, and generate the initial solution; 3.3 Arrange the m types of "shortest processing time" in descending order, give priority to the workpieces with "long" processing time, formulate the "longest" production plan, and generate the initial solution; 3.4 Randomly sort all workpieces, produce them according to the sorting, formulate a "random" production plan, and generate an initial solution. The number of random sorting times is twice the total number of workpieces; 3.5 Construct the initial population using the initial solutions generated in 3.2 to 3.

4.

4. The flexible manufacturing workshop scheduling method based on improved wave optimization algorithm according to claim 1 is characterized in that: Step 4 specifically includes: Initialization parameters: Number of particles n, maximum number of iterations T, space size [lb j ,ub j ],in n w1 Indicates the number of particles emitted by simulated electromagnetic waves; n w2 represents the number of particles simulating the reflection of electromagnetic waves; n w3 Indicates the number of particles receiving the simulated electromagnetic wave; ub j , lb j are the upper and lower bounds of the search for the j-th problem variable, respectively; The algorithm starts: Initialize the population using the improved Sobol sequence: W n =W max -r(W max -W min ), where the optimal solution range is [W min ,W max ],W min The minimum value of the optimal solution is equivalent to lb j The collection of W max The maximum value of the optimal solution is equivalent to ub j The set of random numbers r∈[0,1] generated by the Sobol sequence; Calculate individual fitness values ​​and sort them; According to the formula W i new =W min +r1·(W max -W min ) Update group position; W i new is the new group position, W min is a vector composed of the minimum values ​​of each dimension of W, W max is a vector composed of the maximum values ​​of each dimension of W, and r1 is a random number between 0 and 1; When the number of iterations t is less than T for i=1to n w1 According to the formula Update particle positions; W best is the current optimal position; W l Press W and W best The position matrix after rearrangement according to the proximity; m is a column vector, the elements follow the normal distribution and are arranged in order; for i=1to n w2 According to the formula W i new =-β·r2·cos(πi / n w2 )·(W best -W fi )+W fi Update particle positions; β is the reflection intensity coefficient, r2 is a random value between 0 and 1, and W fi It is to rearrange the position matrix of W in order of fitness value from small to large; for i=1to n w3 According to the formula Update particle positions; δ is the acceptance coefficient, η is a random number that follows a normal distribution, λ is the correction coefficient, r3, r4, r5, r6 are random numbers between 0 and 1, It is the uniform optimal position obtained by convolving the historical optimal position; Where f is the calculated fitness value; for i=1to n According to the formula g i =(f(W +εi )-f(W -εi )) / 2ε and W i =W i -α·g i Update particle positions; g is the gradient, W +εi =W i +ε,W -εi =W i -ε , ε=10 -6 , α is compensation, f is the calculated fitness value; Generate reverse solutions using a random reverse learning strategy; t=t+1 Output the optimal solution W best ; W best is the optimal solution, according to W i =W i -α·g i W i The fitness value is obtained, and the W with the smallest fitness value i That is W best .

5. The flexible manufacturing workshop scheduling method based on improved wave optimization algorithm according to claim 1 is characterized in that: It also includes step 5: calling the visualization function to display the relevant scheduling tasks on the company-level, workshop-level and workstation-level display screens.

6. A flexible manufacturing workshop scheduling system based on improved wave optimization algorithm, characterized in that: include: An input parameter module is used to input the number of workpieces to be produced, the quantity of each workpiece to be produced, the number of machines in the flexible workshop, the process table of each workpiece to be produced, and the processing schedule of each process of each workpiece on the machine; A module for determining the shortest processing time is used to determine the shortest processing time of each process of each workpiece by using the process table of each workpiece and the processing time table of each process of each workpiece on the machine; The processing plan determination module is used to utilize the shortest processing time of each workpiece, formulate feasible processing plans based on the principles of giving priority to workpieces with shorter processing time, giving priority to workpieces with longer processing time, and randomly arranging the processing order, and generate the initial solution and completion time; The module for calculating the optimal scheduling solution is used to call the improved wave optimization method to calculate the optimal scheduling solution.

7. The flexible manufacturing workshop scheduling system based on improved wave optimization algorithm according to claim 6 is characterized in that: Also includes: The visualization module is used to call the visualization function and display related scheduling tasks on the company-level, workshop-level and workstation-level display screens.

8. A device, characterized in that: comprising one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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