Scheduling and sorting method, device, computer equipment, and storage medium

Abstract

The present application relates to a scheduling and sorting method, apparatus, computer equipment, and storage medium. The method includes: obtaining a preset number of first sorting information; determining each second sorting information based on each first sorting information and an adaptive crossover mutation strategy, and updating the parameter value of the adaptive crossover mutation strategy; determining each third sorting information based on each first sorting information, each target device code, and a local adjustment strategy; inputting each first sorting information, each second sorting information, and each third sorting information into a screening model to obtain a preset number of optimized sorting information; iterating the above steps until each target optimized sorting information that meets the preset iteration stop condition is determined; and determining the optimal sorting information based on each target optimized sorting information that meets the preset iteration stop condition. The use of this method can make the obtained optimal sorting information more effective for multi-objective optimization.

Description

Technical Field

[0001] The present application relates to the technical field of job scheduling, and in particular to a scheduling and sorting method, apparatus, computer equipment, storage medium, and computer program product. Background Art

[0002] In previous workshop production scheduling research, researchers used multiple objectives corresponding to the sorting information as optimization indicators in the process of optimizing scheduling sorting, and derived a multi-objective scheduling sorting strategy.

[0003] Traditional multi-objective scheduling and sorting strategies randomly arrange known equipment, workpieces, and processes to obtain initial sorting information. This initial sorting information is then fed into the multi-objective scheduling and sorting strategy to obtain new sorting information. After filtering the initial and new sorting information, the filtered sorting information is used as the initial sorting information and the random permutation step is iteratively performed. When the number of iterations is met, the sorting information obtained from the last iteration is filtered to determine the optimal sorting information. The optimal sorting information obtained using this strategy is poorly effective for multi-objective optimization and cannot meet practical work requirements. Summary of the Invention

[0004] Based on this, it is necessary to provide a scheduling and sorting method, apparatus, computer equipment, computer-readable storage medium and computer program product to address the above technical issues.

[0005] In a first aspect, the present application provides a scheduling and sorting method. The method comprises:

[0006] Acquire a preset number of first sorting information; the first sorting information is used to reflect the arrangement order of the target equipment codes corresponding to each target process code when the arrangement order of the target process codes is preset;

[0007] Determine each second sorting information according to each first sorting information and an adaptive crossover mutation strategy, and update a parameter value of the adaptive crossover mutation strategy;

[0008] determining each third sorting information according to each first sorting information, each target device code, and a local adjustment strategy;

[0009] Inputting each of the first ranking information, each of the second ranking information, and each of the third ranking information into a screening model to obtain the preset number of optimized ranking information;

[0010] If the preset iterative stopping condition is not satisfied, taking each optimized ranking information as each first ranking information, and returning to the step of determining each second ranking information based on each first ranking information and the adaptive crossover mutation strategy, and updating the parameter value of the adaptive crossover mutation strategy, until each target optimized ranking information that satisfies the preset iterative stopping condition is determined;

[0011] The optimal sorting information is determined based on the target optimization sorting information that meets the preset iteration stop condition.

[0012] Optionally, obtaining a preset number of first sorting information includes:

[0013] Obtaining the arrangement order of each target equipment code, each target process code, and each preset target process code;

[0014] Inputting the target equipment code, the target process code, and the arrangement order of the target process code into a random sorting network to obtain initial sorting information;

[0015] Each of the initial ranking information is input into a screening model to obtain a preset number of first ranking information.

[0016] Optionally, updating the parameter value of the adaptive crossover mutation strategy includes:

[0017] Inputting the current iteration number into the parameter adjustment algorithm to obtain new parameter values of the adaptive crossover mutation strategy;

[0018] The original parameter values of the adaptive crossover mutation strategy are updated with the new parameter values.

[0019] Optionally, the local adjustment strategy includes an extreme optimization mutation strategy and a random optimization mutation strategy; and determining each third sorting information according to each first sorting information, each target device code, and the local adjustment strategy includes:

[0020] Selecting each sparse sorting information according to each of the first sorting information;

[0021] Inputting each of the target device codes and each of the sparse sorting information into the extreme optimization mutation strategy to obtain each of the third initial sorting information;

[0022] Input each of the third initial sorting information into the random optimization mutation strategy to obtain each of the third sorting information.

[0023] Optionally, inputting each target device code and each sparse sorting information into the extreme optimization mutation strategy to obtain each third initial sorting information includes:

[0024] For each sparse sorting information, selecting a first target process code and a first target equipment code corresponding to the first target process code from each target process code in the sparse sorting information;

[0025] Each third initial sorting information is determined according to the sparse sorting information, each target device code, and a first target device code corresponding to the first target process code.

[0026] Optionally, the target optimization ranking information includes target values of the optimization targets, and determining the optimal ranking information based on the target optimization ranking information includes:

[0027] For each target optimization ranking information, performing weighted sum calculation on the target values of each target in the target optimization ranking information to determine the target weighted value of the target optimization ranking information;

[0028] The target optimization ranking information corresponding to the maximum target weight value is selected as the optimal ranking information.

[0029] In a second aspect, the present application further provides a scheduling and sorting device. The device comprises:

[0030] An acquisition module is configured to acquire a preset number of first sorting information; the first sorting information is configured to reflect the arrangement order of the target equipment codes corresponding to the target process codes when the arrangement order of the target process codes is preset;

[0031] a first determining module, configured to determine each second sorting information according to each first sorting information and an adaptive crossover mutation strategy, and to update a parameter value of the adaptive crossover mutation strategy;

[0032] a second determining module, configured to determine each piece of third sorting information according to each piece of the first sorting information, each piece of the target device code, and a local adjustment strategy;

[0033] an input module, configured to input each of the first ranking information, each of the second ranking information, and each of the third ranking information into a screening model to obtain the preset number of optimized ranking information;

[0034] an iteration module, configured to, if a preset iteration stopping condition is not satisfied, use each optimized ranking information as each first ranking information, and return to the step of determining each second ranking information based on each first ranking information and an adaptive crossover mutation strategy, and updating a parameter value of the adaptive crossover mutation strategy, until each target optimized ranking information that satisfies the preset iteration stopping condition is determined;

[0035] The third determining module is configured to determine the optimal sorting information based on the target optimization sorting information that satisfies the preset iterative stopping condition.

[0036] Optionally, the acquisition module is specifically configured to:

[0037] Obtaining the arrangement order of each target equipment code, each target process code, and each preset target process code;

[0038] Inputting the target equipment code, the target process code, and the arrangement order of the target process code into a random sorting network to obtain initial sorting information;

[0039] Each of the initial ranking information is input into a screening model to obtain a preset number of first ranking information.

[0040] Optionally, the first determining module is specifically configured to:

[0041] Inputting the current iteration number into the parameter adjustment algorithm to obtain new parameter values of the adaptive crossover mutation strategy;

[0042] The original parameter values of the adaptive crossover mutation strategy are updated with the new parameter values.

[0043] Optionally, the local adjustment strategy includes an extreme optimization mutation strategy and a random optimization mutation strategy; the second determination module is specifically configured to:

[0044] Selecting each sparse sorting information according to each of the first sorting information;

[0045] Inputting each of the target device codes and each of the sparse sorting information into the extreme optimization mutation strategy to obtain each of the third initial sorting information;

[0046] Input each of the third initial sorting information into the random optimization mutation strategy to obtain each of the third sorting information.

[0047] Optionally, the second confirmation module is specifically configured to:

[0048] For each sparse sorting information, selecting a first target process code and a first target equipment code corresponding to the first target process code from each target process code in the sparse sorting information;

[0049] Each third initial sorting information is determined according to the sparse sorting information, each target device code, and a first target device code corresponding to the first target process code.

[0050] Optionally, the third determining module is specifically configured to:

[0051] For each target optimization ranking information, performing weighted sum calculation on the target values of each target in the target optimization ranking information to determine the target weighted value of the target optimization ranking information;

[0052] The target optimization ranking information corresponding to the maximum target weight value is selected as the optimal ranking information.

[0053] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0054] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the storage medium includes a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any one of the methods of the first aspect.

[0055] In a fifth aspect, the present application provides a computer program product, comprising: a computer program, characterized in that when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0056] The above-mentioned scheduling and sorting method, apparatus, computer equipment and storage medium obtain a preset number of first sorting information; the first sorting information is used to reflect the arrangement order of the target equipment codes corresponding to each target process code under the preset arrangement order of the target process code; according to each first sorting information and the adaptive crossover mutation strategy, each second sorting information is determined, and the parameter value of the adaptive crossover mutation strategy is updated; according to each first sorting information, each target equipment code, and the local adjustment strategy, each third sorting information is determined; each first sorting information, each second sorting information, and each third sorting information is input into the screening model to obtain the preset number of optimized sorting information; if the preset iteration stopping condition is not met, each optimized sorting information is used as each first sorting information, and the step of determining each second sorting information and updating the parameter value of the adaptive crossover mutation strategy according to each first sorting information and the adaptive crossover mutation strategy is returned to execute until each target optimized sorting information that meets the preset iteration stopping condition is determined; the optimal sorting information is determined according to each target optimized sorting information that meets the preset iteration stopping condition. New sorting information is obtained by applying an adaptive cross-compilation strategy that automatically updates parameter values to the obtained sorting information, as well as an optimized local adjustment strategy. The obtained sorting information and the new sorting information are preferentially selected, and the above steps are iterated to obtain optimized sorting information for each target, and the optimized sorting information for each target is preferentially selected again to obtain the optimal sorting information, so that the obtained optimal sorting information has a better effect on multi-objective optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1A flowchart of a scheduling and sorting method according to an embodiment is shown;

[0058] Figure 2 Schematic diagram of a flow chart of a step of obtaining first sorting information in one embodiment;

[0059] Figure 3 FIG. 1 is a flow chart of a step of determining third sorting information in one embodiment;

[0060] Figure 4 A flowchart of a scheduling and sorting method according to another embodiment is shown;

[0061] Figure 5 It is a structural block diagram of a scheduling and sorting device in one embodiment;

[0062] Figure 6 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0064] The scheduling and sorting method provided in the embodiment of the present application can be applied to a terminal, a server, or a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. The terminal may include but is not limited to various personal computers, laptops, tablet computers, etc. The terminal is used to obtain a preset number of first arrangement orders; determine each second sorting information based on each first sorting information and an adaptive crossover mutation strategy; determine each third sorting information based on each first sorting information and a local adjustment strategy; input each first sorting information, each second sorting information, and each third sorting information into a screening model to obtain the preset number of optimized sorting information; and iterate until each target optimized sorting information that meets the preset iteration stop condition is determined; and determine the optimal sorting information based on each target optimized sorting information that meets the preset iteration stop condition.

[0065] In one embodiment, Figure 1 As shown, a scheduling and sorting method is provided. Taking the method applied to a terminal as an example, the method includes the following steps:

[0066] Step S101: Obtain a preset number of first sorting information.

[0067] The first sorting information is used to reflect the arrangement order of the target equipment codes corresponding to the target process codes when the arrangement order of the target process codes is preset.

[0068] In this embodiment, the terminal pre-stores the number of first sorting information that needs to be obtained. The terminal obtains the arrangement order of the target equipment codes corresponding to each target process code; and obtains the target value of each optimization target through the arrangement order of the target equipment codes corresponding to each target process code; finally, the obtained arrangement order of each target equipment and the target value of each optimization target corresponding to each arrangement order are used as the first sorting information. The specific acquisition process will be described in detail later. A target device can only process one target process at a time. Different target devices have different processing times for the same target process. Each target device can process each target process. The arrangement order of each target process of the same workpiece is the same, but the subsequent process can only start processing after the previous process is completed. Each target process code is the code of each process required to process a workpiece, and each target device code is the code of each target device that executes each target process.

[0069] Optimization objectives may include, but are not limited to, completion time, equipment load rate, production efficiency, and unit energy consumption. Completion time is the theoretical time required to complete a single process of a workpiece according to the order of the target process codes; equipment load rate is the maximum number of parts processed per unit time by the target equipment corresponding to each target process; production efficiency is the number of parts processed per unit time; and unit energy consumption is the theoretical total energy consumption per unit time by the target equipment corresponding to each target process.

[0070] Step S102 : determining each second sorting information according to a preset number of first sorting information and an adaptive crossover mutation strategy, and updating a parameter value of the adaptive crossover mutation strategy.

[0071] In this embodiment, the adaptive crossover and mutation strategy includes an adaptive crossover strategy and an adaptive mutation strategy. The terminal determines each initial second arrangement order based on a preset number of acquired first sorting information and the adaptive crossover strategy, and updates the parameter value of the adaptive crossover strategy. Then, based on each initial second arrangement order and the adaptive mutation strategy, the terminal determines each second arrangement order and updates the parameter value of the adaptive mutation strategy. Optionally, in another embodiment, the terminal determines each initial second arrangement order based on each acquired first arrangement order and the adaptive mutation strategy, and updates the parameter value of the adaptive mutation strategy. Then, based on each initial second sorting information and the adaptive crossover strategy, the terminal determines each second arrangement order and updates the parameter value of the adaptive crossover strategy. The order in which the terminal executes the adaptive crossover strategy and the adaptive mutation strategy is not limited.

[0072] Step S103 : determining each third sorting information according to a preset number of first sorting information, each target device code, and a local adjustment strategy.

[0073] In this embodiment, the terminal determines the third orderings based on the acquired first orderings, target device codes, and local adjustment strategies. The specific calculation process will be described in detail later.

[0074] In step S104 , a preset number of first ranking information, each second ranking information, and each third ranking information are input into a screening model to obtain a preset number of optimized ranking information.

[0075] In this embodiment, the screening model includes a ranking module, a congestion calculation module, and a optimization module. The terminal combines a preset number of first ranking information, each second ranking information, and each third ranking information into a ranking group, and sequentially inputs the ranking group into the ranking module, the congestion calculation module, and the optimization module to obtain a preset number of optimized ranking information.

[0076] Sorting module: The terminal arranges and divides the sorting information of the sorting group according to the target value of each optimization target of each sorting information. When the target value of each optimization target of sorting information A is greater than the target value of each optimization target of sorting information B, the sorting order of sorting information A is before the sorting order of sorting information B. When the target value of each optimization target of sorting information A is different from the target value of each optimization target of sorting information B, the two sorting information are arranged in parallel without any order. The terminal divides the sorted sorting information into sub-sorting groups in order from front to back, and each sub-sorting group contains the same number of sorting information.

[0077] Congestion calculation module: For each piece of sorting information, the terminal inputs the congestion algorithm according to the target value of each optimization target of the sorting information, determines the congestion distance of each optimization target of the sorting information, and sums the congestion distances of each optimization target of the sorting information to obtain the congestion distance of the sorting information.

[0078] Optimization module: The terminal first selects the sub-sorting group containing the sorting information in the front row; if the number of sorting information in this group is less than the preset number, the terminal selects all the sorting information in this group; and then selects the sub-sorting group whose sorting information is ranked second only to the selected group; if the sum of the number of sorting information in the selected sub-sorting groups is less than the preset number, the above steps are repeated until the sum of the number of sorting information in the selected sub-sorting groups is greater than the preset number. In the last sub-sorting group selected by the terminal, by comparing the size of the congestion distance of each sorting information in the sub-sorting group, the sorting information with the largest congestion distance is preferentially selected. When the number of all selected sorting information is equal to the preset number, the terminal stops executing this step; and the selected preset number of sorting information is used as the preset number of optimized sorting information.

[0079] Step S105: If the preset iteration stopping condition is not met, each optimized sorting information is used as a preset number of first sorting information, and the process returns to the step of determining each second sorting information based on the preset number of first sorting information and the adaptive crossover mutation strategy, and updating the parameter value of the adaptive crossover mutation strategy, until each target optimized sorting information that meets the preset iteration stopping condition is determined.

[0080] In this embodiment, the preset iteration stop condition is the number of iterations. When the number of iterations is not met, the terminal uses each optimization sorting information as the preset number of first sorting information and returns to execute step S102; until the number of iterations is met, the terminal outputs each target optimization sorting information obtained from the last iteration (that is, each target optimization sorting information that meets the preset iteration stop condition).

[0081] Step S106 , determining the optimal sorting information based on the target optimization sorting information that meets the preset iteration stop condition.

[0082] In this embodiment, the terminal screens the target optimization ranking information obtained in the last iteration (i.e., the target optimization ranking information that meets the preset iteration stop condition), and selects the target optimization ranking information corresponding to the optimal target value of each optimization target from the target optimization ranking information as the optimal ranking information.

[0083] Based on the above scheme, new sorting information is obtained by automatically updating the parameter values of the acquired sorting information through an adaptive cross-compilation strategy and an optimized local adjustment strategy; the acquired sorting information and the new sorting information are preferentially selected, and the above steps are iterated to obtain the optimized sorting information of each target, and the optimized sorting information of each target is preferentially selected again to obtain the optimal sorting information, so that the obtained optimal sorting information has a better effect on multi-objective optimization.

[0084] Optional, such as Figure 2 As shown, obtaining a preset number of first sorting information includes:

[0085] Step S201 , obtaining the arrangement order of each target equipment code, each target process code, and each preset target process code.

[0086] In this embodiment, the terminal obtains each target process through the workpiece to be processed and the processing steps required for the workpiece; and encodes each target process to obtain each target process code; the terminal determines the arrangement order of each target process code according to the sequence of the processing steps of the workpiece; and obtains each target device according to each device that can execute the target process, and encodes each target device to obtain each target device code.

[0087] In step S202 , each target device code, each target process code, and the arrangement order of each target process code are input into a random sorting network to obtain initial sorting information.

[0088] In this embodiment, the terminal uses a random sorting network to sort the target equipment codes according to the order of the target process codes. For each target equipment, the terminal pre-stores the target value of the optimization target for each target equipment. The terminal then calculates the target value of each optimization target corresponding to the order of the target equipment by summing and averaging the target values of the optimization targets for the target equipment corresponding to each target process. The terminal then uses the order of the target equipment codes and the target value of each optimization target corresponding to the order of the target equipment codes as initial sorting information.

[0089] Step S203: Input each initial sorting information into a screening model to obtain a preset number of first sorting information.

[0090] In this embodiment, the terminal inputs each initial sorting information into the screening model to obtain a preset number of first sorting information.

[0091] Based on the above solution, each piece of initial sorting information is randomly acquired and then screened to obtain each piece of first sorting information, thereby improving the feasibility of each piece of first sorting information.

[0092] Optionally, updating the parameter values of the adaptive crossover mutation strategy includes: inputting the number of current iterations into a parameter adjustment algorithm to obtain new parameter values of the adaptive crossover mutation strategy; and updating the original parameter values of the adaptive crossover mutation strategy with the new parameter values.

[0093] In this embodiment, the parameter value of the adaptive crossover strategy is the crossover rate, and the parameter value of the adaptive mutation strategy is the mutation rate. After each iteration, the terminal obtains the number of iterations for the next iteration. When step S103 is executed next time and the second sorting information is determined, the terminal determines a new crossover rate using the crossover rate model and updates the original crossover rate of the adaptive crossover strategy with the new crossover rate; the terminal determines a new mutation rate using the mutation rate model and updates the original mutation rate of the adaptive mutation strategy with the new mutation rate. The entire iteration can be divided into an initial stage, a mid-stage, and a late stage according to the number of iterations. The division criteria for each stage are:

[0094] N1=αN

[0095] N2=(1-α)N

[0096] In the above formula, α is the stage division parameter, N is the maximum number of iterations, 0~N1 is the early stage of evolution, N1~N2 is the middle stage of evolution, and N2~N is the late stage of evolution.

[0097] Crossover rate model:

[0098]

[0099] Mutation rate model:

[0100]

[0101] In the above formula, P c is the individual crossover rate, P m is the individual mutation rate, and β is the adjustment parameter of the crossover rate and mutation rate.

[0102] Based on the above scheme, by updating the parameter values of the adaptive crossover mutation strategy, the search range is dynamically adjusted in the early stage of iteration, so that the algorithm has a larger search range in the early stage, improving the global search ability of the algorithm, and a smaller search range in the later stage, improving the ability of the population to approach the optimal solution, thereby improving the efficiency of the algorithm.

[0103] Optional, such as Figure 3 As shown, the local adjustment strategy includes an extreme optimization mutation strategy and a random optimization mutation strategy; according to a preset number of first sorting information, each target device code, and the local adjustment strategy, each third sorting information is determined, including:

[0104] Step S301 : selecting each sparse sorting information according to a preset number of first sorting information.

[0105] In this embodiment, the terminal selects the first sorting information whose congestion distance is less than a preset congestion distance threshold as the sparse sorting information.

[0106] Step S302 : Input each target device code and each sparse sorting information into the extreme optimization mutation strategy to obtain each third initial sorting information.

[0107] In this embodiment, the terminal inputs each target device code and each sparse sorting information into the extreme optimization mutation strategy to obtain each third initial sorting information; the specific calculation process will be described in detail later.

[0108] Step S303: input each third initial sorting information into the random optimization mutation strategy to obtain each third sorting information.

[0109] In this embodiment, the random optimization mutation strategy includes a swap mutation strategy, an insertion mutation strategy, and a reverse order mutation strategy. For each piece of third initial sorting information, the terminal inputs the third initial sorting information into the swap mutation strategy, the insertion mutation strategy, and the reverse order mutation strategy, respectively, to obtain the third sorting information. The order in which the third initial sorting information is input into the swap mutation strategy, the insertion mutation strategy, and the reverse order mutation strategy is not limited.

[0110] The swap mutation strategy is to randomly select target equipment codes corresponding to two target process codes at different arrangement positions in the third initial sorting information, and swap the target equipment codes corresponding to the two target process codes at different arrangement positions to obtain the third sorting information. For example, the order of the target process codes in the third initial sorting information is: a1, a2, a3, a4, a5, a6, and the order of the target equipment codes corresponding to the target process codes is: b1, b2, b3, b4, b5, b6. Select b2 corresponding to a2 and b4 corresponding to a4 and input the swap mutation strategy. The order of the target process codes in the third sorting information is: a1, a2, a3, a4, a5, a6, and the order of the target equipment codes corresponding to the target process codes is: b1, b4, b3, b2, b5, b6.

[0111] The insertion mutation strategy involves randomly selecting the target equipment code corresponding to the target process code in the third initial sorting information. The target equipment code corresponding to the target process code with the later position replaces the target equipment code corresponding to the previous target process code with the earlier position. The remaining target equipment codes are then shifted one position backward, resulting in the third sorting information. For example, the order of the target process codes in the third initial sorting information is a1, a2, a3, a4, a5, a6, and the order of the target equipment codes corresponding to the target process codes is b1, b2, b3, b4, b5, b6. Selecting b3 corresponding to a3 and b5 corresponding to a5 and applying the insertion mutation strategy results in the order of the target process codes in the third sorting information being a1, a2, a3, a4, a5, a6, and the order of the target equipment codes corresponding to the target process codes being b1, b2, b5, b3, b4, b6.

[0112] The reverse order mutation strategy involves randomly selecting the target equipment code corresponding to the target process code at the corresponding position in the third initial sorting information, and then reversely sorting all target equipment codes between (including) the two target equipment codes to obtain the third sorting information. For example, the target process codes in the third initial sorting information are arranged in the order of a1, a2, a3, a4, a5, a6, and the target equipment codes corresponding to the target process codes are arranged in the order of b1, b2, b3, b4, b5, b6. Selecting b3 corresponding to a3 and b5 corresponding to a5 and applying the insertion mutation strategy results in the target process codes in the third sorting information being arranged in the order of a1, a2, a3, a4, a5, a6, and the target equipment codes corresponding to the target process codes being arranged in the order of b1, b2, b5, b4, b3, b6.

[0113] Based on the above scheme, the sparse arrangement information is adjusted through a local adjustment strategy to obtain the third sorting information, which enhances the local search capability of the algorithm and expands the diversity of the sorting information.

[0114] Optionally, each target equipment code and each sparse sorting information is input into the extreme optimization mutation strategy to obtain each third initial sorting information, including: for each sparse sorting information, selecting the first target process code and the first target equipment code corresponding to the first target process code from each target process code of the sparse sorting information; determining the third initial sorting information based on the sparse sorting information, each target equipment code, and the first target equipment code corresponding to the first target process code.

[0115] In this embodiment, for each sparse sorting information, the terminal selects the first target process code and the first target device code corresponding to the first target process code from each process code in the sparse sorting information, and selects a new target device code from each target device code other than the target device code corresponding to each target process code contained in the coefficient sorting information to replace the first target device code, thereby determining the third sorting information.

[0116] For example, the target process codes in the third initial sorting information are arranged in the order of a1, a2, a3, a4, a5, a6, and the target equipment codes corresponding to the target process codes are arranged in the order of b1, b2, b3, b4, b5, b6. Select b3 corresponding to a3, and select b7 to replace b3 in each target equipment code except b1, b2, b3, b4, b5, and b6. The target process codes in the third sorting information are arranged in the order of a1, a2, a3, a4, a5, a6, and the target equipment codes corresponding to the target process codes are arranged in the order of b1, b2, b7, b4, b5, and b6.

[0117] Based on the above scheme, the sparse arrangement information is adjusted through the extreme optimization mutation strategy to obtain the third initial sorting information, which enhances the local search ability of the algorithm and expands the diversity of sorting information.

[0118] Optionally, the target optimization ranking information includes the target value of each optimization target, and the optimal ranking information is determined based on each target optimization ranking information, including: for each target optimization ranking information, performing weighted summation calculation on the target value of each target in the target optimization ranking information to determine the target weight value of the target optimization ranking information; selecting the target optimization ranking information corresponding to the maximum target weight value as the optimal ranking information.

[0119] In this embodiment, the terminal determines the weight of each target through the Analytic Hierarchy Process (AHP), optimizes the ranking information for each target, adds up the target values of each target in the target optimization ranking information, obtains the target weight value of each target optimization ranking information, and selects the target optimization ranking information corresponding to the maximum target weight value as the optimal ranking information.

[0120] Based on the above scheme, by performing weighted summation on the target optimization ranking information, the target optimization ranking information with the largest weighted value is selected as the optimal ranking information, which further improves the effect of the obtained optimal ranking information.

[0121] This application also provides a scheduling order example, such as Figure 4 As shown, the specific processing process includes the following steps:

[0122] Step S401 , obtaining the arrangement order of each target equipment code, each target process code, and each preset target process code.

[0123] In step S402 , each target device code, each target process code, and the arrangement order of each target process code are input into a random sorting network to obtain initial sorting information.

[0124] In step S403, each initial sorting information is input into a screening model to obtain a preset number of first sorting information. The first sorting information is used to reflect the sorting order of the target equipment codes corresponding to each target process code under the preset sorting order of the target process codes.

[0125] Step S404 : determining each second sorting information according to each first sorting information and the adaptive crossover mutation strategy.

[0126] Step S405 : Input the current iteration number into the parameter adjustment algorithm to obtain new parameter values of the adaptive crossover mutation strategy.

[0127] Step S406: Update the original parameter values of the adaptive crossover mutation strategy with the new parameter values.

[0128] Step S407: Select each sparse sorting information according to each first sorting information.

[0129] Step S408 : for each sparse sorting information, select a first target process code and a first target equipment code corresponding to the first target process code from each target process code in the sparse sorting information.

[0130] Step S409 : determining each third initial sorting information according to the sparse sorting information, each target equipment code, and the first target equipment code corresponding to the first target process code.

[0131] Step S410: Input each third initial sorting information into the random optimization mutation strategy to obtain each third sorting information.

[0132] In step S411 , each piece of first ranking information, each piece of second ranking information, and each piece of third ranking information are input into a screening model to obtain a preset number of optimized ranking information.

[0133] Step S412: determine whether a preset iteration stop condition is met.

[0134] If yes, execute step S413; if no, use each optimized sorting information as each first sorting information, and execute step S404.

[0135] Step S413: Determine the optimization ranking information of each target.

[0136] Step S414: for each target optimization ranking information, perform weighted sum calculation on the target value of each target in the target optimization ranking information to determine the target weight value of the target optimization ranking information.

[0137] Step S415: Select the target optimization ranking information corresponding to the maximum target weight value as the optimal ranking information.

[0138] The steps in the flowcharts of the various embodiments described are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts of the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0139] Based on the same inventive concept, embodiments of the present application also provide a scheduling and sequencing device for implementing the aforementioned scheduling and sequencing method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more scheduling and sequencing device embodiments provided below can be found in the above-mentioned limitations of the scheduling and sequencing method, and will not be further elaborated here.

[0140] In one embodiment, Figure 5 As shown, a scheduling and sorting device is provided, comprising: an acquisition module 510, a first determination module 520, a second determination module 530, an input module 540, an iteration module 550, and a third determination module 560, wherein:

[0141] An acquisition module 510 is configured to acquire a preset number of first sorting information items; the first sorting information items are configured to reflect the order of target equipment codes corresponding to the target process codes when the order of target process codes is preset.

[0142] A first determining module 520 is configured to determine each second sorting information according to each first sorting information and an adaptive crossover mutation strategy, and to update a parameter value of the adaptive crossover mutation strategy;

[0143] A second determining module 530 is configured to determine third sorting information based on the first sorting information, the target device code, and the local adjustment strategy;

[0144] An input module 540 is used to input each first ranking information, each second ranking information, and each third ranking information into a screening model to obtain a preset number of optimized ranking information;

[0145] Iterative module 550 is configured to, if the preset iteration stopping condition is not satisfied, use each optimized ranking information as each first ranking information, and return to the step of determining each second ranking information based on each first ranking information and the adaptive crossover mutation strategy, and updating the parameter value of the adaptive crossover mutation strategy, until each target optimized ranking information that satisfies the preset iteration stopping condition is determined;

[0146] The third determining module 560 is configured to determine the optimal sorting information based on the target optimization sorting information that satisfies the preset iteration stopping condition.

[0147] Optionally, the acquisition module 510 is specifically configured to:

[0148] Obtaining the arrangement order of each target equipment code, each target process code, and each preset target process code;

[0149] Inputting each target equipment code, each target process code, and the arrangement order of each target process code into a random sorting network to obtain each initial sorting information;

[0150] Each initial sorting information is input into the screening model to obtain a preset number of first sorting information.

[0151] Optionally, the first determining module 520 is specifically configured to:

[0152] Input the current iteration number into the parameter adjustment algorithm to obtain the new parameter value of the adaptive crossover mutation strategy;

[0153] Update the original parameter values of the adaptive crossover mutation strategy with the new parameter values.

[0154] Optionally, the local adjustment strategy includes an extreme optimization mutation strategy and a random optimization mutation strategy; the second determination module 530 is specifically configured to:

[0155] Selecting each sparse sorting information according to each first sorting information;

[0156] Input each target device code and each sparse sorting information into the extreme optimization mutation strategy to obtain each third initial sorting information;

[0157] Each third initial sorting information is input into the random optimization mutation strategy to obtain each third sorting information.

[0158] Optionally, the second confirmation module 530 is specifically configured to:

[0159] For each sparse sorting information, select a first target process code and a first target equipment code corresponding to the first target process code from each target process code in the sparse sorting information;

[0160] The third initial sorting information is determined according to the sparse sorting information, the target equipment codes, and the first target equipment code corresponding to the first target process code.

[0161] Optionally, the third determining module 560 is specifically configured to:

[0162] For each target optimization ranking information, the target values of each target in the target optimization ranking information are weighted and summed to determine the target weight value of the target optimization ranking information;

[0163] The target optimization ranking information corresponding to the maximum target weight value is selected as the optimal ranking information.

[0164] Each module in the scheduling and sequencing device described above may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0165] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a scheduling and sorting method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0166] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0167] In an embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0168] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0169] In an embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0170] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0171] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided in this application may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, etc., but are not limited to these.

[0172] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0173] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A workshop production scheduling method, characterized in that: The method comprises: Obtain each target process through the workpiece to be processed and the processing process required for the workpiece, and encode each target process to obtain each target process code; According to the sequence of the processing procedures of each workpiece, the arrangement order of each target process code is determined, and each device that can perform the target process is used as each target device; each target device is coded as each target device code; wherein, one target device can only process one target process at a time, different target devices have different processing times for the same target process, each target device can process each target process, and the arrangement order of each target process for the same workpiece is the same, but the subsequent target process can only be started after the previous target process is completed; each target process code is the code of each process required to process a workpiece, and each target device code is the code of each target device that performs each target process; The target values of the optimization targets of the target equipment corresponding to each target process are summed and averaged to obtain the target values of the optimization targets corresponding to the arrangement order of each target equipment, and the arrangement order of each target equipment code and the target values of the optimization targets corresponding to the arrangement order of each target equipment are used as the initial sorting information; wherein, each of the optimization targets includes completion time, equipment load rate, production efficiency, and unit energy consumption, the completion time is the theoretical time for a workpiece to complete a processing according to the arrangement order of each target process code, the equipment load rate is the maximum number of parts processed by the target equipment corresponding to each target process in unit time, the production efficiency is the number of parts processed in unit time, and the unit energy consumption is the theoretical total energy consumption of the target equipment corresponding to each target process in unit time; Inputting each of the initial sorting information into a screening model to obtain a preset number of first sorting information; the first sorting information is used to reflect the arrangement order of the target equipment codes corresponding to each target process code under the preset arrangement order of the target process codes; Determining each second sorting information according to each first sorting information and an adaptive crossover mutation strategy; Inputting the number of current iterations into a parameter adjustment algorithm to obtain a new parameter value of the adaptive crossover mutation strategy; the adaptive crossover mutation strategy includes an adaptive crossover strategy and an adaptive mutation strategy, the parameter value of the adaptive crossover strategy is a crossover rate, and the parameter value of the adaptive mutation strategy is a mutation rate; Determine a new crossover rate through a crossover rate model, and update the original crossover rate of the adaptive crossover strategy with the new crossover rate; A new mutation rate is determined through a mutation rate model, and the new mutation rate is used to update the original mutation rate of the adaptive mutation strategy; wherein the entire iteration is divided into an initial stage, a middle stage, and a late stage according to the number of iterations, and the division criteria for each stage are: ； ； In the above formula, is the stage division parameter, N is the maximum number of iterations, In the early stages of evolution, The middle stage of evolution, It is the late stage of evolution; Crossover rate model: ； Mutation rate model: ； In the above formula, is the individual crossover rate, is the individual mutation rate, is the adjustment parameter of crossover rate and mutation rate; Selecting each sparse sorting information according to each of the first sorting information; For each sparse sorting information, selecting a first target process code and a first target equipment code corresponding to the first target process code from each target process code in the sparse sorting information; determining each third initial sorting information according to the sparse sorting information, each target device code, and a first target device code corresponding to the first target process code; Inputting each of the third initial sorting information into a random optimization mutation strategy to obtain each of the third sorting information; Inputting each of the first ranking information, each of the second ranking information, and each of the third ranking information into a screening model to obtain the preset number of optimized ranking information; If the preset iterative stopping condition is not satisfied, taking each optimized ranking information as each first ranking information, and returning to the step of determining each second ranking information based on each first ranking information and the adaptive crossover mutation strategy, and updating the parameter value of the adaptive crossover mutation strategy, until each target optimized ranking information that satisfies the preset iterative stopping condition is determined; The optimal sorting information is determined based on the target optimization sorting information that meets the preset iteration stop condition.

2. The method according to claim 1, characterized in that The target optimization ranking information includes target values of each optimization target, and determining optimal ranking information based on each target optimization ranking information includes: For each target optimization ranking information, performing weighted sum calculation on the target values of each target in the target optimization ranking information to determine the target weighted value of the target optimization ranking information; The target optimization ranking information corresponding to the maximum target weight value is selected as the optimal ranking information.

3. A workshop production scheduling and sequencing device, characterized in that: The device comprises: The acquisition module is used to obtain each target process through the workpiece to be processed and the processing steps required for the workpiece, and encode each target process to obtain each target process code; according to the sequence of the processing steps of each workpiece, the arrangement order of each target process code is determined, and each device that can execute the target process is used as each target device; each target device is encoded as each target device code; wherein, one target device can only process one target process at a time, different target devices have different processing times for the same target process, each target device can process each target process, and the arrangement order of each target process of the same workpiece is the same, but the subsequent target process can only start processing after the previous target process is completed; each target process code is the code of each process required to process a workpiece, and each target device is encoded as each target device that executes each target process. The coding of the target equipment; by adding and averaging the target values of the optimization targets of the target equipment corresponding to each target process, the target values of the optimization targets corresponding to the arrangement order of each target equipment are obtained, and the arrangement order of each target equipment code and the target values of the optimization targets corresponding to the arrangement order of each target equipment are used as the initial sorting information; wherein, each of the optimization targets includes completion time, equipment load rate, production efficiency, and unit energy consumption, the completion time is the theoretical time for a workpiece to complete a processing according to the arrangement order of each target process code, the equipment load rate is the maximum number of parts processed by the target equipment corresponding to each target process in unit time, the production efficiency is the number of parts processed in unit time, and the unit energy consumption is the theoretical total energy consumption of the target equipment corresponding to each target process in unit time; Inputting each of the initial sorting information into a screening model to obtain a preset number of first sorting information; the first sorting information is used to reflect the arrangement order of the target equipment codes corresponding to each target process code under the preset arrangement order of the target process codes; The first determination module is configured to determine each second sorting information based on each first sorting information and an adaptive crossover mutation strategy; input the number of current iterations into a parameter adjustment algorithm to obtain a new parameter value of the adaptive crossover mutation strategy; the adaptive crossover mutation strategy includes an adaptive crossover strategy and an adaptive mutation strategy, the parameter value of the adaptive crossover strategy is a crossover rate, and the parameter value of the adaptive mutation strategy is a mutation rate; determine a new crossover rate through a crossover rate model, and update the original crossover rate of the adaptive crossover strategy with the new crossover rate; determine a new mutation rate through a mutation rate model, and update the original mutation rate of the adaptive mutation strategy with the new mutation rate; wherein the entire iteration is divided into an initial stage, a middle stage, and a late stage according to the number of iterations, and the division criteria for each stage are: ; In the above formula, is the stage division parameter, N is the maximum number of iterations, In the early stages of evolution, The middle stage of evolution, For the late stage of evolution; crossover rate model: ; Mutation rate model: In the above formula, is the individual crossover rate, is the individual mutation rate, is the adjustment parameter of crossover rate and mutation rate; a second determination module configured to select sparse sorting information based on each of the first sorting information; for each sparse sorting information, select a first target process code and a first target equipment code corresponding to the first target process code from each target process code in the sparse sorting information; determine each third initial sorting information based on the sparse sorting information, each of the target equipment codes, and the first target equipment code corresponding to the first target process code; and input each of the third initial sorting information into a random optimization mutation strategy to obtain each third sorting information; an input module, configured to input each of the first ranking information, each of the second ranking information, and each of the third ranking information into a screening model to obtain the preset number of optimized ranking information; an iteration module, configured to, if a preset iteration stopping condition is not satisfied, use each optimized ranking information as each first ranking information, and return to the step of determining each second ranking information based on each first ranking information and an adaptive crossover mutation strategy, and updating a parameter value of the adaptive crossover mutation strategy, until each target optimized ranking information that satisfies the preset iteration stopping condition is determined; The third determining module is configured to determine the optimal sorting information based on the target optimization sorting information that satisfies the preset iterative stopping condition.

4. The device according to claim 3, characterized in that The third determining module is specifically configured to: For each target optimization ranking information, performing weighted sum calculation on the target values of each target in the target optimization ranking information to determine the target weighted value of the target optimization ranking information; The target optimization ranking information corresponding to the maximum target weight value is selected as the optimal ranking information.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 2 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 2 are implemented.

7. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 2 are implemented.

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