Equipment production scheduling multi-objective optimization method and system based on multi-chain collaboration

Abstract

The invention provides an equipment production scheduling multi-objective optimization method and system based on multi-chain collaboration, and relates to the field of production scheduling, and the method comprises the steps: setting input parameters according to workpiece data, agent data, factory data of a supplier production stage and factory data of a manufacturer reprocessing stage in an equipment production process; obtaining a global optimal solution according to the input parameters and a non-dominated sorting genetic algorithm based on variable neighborhood search; the globally optimal solution is decoded to determine a set of workpieces allocated to each machine at the supplier production stage, a machining order of the workpieces on each machine, and a machining order of the workpieces on a single machine at the manufacturer remachining stage. According to the method, collaborative optimization and configuration can be powerfully carried out on resources in two stages of supplier processing and manufacturer packaging, so that the operation efficiency and the integration capability of multi-chain collaborative production of equipment are remarkably improved.

Description

Technical Field

[0001] The present application relates to the technical field of production scheduling, and specifically to a multi-objective optimization method and system for equipment production scheduling based on multi-chain collaboration. Background Art

[0002] With the development of technology, the supply chain system of high-end equipment manufacturing industry has become increasingly complex. The entire production process is no longer completed by a single supply chain alone, but by multiple upstream and downstream enterprises working together. The batch production chain is responsible for batch production of research and development workpieces, and the operation and maintenance chain is responsible for repairing original parts and supplying spare parts. The production process of high-end equipment involves many raw materials, suppliers and manufacturers. The dual-agent scheduling strategy is introduced. The production demand of batch production parts and operation and maintenance parts corresponds to two different agents. The production processes of the batch production chain and the operation and maintenance chain are coordinated and the production capacity of the supply chain is integrated to ensure efficient and reliable production of the entire chain of high-end equipment.

[0003] The existing high-end equipment production scheduling optimization method schedules the production processes of the batch production and operation and maintenance supply chains separately, usually scheduling the production process for a single goal, paying less attention to the dual-agent collaboration in actual situations, and also paying less attention to production scheduling under multi-objective scenarios. As a result, it is impossible to accurately guide the production of batch production parts and operation and maintenance parts of high-end equipment, and it is impossible to minimize the losses caused by delayed production, thus causing insufficient resource utilization. Summary of the invention

[0004] In response to the shortcomings of the prior art, the present application provides a multi-objective optimization method and system for equipment production scheduling based on multi-chain collaboration, which solves the technical problem of insufficient collaborative scheduling optimization of batch production chain and operation and maintenance chain in equipment production.

[0005] To achieve the above objectives, this application is implemented through the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a multi-objective optimization method for equipment production scheduling based on multi-chain collaboration, and the multi-objective optimization method for equipment production scheduling includes:

[0007] S1. According to the workpiece data, agent data, factory data of the supplier's production stage, and factory data of the manufacturer's reprocessing stage in the equipment production process, the input parameters of the non-dominated sorting genetic algorithm based on variable neighborhood search are set; wherein the agent data corresponds to the batch production chain agent A and the operation and maintenance chain agent B; S2. According to the input parameters and the non-dominated sorting genetic algorithm based on variable neighborhood search, the global optimal solution θ is obtained. best ; S3, for the global optimal solution θ bestDecoding is performed to determine the set of workpieces assigned to each machine during the supplier's production phase, the processing sequence of the workpieces on each machine, and the processing sequence of the workpieces on a single machine during the manufacturer's reprocessing phase.

[0008] In a second aspect, the embodiment of the present application provides a multi-objective optimization system for equipment production scheduling based on multi-chain collaboration, and the multi-objective optimization system for equipment production scheduling includes: a parameter setting module, an acquisition module and a decoding module; wherein,

[0009] The parameter setting module is used to set the input parameters of the non-dominated sorting genetic algorithm based on variable neighborhood search according to the workpiece data, agent data, supplier's factory data in the production stage, and manufacturer's factory data in the reprocessing stage in the equipment production process; among which, the agent data corresponds to the batch production chain agent A and the operation and maintenance chain agent B;

[0010] The acquisition module is used to obtain the global optimal solution θ according to the input parameters and the non-dominated sorting genetic algorithm based on variable neighborhood search. best ;

[0011] The decoding module is used to solve the global optimal solution θ best Decoding is performed to determine the set of workpieces assigned to each machine during the supplier's production phase, the processing sequence of the workpieces on each machine, and the processing sequence of the workpieces on a single machine during the manufacturer's reprocessing phase.

[0012] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the multi-objective optimization method for equipment production scheduling based on multi-chain collaboration in the aforementioned first aspect is implemented.

[0013] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a program or instruction. When the program or instruction is executed by a processor, the multi-objective optimization method for equipment production scheduling based on multi-chain collaboration in the aforementioned first aspect is implemented.

[0014] This application provides a multi-objective optimization method and system for equipment production scheduling based on multi-chain collaboration. Compared with the prior art, it has the following beneficial effects:

[0015] This application takes into account the situation of multi-chain integrated production of equipment, that is, when the workpiece comes from two agents, the batch production chain and the operation and maintenance chain, the production process is optimized with multiple objectives in terms of manufacturing span and delay cost, and parallel machining methods and single-machine machining methods are comprehensively considered; this application sets input parameters according to the workpiece data, agent data, factory data of the supplier's production stage, and factory data of the manufacturer's reprocessing stage in the equipment production process, and solves the problem through a non-dominated sorting genetic algorithm based on neighborhood search and obtains the global optimal solution, which can effectively coordinate and optimize and configure the resources of the two stages of supplier processing and manufacturer packaging, thereby significantly improving the operational efficiency and integration capabilities of multi-chain collaborative production of high-end equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flowchart of a multi-objective optimization method for equipment production scheduling based on multi-chain collaboration provided in an embodiment of the present application;

[0018] Figure 2 It is a coding schematic diagram provided in an embodiment of the present application, which is produced by a supplier and reprocessed by a manufacturer;

[0019] Figure 3 is an exemplary flow chart of a non-dominated sorting genetic algorithm based on variable neighborhood search provided in an embodiment of the present application;

[0020] Figure 4 It is a structural schematic diagram of a multi-objective optimization system for equipment production scheduling based on multi-chain collaboration provided in an embodiment of the present application;

[0021] Figure 5 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] 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. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0024] The embodiments of the present application solve the technical problem of insufficient coordinated scheduling and optimization of batch production chain and operation and maintenance chain in equipment production by providing a multi-objective optimization method and system for equipment production scheduling based on multi-chain collaboration.

[0025] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows:

[0026] With the development of technology, the supply chain system of high-end equipment manufacturing industry has become increasingly complex. The entire production process is no longer completed by a single supply chain alone, but by multiple upstream and downstream enterprises working together. The batch production chain is responsible for batch production of research and development workpieces, and the operation and maintenance chain is responsible for repairing original parts and supplying spare parts. The production process of high-end equipment involves many raw materials, suppliers and manufacturers. The dual-agent scheduling strategy is introduced. The production demand of batch production parts and operation and maintenance parts corresponds to two different agents. The production processes of the batch production chain and the operation and maintenance chain are coordinated and the production capacity of the supply chain is integrated to ensure efficient and reliable production of the entire chain of high-end equipment.

[0027] The existing high-end equipment production scheduling optimization method schedules the production processes of the batch production and operation and maintenance supply chains separately, usually scheduling the production process for a single goal, paying less attention to the dual-agent collaboration in actual situations, and also paying less attention to production scheduling under multi-objective scenarios. As a result, it is impossible to accurately guide the production of batch production parts and operation and maintenance parts of high-end equipment, and it is impossible to minimize the losses caused by delayed production, thus causing insufficient resource utilization.

[0028] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0029] The following first introduces a multi-objective optimization method for equipment production scheduling based on multi-chain collaboration provided in an embodiment of the present application.

[0030] The present application embodiment provides a flowchart of a multi-objective optimization method for equipment production scheduling based on multi-chain collaboration, such as Figure 1 As shown, the multi-objective optimization method for equipment production scheduling may include the following steps S1-S3.

[0031] S1. According to the workpiece data, agent data, factory data of the supplier's production stage, and factory data of the manufacturer's reprocessing stage in the equipment production process, the input parameters of the non-dominated sorting genetic algorithm based on variable neighborhood search are set; wherein the agent data corresponds to the batch production chain agent A and the operation and maintenance chain agent B;

[0032] S2. Obtain the global optimal solution θ based on the input parameters and the non-dominated sorting genetic algorithm based on variable neighborhood search best ;

[0033] S3, for the global optimal solution θ best Decoding is performed to determine the set of workpieces assigned to each machine during the supplier's production phase, the processing sequence of the workpieces on each machine, and the processing sequence of the workpieces on a single machine during the manufacturer's reprocessing phase.

[0034] The above is a specific implementation method of a multi-objective optimization method for equipment production scheduling based on multi-chain collaboration provided in an embodiment of the present application. It can be understood that the present application takes into account the situation of multi-chain integrated production of equipment, that is, when the workpiece comes from two agents, the batch production chain and the operation and maintenance chain, the production process is multi-objective optimized in terms of manufacturing span and delay cost, and parallel machining methods and single-machine machining methods are comprehensively considered; the present application sets input parameters according to the workpiece data, agent data, factory data of the supplier's production stage, and factory data of the manufacturer's reprocessing stage in the equipment production process, and solves the problem through a non-dominated sorting genetic algorithm based on neighborhood search and obtains the global optimal solution, which can effectively coordinate and optimize and configure the resources of the two stages of supplier processing and manufacturer packaging, thereby significantly improving the operational efficiency and integration capabilities of multi-chain collaborative production of high-end equipment.

[0035] In some embodiments, the dual-agent workpiece is produced by multiple machines in the supplier production stage and reprocessed by a single machine in the manufacturer reprocessing stage; the input parameters include: the workpiece set of the batch chain agent, the workpiece set of the operation and maintenance chain agent, the set of all workpieces of the dual agent, the machine set in the supplier production stage, the processing time of the workpiece in the supplier production stage; the processing time of the workpiece in the manufacturer packaging stage, the workpiece delivery period of the batch chain agent, the workpiece weight of the batch chain agent, and the upper limit of the manufacturing span of the operation and maintenance chain agent.

[0036] In the embodiments of the present application, it is understandable that workpiece data, agent data, factory data of the supplier's production stage, and factory data of the manufacturer's reprocessing stage in the production process of high-end equipment can be obtained through historical data analysis, manual entry, etc., and the input parameters of the variable neighborhood search algorithm and the non-dominated sorting genetic algorithm are set based on the obtained data.

[0037] Specifically, in the input parameters, the batch chain agent's artifact collection in, Represents the artifact h in the batch production chain agent A; the artifact collection of the operation and maintenance chain agent in Represents artifact l in the operation and maintenance chain agent B; the set of all artifacts of the dual agent Among them J k represents the workpiece k in the batch production chain agent A or the operation and maintenance chain agent B; the set of machines in the supplier production stage {M1, ..., M g , ..., M m}, where M g represents the machine g in the supplier's production plant, each machine has the same function; the supplier's production stage artifact J k Processing time a k , a k Indicates workpiece J k Processing time; manufacturer packaging stage workpiece J k Processing time b k , b k Indicates workpiece J k Packaging time; Workpiece delivery time of batch production chain agent represents the duration constraint of workpiece h of agent A; the workpiece weight of the batch chain agent represents the penalty coefficient for delayed completion of agent A's workpiece h; the upper bound Q of the manufacturing span of the operation and maintenance chain agent, Q represents the completion time constraint of agent B's workpiece.

[0038] In some embodiments, please refer to Figure 3 , the aforementioned S2 step may include the following process:

[0039] S21, setting the execution parameters of the non-dominated sorting genetic algorithm based on variable neighborhood search, the execution parameters include: the first maximum number of iterations Gen1, and setting the initial gen1 = 1, the population size N, the crossover probability P c , the second maximum number of iterations of local search Gen2, and the neighborhood set Each neighborhood structure NS l The initial selection probability Pl = 1 / l max ;

[0040] S22. Generate an initial population according to the input parameters, decode each solution in the initial population to obtain the manufacturing span and delay cost of batch chain agent A, calculate the non-dominated sorting and congestion value of the individuals, and record the current optimal solution as the global optimal solution θ best ;

[0041] The individuals in the initial population are composed of SG codes and MS codes; SG codes are used to represent the order of assignment of workpieces and machines in the supplier's production stage, and MS codes are used to represent the order of workpiece processing in the manufacturer's packaging stage. SG codes are real numbers δ k It includes an integer part and a decimal part. The integer part indicates the machine number to which the workpiece is assigned. The decimal part is a two-digit decimal that is different from each other. The larger the decimal part, the earlier the production sequence on the same machine. MS codes are different integers. Indicates the sorting position of the artifact package.

[0042] S23, using a crossover operator to perform a crossover operation on the initial population X to generate a new population X1, and merging the initial population X and the new population X1 to generate a target population; wherein the number of the new population X1 is N, and the number of the target population is 2N;

[0043] S24, based on the non-dominated sorting and crowding value of each individual in the target population, an elite strategy is adopted to perform a selection operation to obtain a new population with the same population size;

[0044] S25. Decode the individuals in the current population to obtain the manufacturing span and delay cost, calculate the non-dominated order and crowding value of each individual, mark the individual with the largest crowding except the first and last non-dominated frontier as θ, and select a neighborhood structure NS from the preset neighborhood set based on the roulette method. l , generate a neighborhood solution θ′ according to the selected neighborhood structure;

[0045] S26. Use neighborhood structure NS l Perform local search on the neighborhood solution θ′ to obtain the local optimal solution θ″, compare the congestion degree of θ and θ″, if θ″ is better than θ, set θ=θ″, and record the number of successful searches for the local optimal solution under the neighborhood structure, otherwise record the number of failed searches for the local optimal solution under the neighborhood structure, and update the selection probability of the neighborhood structure;

[0046] S27, compare the optimal solution θ in the current neighborhood with the worst solution in the previous generation population. If the optimal solution θ is better than the worst solution in the previous generation population, replace the worst solution with the optimal solution θ, and compare the optimal solution θ in the current neighborhood with the global optimal solution θ. best Compare, if θ is better than θ best , then let θ best =θ;

[0047] S28, determine whether gen1≤Gen1 is true, if true, set gen1=gen1+1, and use the updated population as the input solution for the next iteration and return to step S22, otherwise output the global optimal solution θ best .

[0048] In the embodiments of the present application, it can be understood that the present application designs a hybrid non-dominated sorting genetic algorithm based on neighborhood search, combining the global optimization ability of the non-dominated sorting genetic algorithm and the local search advantage of the variable neighborhood search algorithm. An adaptive selection mechanism is designed in the two algorithms, and a special crossover operator and a local search neighborhood structure are designed, thereby effectively improving the solution efficiency and quality of the algorithm.

[0049] In some embodiments, in step S22, the present application encodes the workpiece and machine data of the supplier's production stage and the workpiece and machine data of the manufacturer's reprocessing stage based on a non-dominated genetic algorithm and a variable neighborhood search hybrid algorithm to obtain an initial population X0 consisting of N initial solutions, each solution in X0 is composed of a combination of two sequences, SG and MS, wherein the SG code represents the assignment order of the workpiece and the machine in the supplier's production stage, and the MS code represents the processing order of the workpiece in the manufacturer's reprocessing stage; the initial population is decoded to minimize the maximum manufacturing span of the batch production chain agent A. and delay cost f A For the target, perform non-dominated sorting on the individuals in the population and calculate the crowding value C r ; Take the best individual as the global optimal solution θ under the current conditions best , set the initial iteration number of the hybrid intelligent optimization algorithm iter1 = 1, and set the initial iteration number of the variable neighborhood search iter2 = 1.

[0050] Understandably, please refer to Figure 2 , the solution X1 is represented by a one-dimensional real number array SG, and the sequence length is N A +N B SG includes an integer part and a decimal part. The integer part represents the machine number to which the workpiece is assigned. The decimal part is a two-digit decimal that is different from each other. The larger the decimal part, the earlier the production sequence on the same machine. SG represents the assignment sequence of workpieces and machines during the supplier's production stage. Solution X2 is represented by a one-dimensional integer array MS, with a sequence length of N. A +N B ; MS is a different integer, indicating the sorting position of the workpiece packaging; MS indicates the order of reprocessing of the workpiece in the manufacturer's packaging stage.

[0051] The decoding process is to calculate the manufacturing span and delay cost of the batch production chain agent of the entire supplier production and manufacturer reprocessing process according to the actual scenario. Based on the characteristics of the two key stages of supplier and manufacturer, solution X is composed of solution X1 of the supplier production stage and solution X2 of the manufacturer reprocessing stage. The decoding process in the above step S22 includes:

[0052] S221, Generate workpiece J based on SG coding k Assigned machine location δ k and processing time a k , based on the preset first strategy to calculate the workpiece Completion time at the supplier's production stage;

[0053] Exemplarily, the first strategy satisfies the expression:

[0054]

[0055] in, Representation of workpiece Assigned to machine g for processing;

[0056]

[0057] S222. Generate artifact J based on SG coding k Assigned machine location δ k and processing time a k , based on the preset second strategy to calculate the workpiece Completion time at the supplier's production stage;

[0058] Exemplarily, the second strategy satisfies the expression:

[0059]

[0060] in Representation of workpiece Assigned to machine g for processing,

[0061]

[0062] S223. Generate workpiece J based on MS coding k Assigned reprocessing location and packaging time b k , calculate the total manufacturing span of batch chain agent A;

[0063] The total manufacturing span calculation of batch chain agent A satisfies the expression: In the formula, Representation of workpiece Completion time at the manufacturer stage, Representation of workpiece The start time of processing at the manufacturer stage, b h Representation of workpiece The processing time, Representation of workpiece Completion time at the supplier stage;

[0064] S224, according to SG code, MS code, workpiece Expected delivery date and the preset penalty coefficient for delay Calculate the total delay cost of batch production chain agent A;

[0065] The total delay cost calculation of batch chain agent A satisfies the expression:

[0066]

[0067] S225. Generate artifact J based on MS coding k Assigned reprocessing location and packaging time b k , calculate the total manufacturing span of operation and maintenance chain agent B;

[0068] The total manufacturing span calculation of the operation and maintenance chain agent B satisfies the expression:

[0069] in, Representation of workpiece Completion time at the manufacturer stage, Representation of workpiece Start-up time at the manufacturer stage, b l express The processing time, Representation of workpiece Completion time at the supplier stage.

[0070] In some embodiments, step S23 may include the following process:

[0071] S231. Select individual X from the initial population X s and individual X t ;

[0072] S232, in the interval Randomly generate two integers Cut1 and Cut2 as intersection points, where New individual X′ s Copy individual X t The SG code is located between Cut1 and Cut2, and the remaining codes retain individual X sThe encoding in; the new individual X′ t Copy individual X s The SG code is located between Cut1 and Cut2, and the remaining codes retain individual X t The encoding in;

[0073] S233, in the interval Randomly generate two integers Cut1 and Cut2 as intersection points, where New individual X′ s Copy individual X t The MS code is located between Cut1 and Cut2, and the remaining codes retain individual X s The encoding in; the new individual X′ t Copy individual X s The MS code is located between Cut1 and Cut2, and the remaining codes retain individual X t The encoding in;

[0074] S234, determine whether the number of crossover times d is greater than N / 2, if so, terminate the crossover operation; otherwise, go to S231.

[0075] In some embodiments, step S24 may include the following process:

[0076] S241, by decoding, the maximum completion time and total delay cost of the workpiece in the batch production chain agent A of each individual in the population are obtained, respectively recorded as and f2(x) = DC;

[0077] S242. Define the non-dominated sorting rule: randomly select individual x from a population of 2N s and individual x t , compare the manufacturing span and delay cost of the operation and maintenance chain agent. If f1(x s )≤f1(x t ), f2(x s )≤f2(x t ), and f1(x s ) <f1(x t ) and f2(x s ) <f2(x t ) at least one of them holds true, then the individual x s Dominant individual x t , and set s=1;

[0078] S243. For all t=1, 2, ..., N and t≠s, compare the individual x according to the definition. s and individual x t The non-dominance relationship; if there is no individual x t Better than individual xs , then the individual x s Record it as a non-dominated individual and add it to the set F1;

[0079] S244, let s=s+1, go to S243, until all non-dominated individuals are found, go to S245;

[0080] S245, calculate the difference between the population and the set F1 and use it as the new set X′, go to S243, repeatedly perform non-dominated selection on the individuals in the set X′, until all individuals in the population are classified according to the non-dominated relationship, and go to S246;

[0081] S246. Calculate the crowding degree C for individuals in the same non-dominated level r , the formula includes:

[0082]

[0083] Among them, C r [s] represents individual x s The degree of congestion;

[0084] x max Represents individual x s The maximum value under the objective function;

[0085] x min Represents individual x s The minimum value under the objective function;

[0086] S247. Select levels in the order of non-dominated levels from low to high. In the same level, select individuals as the next generation individuals in the order of crowding values ​​from high to low until 2N population individuals are selected.

[0087] In the embodiments of the present application, it can be understood that the present application performs multi-objective optimization of the manufacturing span and delay cost of the production process of high-end equipment while considering that the workpieces come from two agents, the batch production chain and the operation and maintenance chain. By solving the problem through a hybrid intelligent algorithm and obtaining an approximate optimal solution, it can effectively coordinate and optimize and configure the resources of the two stages of supplier processing and manufacturer packaging in multi-chain integrated production, thereby significantly improving the operational efficiency and integration capabilities of multi-chain collaborative production of high-end equipment.

[0088] In some embodiments, step S25 may include the following process:

[0089] S251. Decode the individuals in the current population to obtain the total manufacturing span of the batch production chain agent. and weighted delay cost f A , and the best individual is recorded as θ. The specific decoding process refers to the above S22;

[0090] S252. Select a neighborhood structure NS in a roulette wheel-based manner l , and generate a neighborhood solution θ′ of θ according to the neighborhood structure NS l ; specifically including:

[0091] Transform the feasible solution θ in the selected neighborhood structure NS l so that the initial solution at this stage jumps to another solution in the feasible region, thereby avoiding repeated iteration under the same solution and preventing the algorithm from falling into local optimality. Considering the complexity of the production and manufacturing packaging of high-end equipment suppliers and the requirements of constraint relationships, 6 neighborhood structures are designed, among which N1, N2, and N3 perform random mutation, and N4, N5, and N6 implement directional search.

[0092] In some embodiments, the preset neighborhood set includes a first neighborhood group for random mutation and a second neighborhood group for directional search; the first neighborhood group includes a first neighborhood structure, a second neighborhood structure, and a third neighborhood structure; the second neighborhood group includes a fourth neighborhood structure, a fifth neighborhood structure, and a sixth neighborhood structure;

[0093] In the first neighborhood structure, define variables a and b, and randomly generate two integers in the interval , assign them to a and b, where a < b, and reverse the encoding between positions a and b in the sequence SG; randomly generate two integers in the interval , assign them to a and b, where a < b, and reverse the encoding between positions a and b in the sequence MS;

[0094] In the second neighborhood structure, define variables a and b, and randomly generate two integers in the interval , assign them to a and b, where a < b, and swap the encoding to the left of position a and to the right of position b in the sequence SG; randomly generate two integers in the interval , assign them to a and b, where a < b, and swap the encoding to the left of position a and to the right of position b in the sequence MS;

[0095] In the third neighborhood structure, randomly generate two integers in the interval , assign them to a and b, where a < b, and swap the encoding at positions a and b in the sequence SG; randomly generate two integers in the interval , assign them to a and b, where a < b, and swap the encoding at positions a and b in the sequence MS;

[0096] In the fourth neighborhood structure, find the last completed proxy B workpiece in the supplier stage, and randomly select one workpiece to swap the encoding at the position of this workpiece in the sequences SG and MS;

[0097] In the fifth neighborhood structure, find the last completed agent B workpiece in the manufacturer stage, and randomly select a workpiece to exchange with the code of the workpiece position in sequence SG and sequence MS;

[0098] In the sixth neighborhood structure, the agent A workpieces with the largest and smallest delay penalty coefficients are found, and the codes of the positions of the workpieces with the largest delay penalty coefficient and the workpieces with the smallest delay penalty coefficient in the sequences SG and MS are exchanged.

[0099] In some embodiments, step S26 may include the following process:

[0100] S261, for the neighborhood structure NS l , in the operation of performing variable neighborhood local search, record the number of successful searches for the local optimal solution in each neighborhood structure S l and the number of failures F l , update the neighborhood selection probability according to the number of successful searches, the formula is as follows: in ε is a sufficiently small positive number, SUC l Represents the neighborhood structure NS l The local search success rate;

[0101] S262, define variable O as a neighborhood selection variable, where O is a number randomly generated in the interval (0,1), and determine the range of O. If it satisfies o l-1 <O<o l , then select the neighborhood structure NS l Performs a neighborhood search operation.

[0102] In the embodiments of the present application, it can be understood that the present application designs a hybrid non-dominated sorting genetic algorithm based on neighborhood search, combining the global optimization ability of the non-dominated sorting genetic algorithm and the local search advantage of the variable neighborhood search algorithm. An adaptive selection mechanism is designed in the two algorithms, and a special crossover operator and a local search neighborhood structure are designed, thereby effectively improving the solution efficiency and quality of the algorithm.

[0103] In some embodiments, the present application provides a multi-objective optimization system 400 for equipment production scheduling based on multi-chain collaboration, such as Figure 4 As shown, the equipment production scheduling multi-objective optimization system 400 may include the following modules:

[0104] The parameter setting module 410 is used to set the input parameters of the non-dominated sorting genetic algorithm based on variable neighborhood search according to the workpiece data, the agent data, the factory data of the supplier's production stage, and the factory data of the manufacturer's reprocessing stage in the equipment production process; wherein the agent data corresponds to the batch production chain agent A and the operation and maintenance chain agent B;

[0105] The acquisition module 420 is used to obtain the global optimal solution θ according to the input parameters and the non-dominated sorting genetic algorithm based on variable neighborhood search. best ;

[0106] Decoding module 430 is used to decode the global optimal solution θ best Decoding is performed to determine the set of workpieces assigned to each machine during the supplier's production phase, the processing sequence of the workpieces on each machine, and the processing sequence of the workpieces on a single machine during the manufacturer's reprocessing phase.

[0107] According to an embodiment of the present application, any multiple modules in the parameter setting module 410, the acquisition module 420 and the decoding module 430 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module.

[0108] In some embodiments, the acquisition module 420 may be specifically used to:

[0109] S21, setting the execution parameters of the non-dominated sorting genetic algorithm based on variable neighborhood search, the execution parameters include: the first maximum number of iterations Gen1, and setting the initial gen1 = 1, the population size N, the crossover probability P c , the second maximum number of iterations of the local search Gen2, and the neighborhood set Each neighborhood structure NS l The initial selection probability P l =1 / l max ;

[0110] S22. Generate an initial population according to the input parameters, decode each solution in the initial population to obtain the manufacturing span and delay cost of batch chain agent A, calculate the non-dominated sorting and congestion value of the individuals, and record the current optimal solution as the global optimal solution θ best ; The individuals in the initial population are composed of SG codes and MS codes; SG codes are used to represent the order of assignment of workpieces and machines in the supplier's production stage, and MS codes are used to represent the order of reprocessing of workpieces in the manufacturer's packaging stage;

[0111] S23, using a crossover operator to perform a crossover operation on the initial population X to generate a new population X1, and merging the initial population X and the new population X1 to generate a target population; wherein the number of the new population X1 is N, and the number of the target population is 2N;

[0112] S24, based on the non-dominated sorting and crowding value of each individual in the target population, an elite strategy is adopted to perform a selection operation to obtain a new population with the same population size;

[0113] S25. Decode the individuals in the current population to obtain the manufacturing span and delay cost, calculate the non-dominated order and crowding value of each individual, mark the individual with the largest crowding except the first and last non-dominated frontier as θ, and select a neighborhood structure NS from the preset neighborhood set based on the roulette method. l , generate a neighborhood solution θ′ according to the selected neighborhood structure;

[0114] S26. Use neighborhood structure NS l Perform local search on the neighborhood solution θ′ to obtain the local optimal solution θ″, compare the congestion of θ and θ″, if θ″ is better than θ, set θ=θ″, and record the number of successful searches for the local optimal solution under the neighborhood structure, otherwise record the number of failed searches for the local optimal solution under the neighborhood structure, and update the selection probability of the neighborhood structure;

[0115] S27, compare the optimal solution θ in the current neighborhood with the worst solution in the previous generation population. If the optimal solution θ is better than the worst solution in the previous generation population, replace the worst solution with the optimal solution θ, and compare the optimal solution θ in the current neighborhood with the global optimal solution θ. best Compare, if θ is better than θ best , then let θ best =θ;

[0116] S28, determine whether gen1≤Gen1 is true, if true, set gen1=gen1+1, and use the updated population as the input solution for the next iteration and return to step S22, otherwise output the global optimal solution θ best .

[0117] Figure 4 Each module in the system shown has the function of implementing each step in the aforementioned multi-objective optimization method for equipment production scheduling based on multi-chain collaboration, and can achieve its corresponding technical effect. For the sake of concise description, it will not be repeated here.

[0118] In some embodiments, the present application provides an electronic device, the structure diagram of the electronic device is as follows Figure 5 shown.

[0119] The electronic device may include a processor 510 and a memory 520 storing computer program instructions.

[0120] Specifically, the processor 510 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0121] The memory 520 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 520 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 520 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 520 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 520 is a non-volatile solid-state memory.

[0122] The memory 520 may include a read-only memory (ROM), a random access memory (RAM), a disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Therefore, generally, the memory 520 includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer executable instructions, and when the software is executed (e.g., by one or more processors), it can perform the operations described in any of the multi-objective optimization methods for equipment production scheduling based on multi-chain collaboration in the above-mentioned embodiments.

[0123] The processor 510 reads and executes computer program instructions stored in the memory 520 to implement any one of the multi-objective optimization methods for equipment production scheduling based on multi-chain collaboration in the above embodiments.

[0124] In one example, the electronic device may further include a communication interface 530 and a bus 500. Figure 5 As shown, the processor 510, the memory 520, and the communication interface 530 are connected via a bus 500 and communicate with each other.

[0125] The communication interface 530 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0126] Bus 500 includes hardware, software or both, and the parts of online data flow billing equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-end bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 500 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0127] In addition, in combination with the multi-objective optimization method for equipment production scheduling based on multi-chain collaboration in the above embodiments, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any of the multi-objective optimization methods for equipment production scheduling based on multi-chain collaboration in the above embodiments is implemented.

[0128] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0129] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0130] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0131] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0132] In summary, compared with the prior art, this application has the following beneficial effects:

[0133] 1. This application considers the situation of multi-chain integrated production of equipment, that is, when the workpiece comes from two agents, the batch production chain and the operation and maintenance chain, the production process is optimized with multiple objectives of manufacturing span and delay cost, and parallel machining method and single machine processing method are comprehensively considered; this application sets input parameters according to the workpiece data, agent data, factory data of the supplier's production stage, and factory data of the manufacturer's reprocessing stage in the equipment production process, and solves the problem through a non-dominated sorting genetic algorithm based on neighborhood search and obtains the global optimal solution, which can effectively coordinate and optimize and configure the resources of the two stages of supplier processing and manufacturer packaging, thereby significantly improving the operational efficiency and integration capabilities of multi-chain collaborative production of high-end equipment.

[0134] 2. This application designs a hybrid non-dominated sorting genetic algorithm based on neighborhood search, combining the global optimization ability of the non-dominated sorting genetic algorithm with the local search advantage of the variable neighborhood search algorithm. An adaptive selection mechanism is designed in both algorithms, and a special crossover operator and local search neighborhood structure are designed, thereby effectively improving the algorithm's solution efficiency and quality.

[0135] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-objective optimization method for equipment production scheduling based on multi-chain collaboration, characterized in that: include: S1. According to the workpiece data, agent data, factory data of the supplier's production stage, and factory data of the manufacturer's reprocessing stage in the equipment production process, the input parameters of the non-dominated sorting genetic algorithm based on variable neighborhood search are set; wherein the agent data corresponds to the batch production chain agent A and the operation and maintenance chain agent B; S2, according to the input parameters and the non-dominated sorting genetic algorithm based on variable neighborhood search, obtain the global optimal solution θ best ; S3, the global optimal solution θ best Decoding is performed to determine the set of workpieces assigned to each machine in the supplier's production stage, the processing sequence of the workpieces on each machine, and the processing sequence of the workpieces on a single machine in the manufacturer's reprocessing stage.

2. The multi-objective optimization method for equipment production scheduling based on multi-chain collaboration according to claim 1 is characterized in that: The dual-agent workpiece is produced by multiple machines in the supplier production stage and reprocessed by a single machine in the manufacturer reprocessing stage; The input parameters include: the workpiece set of the batch production chain agent, the workpiece set of the operation and maintenance chain agent, the set of all workpieces of the dual agent, the machine set in the supplier's production stage, the processing time of the workpiece in the supplier's production stage; the reprocessing time of the workpiece in the manufacturer's packaging stage, the workpiece delivery period of the batch production chain agent, the workpiece weight of the batch production chain agent, and the upper limit of the manufacturing span of the operation and maintenance chain agent.

3. The multi-objective optimization method for equipment production scheduling based on multi-chain collaboration according to claim 1 is characterized in that: The S2 step includes: S21, setting execution parameters of the non-dominated sorting genetic algorithm based on variable neighborhood search, the execution parameters include: the first maximum number of iterations Gen1, and setting the initial gen1 = 1, the population size N, the crossover probability P c , the second maximum number of iterations of the local search Gen2, and the neighborhood set Each neighborhood structure NS l The initial selection probability P l =1 / l max ; S22, generating an initial population according to the input parameters, decoding each solution in the initial population to obtain the manufacturing span and delay cost of the batch production chain agent A, calculating the non-dominated sorting and congestion value of the individuals, and recording the current optimal solution as the global optimal solution θ best ; The individuals in the initial population are composed of SG codes and MS codes; the SG code is used to characterize the assignment order of workpieces and machines in the supplier's production stage, and the MS code is used to characterize the order of workpiece reprocessing in the manufacturer's packaging stage; S23, using a crossover operator to perform a crossover operation on the initial population X to generate a new population X1, and merging the initial population X and the new population X1 to generate a target population; wherein the number of the new population X1 is N, and the number of the target population is 2N; S24. In the target population, all individuals are sorted non-dominatedly and the crowding values ​​are calculated. The selection operation is performed through the elite strategy to obtain a new population with the same population size. S25. Decode the individuals in the current population to obtain the manufacturing span and delay cost, calculate the non-dominated order and crowding value of each individual, mark the individual with the largest crowding except the first and last non-dominated frontier as θ, and select a neighborhood structure NS from the preset neighborhood set based on the roulette method. l , generate a neighborhood solution θ′ according to the selected neighborhood structure; S26. Use neighborhood structure NS l Perform a local search on the neighborhood solution θ′ to obtain a local optimal solution θ″, compare the congestion of θ and θ″, if θ″ is better than θ, set θ=θ″, and record the number of successful searches for the local optimal solution under the neighborhood structure, otherwise record the number of failed searches for the local optimal solution under the neighborhood structure, and update the selection probability of the neighborhood structure; S27, compare the optimal solution θ in the current neighborhood with the worst individual in the previous generation population. If the optimal solution θ is better than the worst solution in the previous generation population, replace the worst solution with the optimal solution θ, and compare the optimal solution θ in the current neighborhood with the global optimal solution θ. best Compare, if θ is better than θ best , then let θ best =θ; S28, determine whether gen1≤Gen1 is true, if true, set gen1=gen1+1, and use the updated population as the input solution for the next iteration and return to step S22, otherwise output the global optimal solution θ best .

4. The multi-objective optimization method for equipment production scheduling based on multi-chain collaboration according to claim 3 is characterized in that: The decoding process in step S22 includes: S221, generate workpiece J based on the SG code k Assigned machine location δ k and processing time a k , based on the preset first strategy to calculate the workpiece Completion time at the supplier's production stage; S222, generate workpiece J based on the SG code k Assigned machine location δ k and processing time a k , based on the preset second strategy to calculate the workpiece Completion time at the supplier's production stage; S223, generate workpiece J based on the MS code k Assigned reprocessing location and packaging time b k , calculate the total manufacturing span of batch chain agent A; The total manufacturing span calculation of the batch production chain agent A satisfies the expression: In the formula, Representation of workpiece Completion time at the manufacturer stage, Representation of workpiece The start time of processing at the manufacturer stage, b h Representation of workpiece The processing time, Representation of workpiece Completion time at the supplier stage; S224, according to the SG code, the MS code, the workpiece Expected delivery date and the preset penalty coefficient for delay Calculate the total delay cost of the batch production chain agent A; The total delay cost calculation of the batch production chain agent A satisfies the expression: S225, generate workpiece J based on the MS code k Assigned reprocessing location and packaging time b k , calculate the total manufacturing span of the operation and maintenance chain agent B; The total manufacturing span calculation of the operation and maintenance chain agent B satisfies the expression: in, Representation of workpiece Completion time at the manufacturer stage, Representation of workpiece Start-of-processing time at the manufacturer stage, b l express The processing time, Representation of workpiece Completion time at the supplier stage.

5. The multi-objective optimization method for equipment production scheduling based on multi-chain collaboration according to claim 4 is characterized in that: The step S23 includes: S231, select individual X from the initial population X s and individual X t ; S232, in the interval Randomly generate two integers Cut1 and Cut2 as intersection points, where New Individual X s 'Copy individual X t The SG code is located between Cut1 and Cut2, and the remaining codes retain individual X s The code in; the new individual X t 'Copy individual X s The SG code is located between Cut1 and Cut2, and the remaining codes retain individual X t The encoding in; S233, in the interval Randomly generate two integers Cut1 and Cut2 as intersection points, where New Individual X s 'Copy individual X t The MS code is located between Cut1 and Cut2, and the remaining codes retain individual X s The code in; the new individual X t 'Copy individual X s The MS code is located between Cut1 and Cut2, and the remaining codes retain individual X t The encoding in; S234, determine whether the number of crossover times d is greater than N / 2, if so, terminate the crossover operation; otherwise, go to S231.

6. The multi-objective optimization method for equipment production scheduling based on multi-chain collaboration according to claim 4 is characterized in that: The step S24 includes: S241, by decoding, the maximum completion time and total delay cost of the workpiece in the batch production chain agent A of each individual in the population are obtained, respectively recorded as and f2(x) = DC; S242. Define the non-dominated sorting rule: randomly select individual x from a population of 2N s and individual x t , compare the manufacturing span and delay cost of the operation and maintenance chain agent. If f1(x s )≤f1(x t ), f2(x s )≤f2(x t ), and f1(x s ) <f1(x t ) and f2(x s ) <f2(x t ) at least one of them holds true, then the individual x s Dominant individual x t , and set s=1; S243. For all t = 1, 2, ..., N and t ≠ s, compare the individual x according to the definition. s and individual x t The non-dominance relationship; if there is no individual x t Better than individual x s , then the individual x s Mark it as a non-dominated individual and add it to the set F1; S244, let s=s+1, go to S243, until all non-dominated individuals are found, go to S245; S245, calculate the difference between the population and the set F1 and use it as the new set X′, go to S243, repeat the non-dominated selection for the individuals in the set X′, until all individuals in the population are classified according to the non-dominated relationship, go to S246; S246. Calculate the crowding degree C for individuals in the same non-dominated level r , the formula includes: Among them, C r [s] represents individual x s The degree of congestion; x max Represents individual x s The maximum value under the objective function; x min Represents individual x s The minimum value under the objective function; S247. Select levels in the order of non-dominated levels from low to high. In the same level, select individuals as members of the next generation population in the order of crowding values ​​from high to low, until 2N population individuals are selected.

7. The multi-objective optimization method for equipment production scheduling based on multi-chain collaboration according to claim 3 is characterized in that: The step S26 includes: S261, for the neighborhood structure NS l , in the operation of performing variable neighborhood local search, record the number of successful searches for the local optimal solution in each neighborhood structure S l and the number of failures F l , update the neighborhood selection probability according to the number of successful searches, the formula is as follows: in ε is a sufficiently small positive number, SUC l Represents the neighborhood structure NS l The local search success rate; S262, define variable O as a neighborhood selection variable, where O is a number randomly generated in the interval (0,1), and determine the range of O. If it satisfies o l-1 <O<o l , then select the neighborhood structure NS l Performs a neighborhood search operation.

8. A multi-objective optimization system for equipment production scheduling based on multi-chain collaboration, characterized in that: include: A parameter setting module is used to set the input parameters of the non-dominated sorting genetic algorithm based on variable neighborhood search according to the workpiece data, agent data, the factory data of the supplier's production stage, and the factory data of the manufacturer's reprocessing stage in the equipment production process; wherein the agent data corresponds to the batch production chain agent A and the operation and maintenance chain agent B; The acquisition module is used to obtain the global optimal solution θ according to the input parameters and the non-dominated sorting genetic algorithm based on variable neighborhood search. best ; A decoding module is used to decode the global optimal solution θ best Decoding is performed to determine the set of workpieces assigned to each machine in the supplier's production stage, the processing sequence of the workpieces on each machine, and the processing sequence of the workpieces on a single machine in the manufacturer's reprocessing stage.

9. An electronic device, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the multi-objective optimization method for equipment production scheduling based on multi-chain collaboration as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores programs or instructions, and when the programs or instructions are executed by the processor, the multi-objective optimization method for equipment production scheduling based on multi-chain collaboration as described in any one of claims 1 to 7 is implemented.

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