Multi-chain coordination-based equipment production scheduling multi-objective optimization method and system

Abstract

This application provides a multi-objective optimization method and system for equipment production scheduling based on multi-chain collaboration, relating to the field of production scheduling. The method includes: setting input parameters based on workpiece data, proxy data, factory data from the supplier's production stage, and factory data from the manufacturer's reprocessing stage; obtaining a globally optimal solution based on the input parameters and a non-dominated sorting genetic algorithm based on variable neighborhood search; and decoding the globally optimal solution to determine the set of workpieces allocated to each machine during the supplier's production stage, the processing order of the workpieces on each machine, and the processing order of the workpieces on a single machine during the manufacturer's reprocessing stage. This application can effectively optimize and allocate resources in both the supplier's processing and the manufacturer's packaging stages, thereby significantly improving the operational efficiency and integration capabilities of multi-chain collaborative equipment production.

Description

Technical Field

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

[0002] With technological advancements, the supply chain system of high-end equipment manufacturing is becoming increasingly complex. The entire production process is no longer completed by a single supply chain, but rather through collaboration among multiple upstream and downstream enterprises. The batch production chain is responsible for mass-producing and finalizing finished products, while the maintenance chain is responsible for repairing original components and supplying spare parts. The production process of high-end equipment involves numerous raw materials, suppliers, and manufacturers. By introducing a dual-agent scheduling strategy, the production needs of batch-produced parts and maintenance parts correspond to two different agents. This collaborative scheduling of the production processes of both the batch production and maintenance chains, and by integrating the production capacity of the entire supply chain, ensures efficient and reliable production of high-end equipment across the entire chain.

[0003] Existing high-end equipment production scheduling optimization methods separate the production processes of the batch production and operation and maintenance supply chains for scheduling. They typically schedule the production process for a single objective, paying little attention to the actual dual-agent collaboration and the production scheduling in multi-objective scenarios. This results in an inability to accurately guide the production of high-end equipment batch parts and operation and maintenance parts, and an inability to minimize the losses caused by production delays, thus leading to insufficient resource utilization. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this 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 between batch production chain and operation and maintenance chain in equipment production.

[0005] To achieve the above objectives, this application provides the following technical solution:

[0006] In a first aspect, embodiments of this application provide a multi-objective optimization method for equipment production scheduling based on multi-chain collaboration, the multi-objective optimization method for equipment production scheduling including:

[0007] S1. Based on the workpiece data, proxy data, supplier production stage factory data, and manufacturer reprocessing stage factory data during the equipment production process, set the input parameters for the non-dominated sorting genetic algorithm based on variable neighborhood search; where the proxy data corresponds to batch production chain proxy A and maintenance chain proxy B; S2. Based on the input parameters and the non-dominated sorting genetic algorithm based on variable neighborhood search, obtain the global optimal solution θ. best S3, for the global optimal solution θ bestDecoding is performed to determine the set of workpieces allocated 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] Secondly, embodiments of this application provide a multi-objective optimization system for equipment production scheduling based on multi-chain collaboration. This system 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, factory data of the supplier's production stage and the factory data of the manufacturer's reprocessing stage during the equipment production process; where the agent data corresponds to batch production chain agent A and operation and maintenance chain agent B.

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

[0011] The decoding module is used to decode the global optimal solution θ. best Decoding is performed to determine the set of workpieces allocated 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] Thirdly, embodiments of this application provide an electronic device, which includes: a processor, a memory, and a program stored in the memory and executable on the processor. When the program is executed by the processor, it implements the multi-objective optimization method for equipment production scheduling based on multi-chain collaboration described in the first aspect above.

[0013] Fourthly, embodiments of this application provide a computer-readable storage medium storing a program or instructions that, when executed by a processor, implement the multi-objective optimization method for equipment production scheduling based on multi-chain collaboration described in the first aspect.

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

[0015] This application considers the scenario of multi-chain integrated production of equipment, specifically when workpieces originate from two agents: the batch production chain and the operation and maintenance chain. It optimizes the production process across multiple objectives, including manufacturing span and delay costs, while comprehensively considering both parallel machining and single-machine machining methods. Based on workpiece data, agent data, factory data from the supplier's production stage, and factory data from the manufacturer's reprocessing stage, this application sets input parameters and solves the problem using a non-dominated sorting genetic algorithm based on neighborhood search to obtain the global optimal solution. This effectively optimizes and allocates resources in both the supplier's processing and the manufacturer's packaging stages, thereby significantly improving the operational efficiency and integration capabilities of multi-chain collaborative production of high-end equipment. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a multi-objective optimization method for equipment production scheduling based on multi-chain collaboration, provided in an embodiment of this application.

[0018] Figure 2 This is a coding diagram of supplier production and manufacturer reprocessing provided in an embodiment of this application;

[0019] Figure 3 This is an exemplary flowchart of a non-dominated sorting genetic algorithm based on variable neighborhood search provided in an embodiment of this application;

[0020] Figure 4 This is a schematic diagram of the structure of a multi-objective optimization system for equipment production scheduling based on multi-chain collaboration, provided in an embodiment of this application.

[0021] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0024] This 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 between batch production chain and operation and maintenance chain in equipment production.

[0025] The technical solution in this application is to solve the above-mentioned technical problems, and the general idea is as follows:

[0026] With technological advancements, the supply chain system of high-end equipment manufacturing is becoming increasingly complex. The entire production process is no longer completed by a single supply chain, but rather through collaboration among multiple upstream and downstream enterprises. The batch production chain is responsible for mass-producing and finalizing finished products, while the maintenance chain is responsible for repairing original components and supplying spare parts. The production process of high-end equipment involves numerous raw materials, suppliers, and manufacturers. By introducing a dual-agent scheduling strategy, the production needs of batch-produced parts and maintenance parts correspond to two different agents. This collaborative scheduling of the production processes of both the batch production and maintenance chains, and by integrating the production capacity of the entire supply chain, ensures efficient and reliable production of high-end equipment across the entire chain.

[0027] Existing high-end equipment production scheduling optimization methods separate the production processes of the batch production and operation and maintenance supply chains for scheduling. They typically schedule the production process for a single objective, paying little attention to the actual dual-agent collaboration and the production scheduling in multi-objective scenarios. This results in an inability to accurately guide the production of high-end equipment batch parts and operation and maintenance parts, and an inability to minimize the losses caused by production delays, thus leading to insufficient resource utilization.

[0028] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0029] The following section first introduces a multi-objective optimization method for equipment production scheduling based on multi-chain collaboration, provided in the embodiments of this application.

[0030] This application provides a flowchart illustrating a multi-objective optimization method for equipment production scheduling based on multi-chain collaboration, as shown in the embodiments below. Figure 1 As shown, the multi-objective optimization method for equipment production scheduling may include the following steps S1-S3.

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

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

[0033] S3, for the global optimal solution θ best Decoding is performed to determine the set of workpieces allocated 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 of a multi-objective optimization method for equipment production scheduling based on multi-chain collaboration provided in this application. It can be understood that 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, and performs multi-objective optimization of the manufacturing span and delay cost of the production process, taking into account both parallel machining and single-machine machining methods. This application sets input parameters based on the workpiece data, agent data, factory data of the supplier's production stage and the factory data of the manufacturer's reprocessing stage in the equipment production process, and solves the problem and obtains the global optimal solution through a non-dominated sorting genetic algorithm based on neighborhood search. It can effectively coordinate and optimize the resources of the supplier's processing and the manufacturer's packaging stages, thereby significantly improving the operational efficiency and integration capability of multi-chain collaborative production of high-end equipment.

[0035] In some embodiments, the workpieces of the dual-agent are 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 agents, the set of machines in the supplier production stage, the processing time of the workpieces in the supplier production stage, the processing time of the workpieces in the manufacturer packaging stage, the delivery time of the workpieces of the batch production chain agent, the workpiece weight of the batch production chain agent, and the upper bound of the manufacturing span of the operation and maintenance chain agent.

[0036] In the embodiments of this application, it is understood that workpiece data, agent data, factory data of the supplier's production stage and factory data of the manufacturer's reprocessing stage in the high-end equipment production process can be obtained through historical data analysis, manual input and other methods. 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 set of workpieces of the batch production chain agent. in, The workpiece h represents the workpiece set in batch production chain agent A; the workpiece set in operation and maintenance chain agent. in Represents workpiece l in the operations and maintenance chain agent B; the set of all workpieces in the dual agent. J k Represents workpiece k in batch production chain agent A or operation and maintenance chain agent B; the set of machines {M1, ..., M} in the supplier's production stage. g M m}, where M g This refers to machine g in the supplier's production plant, where each machine has the same function; and workpiece J in the supplier's production stage. k Processing time a k a k Indicates workpiece J k Processing time; Manufacturer packaging stage of workpiece J k Processing time b k b k Indicates workpiece J k Packaging time; delivery time of workpieces for wholesale supply chain agents This represents the time constraint for proxy workpiece h of batch production chain; the workpiece weight of the proxy. The penalty coefficient for delayed completion of workpiece h by proxy A is represented; the upper bound of the manufacturing span Q of the operation and maintenance chain proxy is represented, where Q represents the completion time constraint of workpiece B by proxy.

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

[0039] S21. Set the execution parameters for the non-dominated sorting genetic algorithm based on variable neighborhood search. The execution parameters include: the first maximum number of iterations, Gen1, with an initial gen1 = 1, population size N, and crossover probability P. c The second maximum number of iterations for 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 based on the input parameters. Decode each solution in the initial population to obtain the manufacturing span and delay cost of batch production chain agent A. Calculate the non-dominated ranking and crowding value of individuals. Record the current optimal solution as the global optimal solution θ. best ;

[0041] The individuals in the initial population consist of SG codes and MS codes; the SG code represents the assignment order of workpieces and machines during the supplier's production stage, while the MS code represents the processing order of workpieces during the manufacturer's packaging stage. The SG code is a real number δ. k It includes an integer part and a decimal part. The integer part represents the machine number to which the workpiece is assigned, and the decimal part consists of two distinct decimal places. A larger decimal part indicates an earlier production order on the same machine. The MS code consists of distinct integers. This indicates the sorting position of the workpiece packaging.

[0042] S23. Use the crossover operator to perform a crossover operation on the initial population X to generate a new population X1. Merge the initial population X and the new population X1 to generate the target population; where 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 ranking 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 of the same size.

[0044] S25. Decode the individuals in the current population to obtain the manufacturing span and delay cost, calculate the non-dominated ranking and crowding value of each individual, and mark the individual with the largest crowding value (excluding the head and tail) at the first non-dominated frontier as θ. Select a neighborhood structure NS from the preset neighborhood set based on a roulette wheel method. l A neighborhood solution θ′ is generated based on the selected neighborhood structure;

[0045] S26, Using Neighborhood Structure NS l Perform a local search on the neighborhood solution θ′ to obtain the local optimum solution θ″. Compare the crowding of θ and θ″. If θ″ is better than θ, let θ = θ″. At the same time, record the number of successful searches for the local optimum solution under this neighborhood structure. Otherwise, record the number of failed searches for the local optimum solution under this 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. If the optimal solution θ is better than the worst solution in the previous generation, then 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 If the comparison is made, and θ is better than θ best Let θ best =θ;

[0047] S28. Determine if gen1 ≤ Gen1 holds true. If it does, let gen1 = gen1 + 1, and use the updated population as the input solution for the next iteration, then return to step S22. Otherwise, output the global optimal solution θ. best .

[0048] In the embodiments of this application, it is understood that a hybrid non-dominated sorting genetic algorithm based on neighborhood search is designed, combining the global optimization capability 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, along with a special crossover operator and a local search neighborhood structure, thereby effectively improving the solution efficiency and quality of the algorithm.

[0049] In some embodiments, in step S22, this application encodes the workpiece and machine data of the supplier's production stage and the manufacturer's reprocessing stage based on a hybrid algorithm of non-dominated genetic algorithm and variable neighborhood search, obtaining an initial population X0 consisting of N initial solutions. Each solution in X0 is composed of two sequences, SG and MS, where SG encoding represents the assignment order of workpieces and machines in the supplier's production stage, and MS encoding represents the processing order of workpieces in the manufacturer's reprocessing stage. The initial population is then decoded to minimize the maximum manufacturing span of the batch production chain agent A. and delay costs f A To determine the target population, perform a non-dominated ordination of individuals and calculate the crowding value C. r The best individual among them is taken as the global optimal solution θ under the current conditions. best Set the initial iteration count of the hybrid intelligent optimization algorithm to iter1 = 1, and set the initial iteration count of the variable neighborhood search to 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 consists of an integer part and a fractional part. The integer part represents the machine number to which the workpiece is assigned, and the fractional part consists of two distinct decimal places. A larger fractional part indicates an earlier production order on the same machine. SG represents the order in which workpieces and machines are assigned during the supplier's production phase. Solution X2 is represented by a one-dimensional integer array MS, with a sequence length of N. A +N B MS are distinct integers representing the sorting position of the workpiece packaging; MS indicates the order of workpiece reprocessing during the manufacturer's packaging stage.

[0051] The decoding process involves calculating the manufacturing span and delay costs of the entire supplier production and manufacturer reprocessing chain in the obtained feasible solution, based on the actual scenario. Based on the characteristics of the two key stages—the supplier and the manufacturer—solution X consists of solution X1 from the supplier production stage and solution X2 from the manufacturer reprocessing stage. The decoding process in step S22 mentioned above includes:

[0052] S221, Generating workpiece J based on SG encoding k The assigned machine location δ k and processing time a k Calculate the workpiece based on the preset first strategy Completion time during the supplier's production phase;

[0053] For example, the first strategy satisfies the expression:

[0054]

[0055] in, Indicates workpiece They were assigned to machine g for processing.

[0056]

[0057] S222, Generating workpiece J based on SG encoding k The assigned machine location δ k and processing time a k The workpiece is calculated based on the preset second strategy. Completion time during the supplier's production phase;

[0058] For example, the second strategy satisfies the expression:

[0059]

[0060] in Indicates workpiece They were assigned to machine g for processing.

[0061]

[0062] S223, Generating workpiece J based on MS encoding k Assigned reprocessing location and packaging time b k Calculate the total manufacturing span of batch production chain agent A;

[0063] The total manufacturing span of batch production chain agent A is calculated according to the expression: In the formula, Indicates workpiece Completion time during the manufacturing phase, Indicates workpiece At the start of processing during the manufacturing phase, b h Indicates workpiece Processing time, Indicates workpiece Completion time during the supplier phase;

[0064] S224, Based on SG code, MS code, and workpiece Preset delivery period and preset delay penalty coefficient Calculate the total delay cost for batch production chain agent A;

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

[0066]

[0067] S225, Generating workpiece J based on MS encoding k Assigned reprocessing location and packaging time b k Calculate the total manufacturing span of the operations and maintenance chain agent B;

[0068] The total manufacturing span of the operations and maintenance chain agent B is calculated according to the expression:

[0069] in, Indicates workpiece Completion time during the manufacturing phase, Indicates workpiece At the start of processing during the manufacturing phase, b l express Processing time, Indicates workpiece Completion time during the supplier phase.

[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 Two random integers, Cut1 and Cut2, are generated as the intersection point. New individual X′ s Copying individual X t The SG encoding is the encoding between Cut1 and Cut2, and the remaining encodings retain the individual X. sThe encoding in the new individual X′ t Copying individual X s The SG encoding is the encoding between Cut1 and Cut2, and the remaining encodings retain the individual X. t The encoding in;

[0073] S233, in the interval Two random integers, Cut1 and Cut2, are generated as the intersection point. New individual X′ s Copying individual X t The MS encoding is located between Cut1 and Cut2, while the remaining encodings preserve the individual X. s The encoding in the new individual X′ t Copying individual X s The MS encoding is located between Cut1 and Cut2, while the remaining encodings preserve the individual X. t The encoding in;

[0074] S234. Determine if the number of crossovers d is greater than N / 2. If yes, terminate the crossover operation; otherwise, go to S231.

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

[0076] S241. 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 by decoding, and are denoted as follows: and f2(x) = DC;

[0077] S242. Define the non-dominated sorting rule: Randomly select an individual x from a population of size 2N. s and individual x t Compare the manufacturing span and delay costs 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 f2(x) and f2(x) s ) <f2(x t If at least one of them is true, then individual x is said to be true. s Dominant individual x t And set s = 1;

[0078] S243. For all t = 1, 2, ..., N and t ≠ s, compare individual x according to the definition. s and individual x t Non-dominant relationship; if no individual x t Superior to individual xs Then individual x s Record it as a non-dominant individual and add it to set F1;

[0079] S244. Let s = s + 1, go to S243, and continue until all non-dominated individuals are found, then go to S245;

[0080] S245. Calculate the difference between the population and set F1 and use it as a new set X′. Go to S243. Repeatedly perform non-dominated selection on individuals in set X′ until all individuals in the population are classified according to the non-dominated relationship. Go to S246.

[0081] S246. Calculate the crowding degree C for individuals at 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 the hierarchy in order of non-dominated hierarchy from low to high. In the same hierarchy, select individuals as the next generation individuals in order of crowding value from high to low, until 2N population individuals are selected.

[0087] In the embodiments of this application, it is understood that, considering the situation where the workpiece comes from two agents, the batch production chain and the operation and maintenance chain, this application performs multi-objective optimization of the manufacturing span and delay cost of the high-end equipment production process. By solving the problem through a hybrid intelligent algorithm and obtaining an approximate optimal solution, it can effectively optimize and allocate resources in the supplier processing and manufacturer packaging stages of multi-chain integrated production, thereby significantly improving the operational efficiency and integration capability 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 deferred cost f A The best individual among them is denoted as θ. Refer to S22 above for the specific decoding process;

[0090] S252. Select a neighborhood structure NS in the way of roulette wheel l , and generate a neighborhood solution θ′ of θ according to this neighborhood structure NS l . Specifically, it includes:

[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, thus avoiding repeated iteration under the same solution and preventing the algorithm from falling into local optimum. Considering the complexity of the production of high-end equipment suppliers and manufacturers' packaging and the requirements of constraint relationships, 6 kinds of 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 on the left side of position a and the right side 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 on the left side of position a and the right side 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 sequence SG and the sequence MS;

[0097] In the fifth neighborhood structure, the last completed agent B workpiece in the manufacturer stage is found, and the encoding of the workpiece position in sequence SG and sequence MS is randomly swapped with a workpiece.

[0098] In the sixth neighborhood structure, the proxy workpiece A with the largest and smallest delay penalty coefficients is found, and the codes of the workpiece positions with the largest and smallest delay penalty coefficients in sequence SG and sequence MS are interchanged.

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

[0100] S261, Regarding the neighborhood structure NS l In the operation of performing variable neighborhood local search, the number of successful searches for local optimal solutions within each neighborhood structure, S, is recorded. l And the number of failures F l The neighborhood selection probability is updated based on the number of successful searches, as shown in the formula: in ε is a sufficiently small positive number, SUC l Representing the neighborhood structure NS l Local search success rate;

[0101] S262. Define variable O as the neighborhood selection variable, where O is a randomly generated number within the interval (0,1). Determine the range of O; if O satisfies... l-1 <O<o l Then choose the neighborhood structure NS l Perform a neighborhood search operation.

[0102] In the embodiments of this application, it is understood that a hybrid non-dominated sorting genetic algorithm based on neighborhood search is designed, combining the global optimization capability 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, along with a special crossover operator and a local search neighborhood structure, thereby effectively improving the solution efficiency and quality of the algorithm.

[0103] In some embodiments, this 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, agent data, factory data of the supplier's production stage and factory data of the manufacturer's reprocessing stage during the equipment production process; wherein, the agent data corresponds to batch production chain agent A and operation and maintenance chain agent B.

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

[0106] Decoding module 430 is used for the global optimal solution θ best Decoding is performed to determine the set of workpieces allocated 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 embodiments of this application, any multiple modules among the parameter setting module 410, acquisition module 420, and decoding module 430 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module.

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

[0109] S21. Set the execution parameters for the non-dominated sorting genetic algorithm based on variable neighborhood search. The execution parameters include: the first maximum number of iterations, Gen1, with an initial gen1 = 1, population size N, and crossover probability P. c The second maximum number of iterations for 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 based on the input parameters. Decode each solution in the initial population to obtain the manufacturing span and delay cost of batch production chain agent A. Calculate the non-dominated ranking and crowding value of individuals. Record the current optimal solution as the global optimal solution θ. best Individuals in the initial population consist of SG and MS codes; SG codes are used to characterize the order in which workpieces are assigned to machines during the supplier's production phase, and MS codes are used to characterize the order in which workpieces are reprocessed during the manufacturer's packaging phase.

[0111] S23. Use the crossover operator to perform a crossover operation on the initial population X to generate a new population X1. Merge the initial population X and the new population X1 to generate the target population; where 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 ranking 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 of the same size.

[0113] S25. Decode the individuals in the current population to obtain the manufacturing span and delay cost, calculate the non-dominated ranking and crowding value of each individual, mark the individual with the largest crowding value at the first non-dominated front (excluding the head and tail) as θ, and select a neighborhood structure NS from the preset neighborhood set based on a roulette wheel method. l A neighborhood solution θ′ is generated based on the selected neighborhood structure;

[0114] S26, Using Neighborhood Structure NS l Perform a local search on the neighborhood solution θ′ to obtain the local optimum solution θ″. Compare the crowding of θ and θ″. If θ″ is better than θ, let θ = θ″. At the same time, record the number of successful searches for the local optimum solution under this neighborhood structure. Otherwise, record the number of failed searches for the local optimum solution under this 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. If the optimal solution θ is better than the worst solution in the previous generation, then 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 If the comparison is made, and θ is better than θ best Let θ best =θ;

[0116] S28. Determine if gen1 ≤ Gen1 holds true. If it does, let gen1 = gen1 + 1, and use the updated population as the input solution for the next iteration, then 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-chain collaborative equipment production scheduling multi-objective optimization method, and can achieve its corresponding technical effect. For the sake of brevity, it will not be elaborated here.

[0118] In some embodiments, this application provides an electronic device, the structural schematic of which is shown below. Figure 5 As 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), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0121] Memory 520 may include mass storage for data or instructions. For example, and not limitingly, memory 520 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 520 may include removable or non-removable (or fixed) media. Where appropriate, memory 520 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 520 is non-volatile solid-state memory.

[0122] Memory 520 may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory 520 includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) 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-chain collaborative equipment production scheduling multi-objective optimization methods in the above embodiments.

[0123] The processor 510 reads and executes computer program instructions stored in the memory 520 to implement any 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 also include a communication interface 530 and a bus 500. Wherein, such as Figure 5 As shown, the processor 510, memory 520, and communication interface 530 are connected via bus 500 and communicate with each other.

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

[0126] Bus 500 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 500 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0127] Furthermore, in conjunction with the multi-chain collaborative equipment production scheduling multi-objective optimization method in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the multi-chain collaborative equipment production scheduling multi-objective optimization methods in the above embodiments.

[0128] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this 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, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on 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, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0130] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. 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 embodiments, or in a different order, or several steps can be performed simultaneously.

[0131] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, 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 apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. 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 is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose 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 scenario of multi-chain integrated production of equipment, that is, when the workpiece comes from two agents in the batch production chain and the operation and maintenance chain, it performs multi-objective optimization of the production process in terms of manufacturing span and delay cost, and comprehensively considers parallel machining and single-machine machining methods. This application sets input parameters based on 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 and obtains the global optimal solution through a non-dominated sorting genetic algorithm based on neighborhood search. It can effectively optimize and allocate resources in the two stages of supplier processing and manufacturer packaging, thereby significantly improving the operational efficiency and integration capability 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 capability 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, along with a special crossover operator and a local search neighborhood structure, thereby effectively improving the solution efficiency and quality of the algorithm.

[0135] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate 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. Based on the workpiece data, proxy data, supplier production stage factory data, and manufacturer reprocessing stage factory data during the equipment production process, set the input parameters for a non-dominated sorting genetic algorithm based on variable neighborhood search; wherein, the proxy data corresponds to the batch production chain proxy. With Operations and Maintenance Chain Agent ; S2. Obtain the global optimal solution based on the input parameters and the non-dominated sorting genetic algorithm based on variable neighborhood search. ; S3. For the global optimal solution Decoding is performed to determine the set of workpieces allocated 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. The workpieces produced by dual agents are manufactured by multiple machines in the supplier's production stage and reprocessed by a single machine in the manufacturer's reprocessing stage. The input parameters include: the set of workpieces of the batch production chain agent, the set of workpieces of the operation and maintenance chain agent, the set of all workpieces of the dual agents, the set of machines in the supplier's production stage, the processing time of the workpieces in the supplier's production stage, the reprocessing time of the workpieces in the manufacturer's packaging stage, the delivery time of the workpieces of the batch production chain agent, the weight of the workpieces of the batch production chain agent, and the upper limit of the manufacturing span of the operation and maintenance chain agent. Step S2 includes: S21. Set the execution parameters of the non-dominated sorting genetic algorithm based on variable neighborhood search, wherein the execution parameters include: a first maximum number of iterations. and let the initial Population size Crossover probability The second maximum number of iterations for local search and neighborhood set Each neighborhood structure Initial selection probability ; S22. Generate an initial population based on the input parameters, and decode each solution in the initial population to obtain the batch production chain agent. The manufacturing span and delay costs are calculated, the non-dominated ranking and crowding values ​​of individuals are calculated, and the current optimal solution is recorded as the global optimal solution. ; wherein, the individuals in the initial population are composed of Encoding and Encoding composition; the The coding is used to characterize the order in which workpieces and machines are assigned during the supplier's production phase. The coding is used to characterize the sequence of workpiece reprocessing during the manufacturer's packaging stage; S23. Using the crossover operator on the initial population Perform crossover operations to generate a new population. Merge the initial population and new populations To generate the target population; among which, the new population The quantity is The number of the target population is ; S24. In the target population, perform non-dominated sorting of all individuals and calculate the crowding value. Then, perform selection operations through an elite strategy to obtain a new population of the same size. S25. Decode the individuals in the current population to obtain the manufacturing span and delay cost, calculate the non-dominated ranking and crowding value of each individual, and mark the individual with the highest crowding value (excluding the head and tail) at the first non-dominated frontier. Based on the roulette wheel method, a neighborhood structure is selected from a preset neighborhood set. Generate a neighborhood solution based on the selected neighborhood structure. ; S26. Using neighborhood structure For the neighborhood solution Perform a local search to obtain a local optimum. ,Compare and The level of congestion, if Superior Then let Simultaneously, record the number of successful searches for local optima under this neighborhood structure; otherwise, record the number of failed searches for local optima under this neighborhood structure, and update the selection probability of the neighborhood structure. S27. Find the optimal solution in the current neighborhood. Compared with the worst individual in the previous generation of the population, if the optimal solution If the worst solution is better than the previous generation of the population, then the optimal solution is adopted. Replace the worst solution and use the best solution in the current neighborhood. With the global optimal solution If a comparison is made, Superior Then let ; S28, Judgment Is it true? If it is true, then let... The updated population is used as the input solution for the next iteration, and the process returns to step S22; otherwise, the globally optimal solution is output. .

2. The multi-objective optimization method for equipment production scheduling based on multi-chain collaboration as described in claim 1, characterized in that, The decoding process in step S22 includes: S221, based on the above Encoding generates workpiece Assigned machine location and processing time Calculate the workpiece based on the preset first strategy Completion time during the supplier's production phase; S222, based on the above Encoding generates workpiece Assigned machine location and processing time The workpiece is calculated based on the preset second strategy. Completion time during the supplier's production phase; S223, based on the above Encoding generates workpiece Assigned reprocessing location and packaging time Calculate batch production chain agency Total manufacturing span; The batch production chain agent The total manufacturing span calculation satisfies the expression: In the formula, , , Indicates workpiece Completion time during the manufacturing phase, Indicates workpiece The start of processing at the manufacturer stage. Indicates workpiece Processing time, Indicates workpiece Completion time during the supplier phase; S224, according to the above coding 、 The Coding, workpiece Preset delivery period and preset delay penalty coefficient Calculate the batch production chain agent Total delay costs; The batch production chain agent The total delay cost calculation satisfies the expression: ； S225, based on the above Encoding generates workpiece Assigned reprocessing location and packaging time Calculate the operation and maintenance chain agent Total manufacturing span; The operation and maintenance chain agent The total manufacturing span calculation satisfies the expression: , in, , Indicates workpiece Completion time during the manufacturing phase, Indicates workpiece At the start of processing during the manufacturing phase, express Processing time, Indicates workpiece Completion time during the supplier phase.

3. The multi-objective optimization method for equipment production scheduling based on multi-chain collaboration as described in claim 2, characterized in that, Step S23 includes: S231, from the initial population Select individual and individuals ; S232, in the interval Two random integers are generated in the middle. , As the intersection point, where New Individual Replicated individuals middle The code is located at and The encoding between the two, the remaining encoding retains the individual The encoding in the new individual Replicated individuals middle The code is located at and The encoding between the two, the remaining encoding retains the individual The encoding in; S233, in the interval Two random integers are generated in the middle. , As the intersection point, where New Individual Replicated individuals middle The code is located at and The encoding between the two, the remaining encoding retains the individual The encoding in the new individual Replicated individuals middle The code is located at and The encoding between the two, the remaining encoding retains the individual The encoding in; S234. Determine the number of crossovers. Is it greater than If yes, terminate the crossover operation; otherwise, proceed to S231.

4. The multi-objective optimization method for equipment production scheduling based on multi-chain collaboration as described in claim 2, characterized in that, Step S24 includes: S241. Obtain the batch production chain agent of each individual in the population through decoding. The maximum completion time and total delay cost of the intermediate workpiece are denoted as follows: and ; S242. Define the non-dominated sorting rule: from a quantity of... Randomly select an individual from the population and individuals Compare the manufacturing span and delay costs of the operation and maintenance chain agency. , ,and and If at least one of them is true, then it is called an individual. Dominant Individual and set ; S243, For all and Comparing individuals according to the definition and individuals Non-dominant relationship; if there are no arbitrary individuals Superior to individuals Then the individual Mark it as a non-dominant individual and add it to the set. middle; S244, Order Proceed to S243 until all non-dominant individuals are identified, then proceed to S245; S245, Computing Populations and Sets The difference is used as a new set. Proceed to S243, for the set Individuals in the population repeatedly perform non-dominated selection until all individuals in the population are classified according to non-dominated relations, and then move to S246; S246. Calculate crowding degree for individuals at the same non-dominated level. The formula includes: in, Represents an individual The degree of congestion; Represents an individual The maximum value under the objective function; Represents an individual The minimum value under the objective function; S247. Select levels in ascending order of non-dominated hierarchy. Within the same level, select individuals as members of the next generation population in descending order of crowding value, until a suitable level is selected. Individuals in a population.

5. The multi-objective optimization method for equipment production scheduling based on multi-chain collaboration as described in claim 1, characterized in that, Step S26 includes: S261, Regarding Neighborhood Structure In the operation of performing variable neighborhood local search, the number of successful searches for local optima within each neighborhood structure is recorded. and number of failures The neighborhood selection probability is updated based on the number of successful searches, as shown in the formula: ,in , , It is a sufficiently small positive number. Representing neighborhood structure Local search success rate; S262, Defining Variables As a neighborhood selection variable, It is in the interval A randomly generated number is used to determine... The range, if it satisfies Then choose neighborhood structure Perform a neighborhood search operation.

6. A multi-objective optimization system for equipment production scheduling based on multi-chain collaboration, based on the multi-objective optimization method for equipment production scheduling based on multi-chain collaboration as described in any one of claims 1-5, characterized in that, include: 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, proxy data, factory data from the supplier's production stage, and factory data from the manufacturer's reprocessing stage during the equipment production process; wherein, the proxy data corresponds to the batch production chain proxy. With Operations and Maintenance Chain Agent ; The acquisition module is used to obtain the global optimal solution based on the input parameters and the non-dominated sorting genetic algorithm based on variable neighborhood search. ; The decoding module is used for the globally optimal solution. Decoding is performed to determine the set of workpieces allocated 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.

7. An electronic device, characterized in that, include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the multi-objective optimization method for equipment production scheduling based on multi-chain collaboration as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that, when executed by a processor, implement the multi-objective optimization method for equipment production scheduling based on multi-chain collaboration as described in any one of claims 1 to 5.

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