Feature selection method in dynamic job shop scheduling rule based on GEP-VNS evolution
A GEP-VNS, job shop technology, applied in data processing applications, instruments, calculations, etc., can solve problems such as low algorithm efficiency and large search space, achieve good scheduling performance, solve large search space, and improve accuracy and efficiency Effect
Pending Publication Date: 2022-08-02
XINJIANG UNIVERSITY
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[0006] In view of the above problems, the purpose of the present invention is to provide a feature selection method based on GEP-VNS evolution dynamic job shop scheduling rules, which can effectively solve the problem of large search space and algorithm efficiency when genetic expression programming evolves dynamic job shop scheduling rules. low problem, significantly improving the quality of generated scheduling rules
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[0089] The present invention will be described in detail below with reference to the accompanying drawings and embodiments.
[0090] The feature selection method in dynamic job shop scheduling rules based on GEP-VNS evolution includes the following steps:
[0091] S1, set the basic parameters of the GEP-VNS algorithm: total number of iterations ITER, population size popsize, number of genes n, head length head, tail length tail, mutation probability p m , the recombination probability p r and the shift probability p s The number of neighborhoods z, the function set FS{+, -, ×, ÷, min, max, if}, the terminal set TS is the original feature set of the dynamic job shop, including shop-related, workpiece-related and machine-related features;
[0092] The set ITER parameter is the end condition of the algorithm. When iter>ITER, the algorithm ends and an excellent scheduling rule set for the dynamic job shop scene is given. One scheduling rule corresponds to a polygenic chromosome ...
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The invention provides a feature selection method in a dynamic job shop scheduling rule based on GEP-VNS evolution. The method comprises the following steps: step 1, setting basic parameters of a GEP-VNS algorithm; 2, performing population initialization; 3, evaluating the fitness of population individuals according to the performance indexes of the workshop scene; 4, dividing the initial population into a series of sub-populations, performing global optimization on each sub-population by using GEP genetic manipulation, and forming an elite library by optimization results; 5, constructing four different neighborhood structures, and performing local search in the elite library by using an adaptive variable neighborhood search method to obtain an optimized population; 6, sorting the individuals in the optimized population according to the fitness, and selecting the first K excellent individuals as an optimal scheduling rule set; and 7, comprehensively considering the fitness of the rules in the optimal scheduling rule set and the contribution degree of the features to the rules to perform feature selection. According to the method, the problems of large search space and low algorithm efficiency caused by redundancy and irrelevant characteristics in the intelligent design of the dynamic job shop scheduling rule based on GEP are solved.
Description
technical field [0001] The invention relates to a feature selection method in a GEP-VNS evolution dynamic job shop scheduling rule. Background technique [0002] With the continuous development of product demand towards individualization, manufacturing processes are more diverse, and competition among manufacturing enterprises is becoming more and more intense. In order to improve their competitiveness, manufacturing enterprises are increasingly concerned about how to efficiently schedule the variability of production nodes under complex workshop conditions and the randomness of production disturbances in the network environment to meet diverse customer needs. It is difficult for traditional shop scheduling optimization methods to immediately respond to various disturbances caused by changes in working conditions in complex manufacturing systems. Scheduling rules are suitable for solving highly complex dynamic job shop scheduling problems in actual production due to their l...
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IPC IPC(8): G06Q10/06G06N3/00
CPCG06Q10/06313G06N3/006G06Q10/06316Y02P90/30
Inventor 阿地兰木·斯塔洪袁逸萍纪志勇巴智勇
Owner XINJIANG UNIVERSITY