Research on Job-shop Scheduling Problem Based-on Improved Genetic Algorithm. Issue 1 (1st March 2022)

• Record Type: Journal Article
• Authors: Wei, Bo; Qi, Jiayuan; Wen, Jiafu
• Is Part Of: Journal of physics. Volume 2216:Issue 1(2022)
• Publication Date: 2022-03-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2216/1/012083
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Considering minimizing completion time and maximizing equipment utilization of job shop scheduling problem, an improved genetic algorithm was proposed. First, constructing a two-layer chromosome coding structure, with which the initial population quality and the diversity of the population were strengthened and improved. Next, in order to enhance the local search ability and improve the algorithm convergence speed, the population generated after selection, crossover, and mutation was divided into families, and the elite population were combined with explosive fireworks of the firework algorithm. Then, in order to prevent the algorithm from maturing prematurely, the disadvantaged groups were mutated through(by) the dynamic mutation mechanism to narrow the gap between the super individual and the average. Last, the parameters of the algorithm were optimized by orthogonal tests, and performance of the algorithm was verified by classical test examples.