An improved genetic algorithm for solving flexible job shop. Issue 4 (June 2021)

• Record Type: Journal Article
• Authors: Yang, Ruifeng
• Is Part Of: Journal of physics. Volume 1952:Issue 4(2021)
• Publication Date: 2021-06
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1952/4/042066
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: According to the characteristics of flexible job shop scheduling (FJPS), a mathematical model was established to minimize the maximum completion time, and an improved genetic algorithm was proposed to solve the problem. A variety of heuristic methods are used to improve the quality of the initial solution. The parallel double-chain encoding is designed and the optimal insertion method is proposed to improve the quality of the solution. Two crossover methods, namely IPOX crossover and multi-point crossover, are adopted to inherit the excellent genes from the parent generation and balance the global development ability of the algorithm. In different coding layers, a variety of variation methods were used to maintain the diversity of the population. The local development ability of the algorithm is enhanced by variable neighborhood search. Finally, by solving the Brandimarte standard example and comparing with other algorithms, the feasibility and effectiveness of the proposed algorithm are verified.