An effective genetic algorithm for the flexible job-shop scheduling problem

Authors:
Guohui Zhang;Liang Gao;Yang Shi
Affiliations:
Zhengzhou Institute of Aeronautical Industry Management, Middle Daxue Road, Zhengzhou 450015, China;The State Key Laboratory of Digital Manufacturing Equipment and Technology, 1037 Luoyu Road, Huazhong University of Science and Technology, Wuhan 430074, China;The State Key Laboratory of Digital Manufacturing Equipment and Technology, 1037 Luoyu Road, Huazhong University of Science and Technology, Wuhan 430074, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 9
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Abstract

In this paper, we proposed an effective genetic algorithm for solving the flexible job-shop scheduling problem (FJSP) to minimize makespan time. In the proposed algorithm, Global Selection (GS) and Local Selection (LS) are designed to generate high-quality initial population in the initialization stage. An improved chromosome representation is used to conveniently represent a solution of the FJSP, and different strategies for crossover and mutation operator are adopted. Various benchmark data taken from literature are tested. Computational results prove the proposed genetic algorithm effective and efficient for solving flexible job-shop scheduling problem.