An effective genetic algorithm for the flexible job-shop scheduling problem |
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| Authors: | Guohui Zhang Liang Gao Yang Shi |
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| Affiliation: | 1. Zhengzhou Institute of Aeronautical Industry Management, Middle Daxue Road, Zhengzhou 450015, China;2. The State Key Laboratory of Digital Manufacturing Equipment and Technology, 1037 Luoyu Road, Huazhong University of Science and Technology, Wuhan 430074, China;1. Department of Business Administration, Fu Jen Catholic University, 510, Zhongzheng Road, Xinzhuang District, New Taipei City, Taiwan, ROC;2. Department of Industrial Management, National Taiwan University of Science and Technology, 106, no. 43, Keelung Road, Taipei, City, Taiwan, ROC;1. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore;2. Computer School, Liaocheng University, Liaocheng 252000, PR China;3. Singapore Institute of Manufacturing Technology, Nanyang Drive 638075, Singapore |
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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. |
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