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    黄学文

    • 副教授       硕士生导师
    • 性别:男
    • 毕业院校:大连理工大学
    • 学位:博士
    • 所在单位:运营与物流管理研究所
    • 学科:企业管理. 管理科学与工程
    • 办公地点:创新园大厦B座,1616
    • 联系方式:0411-84709477转83
    • 电子邮箱:huangxuewen@dlut.edu.cn

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    AN ENHANCED GENETIC ALGORITHM WITH AN INNOVATIVE ENCODING STRATEGY FOR FLEXIBLE JOB-SHOP SCHEDULING WITH OPERATION AND PROCESSING FLEXIBILITY

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    论文类型:期刊论文

    第一作者:黄学文

    通讯作者:Zhang, Xiaotong,Islam, Sardar M. N.,Vega-Mejia, Carlos A.

    发表时间:2021-01-10

    发表刊物:JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION

    卷号:16

    期号:6

    页面范围:2943-2969

    ISSN号:1547-5816

    关键字:IPPS; flexible job-shop scheduling; operation flexibility; processing flexibility; Genetic Algorithm

    摘要:This paper considers the Flexible Job-shop Scheduling Problem with Operation and Processing flexibility (FJSP-OP) with the objective of minimizing the makespan. A Genetic Algorithm based approach is presented to solve the FJSP-OP. For the performance improvement, a new and concise Four-Tuple Scheme (FTS) is proposed for modeling a job with operation and processing flexibility. Then, with the FTS, an enhanced Genetic Algorithm employing a more efficient encoding strategy is developed. The use of this encoding strategy ensures that the classic genetic operators can be adopted to the utmost extent without generating infeasible offspring. Experiments have validated the proposed approach, and the results have shown the effectiveness and high performance of the proposed approach.