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A Hybrid Multiobjective Evolutionary Approach for Flexible Job-Shop Scheduling Problems

Author

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  • Jian Xiong
  • Xu Tan
  • Ke-wei Yang
  • Li-ning Xing
  • Ying-wu Chen

Abstract

This paper addresses multiobjective flexible job-shop scheduling problem (FJSP) with three simultaneously considered objectives: minimizing makespan, minimizing total workload, and minimizing maximal workload. A hybrid multiobjective evolutionary approach (H-MOEA) is developed to solve the problem. According to the characteristic of FJSP, a modified crowding distance measure is introduced to maintain the diversity of individuals. In the proposed H-MOEA, well-designed chromosome representation and genetic operators are developed for FJSP. Moreover, a local search procedure based on critical path theory is incorporated in H-MOEA to improve the convergence ability of the algorithm. Experiment results on several well-known benchmark instances demonstrate the efficiency and stability of the proposed algorithm. The comparison with other recently published approaches validates that H-MOEA can obtain Pareto-optimal solutions with better quality and/or diversity.

Suggested Citation

  • Jian Xiong & Xu Tan & Ke-wei Yang & Li-ning Xing & Ying-wu Chen, 2012. "A Hybrid Multiobjective Evolutionary Approach for Flexible Job-Shop Scheduling Problems," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-27, August.
    • Handle: RePEc:hin:jnlmpe:478981
    DOI: 10.1155/2012/478981
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    Cited by:

    1. Zigao Wu & Shaohua Yu & Tiancheng Li, 2019. "A Meta-Model-Based Multi-Objective Evolutionary Approach to Robust Job Shop Scheduling," Mathematics, MDPI, vol. 7(6), pages 1-19, June.

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