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Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm

Author

Listed:
  • Yaping Fu

    (Qingdao University
    Northeastern University)

  • Hongfeng Wang

    (Northeastern University)

  • Guangdong Tian

    (Jilin University)

  • Zhiwu Li

    (Macau University of Science and Technology
    Xidian University)

  • Hesuan Hu

    (Xidian University)

Abstract

Multi-agent and deteriorating scheduling has gained an increasing concern from academic and industrial communities in recent years. This study addresses a two-agent stochastic flow shop deteriorating scheduling problem with the objectives of minimizing the makespan of the first agent and the total tardiness of the second agent. In the investigated problem, the normal processing time of jobs is a random variable, and the actual processing time of jobs is a linear function of their normal processing time and starting time. To solve this problem efficiently, this study proposes a hybrid multi-objective evolutionary algorithm which is a combination of an evolutionary algorithm and a local search method. It maintains two populations and one archive. The two populations are utilized to execute the global and local searches, where one population employs an evolutionary algorithm to explore the whole solution space, and the other applies a local search method to exploit the promising regions. The archive is used to guide the computation resource allocation in the search process. Some special techniques, i.e., evolutionary methods, local search methods and information exchange strategies between two populations, are designed to enhance the exploration and exploitation ability. Comparing with the classical and popular multi-objective evolutionary algorithms on some test instances, the experimental results show that the proposed algorithm can produce satisfactory solution for the investigated problem.

Suggested Citation

  • Yaping Fu & Hongfeng Wang & Guangdong Tian & Zhiwu Li & Hesuan Hu, 2019. "Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2257-2272, June.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:5:d:10.1007_s10845-017-1385-4
    DOI: 10.1007/s10845-017-1385-4
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    References listed on IDEAS

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    Cited by:

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    3. Fu, Yaping & Wu, Di & Wang, Yan & Wang, Hongfeng, 2020. "Facility location and capacity planning considering policy preference and uncertain demand under the One Belt One Road initiative," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 172-186.

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