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Energy-Efficient Hybrid Flowshop Scheduling with Consistent Sublots Using an Improved Cooperative Coevolutionary Algorithm

Author

Listed:
  • Chengshuai Li

    (School of Computer Science, Liaocheng University, Liaocheng 252059, China)

  • Biao Zhang

    (School of Computer Science, Liaocheng University, Liaocheng 252059, China)

  • Yuyan Han

    (School of Computer Science, Liaocheng University, Liaocheng 252059, China)

  • Yuting Wang

    (School of Computer Science, Liaocheng University, Liaocheng 252059, China)

  • Junqing Li

    (School of Computer Science, Shandong Normal University, Jinan 252000, China)

  • Kaizhou Gao

    (Macau Institute of Systems Engineering, Macau University of Science and Technology, Taipa, Macau 999078, China)

Abstract

Energy conservation, emission reduction, and green and low carbon are of great significance to sustainable development, and are also the theme of the transformation and upgrading of the manufacturing industry. This paper concentrates on studying the energy-efficient hybrid flowshop scheduling problem with consistent sublots (HFSP_ECS) with the objective of minimizing the energy consumption. To solve the problem, the HFSP_ECS is decomposed by the idea of “divide-and-conquer”, resulting in three coupled subproblems, i.e., lot sequence, machine assignment, and lot split, which can be solved by using a cooperative methodology. Thus, an improved cooperative coevolutionary algorithm (vCCEA) is proposed by integrating the variable neighborhood descent (VND) strategy. In the vCCEA, considering the problem-specific characteristics, a two-layer encoding strategy is designed to represent the essential information, and a novel collaborative model is proposed to realize the interaction between subproblems. In addition, special neighborhood structures are designed for different subproblems, and two kinds of enhanced neighborhood structures are proposed to search for potential promising solutions. A collaborative population restart mechanism is established to ensure the population diversity. The computational results show that vCCEA can coordinate and solve each subproblem of HFSP_ECS effectively, and outperform the mathematical programming and the other state-of-the-art algorithms.

Suggested Citation

  • Chengshuai Li & Biao Zhang & Yuyan Han & Yuting Wang & Junqing Li & Kaizhou Gao, 2022. "Energy-Efficient Hybrid Flowshop Scheduling with Consistent Sublots Using an Improved Cooperative Coevolutionary Algorithm," Mathematics, MDPI, vol. 11(1), pages 1-27, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:77-:d:1014581
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    References listed on IDEAS

    as
    1. Ruiz, Rubén & Vázquez-Rodríguez, José Antonio, 2010. "The hybrid flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 205(1), pages 1-18, August.
    2. Pan, Quan-Ke & Gao, Liang & Li, Xin-Yu & Gao, Kai-Zhou, 2017. "Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times," Applied Mathematics and Computation, Elsevier, vol. 303(C), pages 89-112.
    3. Kalir, Adar A. & Sarin, Subhash C., 2001. "A near-optimal heuristic for the sequencing problem in multiple-batch flow-shops with small equal sublots," Omega, Elsevier, vol. 29(6), pages 577-584, December.
    4. Min Shi & Shang Gao, 2017. "Reference sharing: a new collaboration model for cooperative coevolution," Journal of Heuristics, Springer, vol. 23(1), pages 1-30, February.
    5. Zhang, Wei & Yin, Changyu & Liu, Jiyin & Linn, Richard J., 2005. "Multi-job lot streaming to minimize the mean completion time in m-1 hybrid flowshops," International Journal of Production Economics, Elsevier, vol. 96(2), pages 189-200, May.
    6. Liu, Jiyin, 2008. "Single-job lot streaming in m - 1 two-stage hybrid flowshops," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1171-1183, June.
    7. Missaoui, Ahmed & Ruiz, Rubén, 2022. "A parameter-Less iterated greedy method for the hybrid flowshop scheduling problem with setup times and due date windows," European Journal of Operational Research, Elsevier, vol. 303(1), pages 99-113.
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