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An Enhanced MOPSO Algorithm for Energy-Efficient Single-Machine Production Scheduling

Author

Listed:
  • Yueyue Liu

    (School of Economics & Management, Xiamen University of Technology, Xiamen 361024, China)

  • Xiaoya Liao

    (School of Economics & Management, Xiamen University of Technology, Xiamen 361024, China)

  • Rui Zhang

    (School of Economics & Management, Xiamen University of Technology, Xiamen 361024, China)

Abstract

In recent years, the concerns on energy efficiency in manufacturing systems have been growing rapidly due to the pursuit of sustainable development. Production scheduling plays a vital role in saving energy and promoting profitability for the manufacturing industry. In this paper, we are concerned with a just-in-time (JIT) single machine scheduling problem which considers the deterioration effect and the energy consumption of job processing operations. The aim is to determine an optimal sequence for processing jobs under the objective of minimizing the total earliness/tardiness cost and the total energy consumption. Since the problem is NP -hard, an improved multi-objective particle swarm optimization algorithm enhanced by a local search strategy (MOPSO-LS) is proposed. We draw on the idea of k -opt neighborhoods and modify the neighborhood operations adaptively for the production scheduling problem. We consider two types of k -opt operations and implement the one without overlap in our local search. Three different values of k have been tested. We compare the performance of MOPSO-LS and MOPSO (excluding the local search function completely). Besides, we also compare MOPSO-LS with the well-known multi-objective optimization algorithm NSGA-II. The experimental results have verified the effectiveness of the proposed algorithm. The work of this paper will shed some light on the fast-growing research related to sustainable production scheduling.

Suggested Citation

  • Yueyue Liu & Xiaoya Liao & Rui Zhang, 2019. "An Enhanced MOPSO Algorithm for Energy-Efficient Single-Machine Production Scheduling," Sustainability, MDPI, vol. 11(19), pages 1-16, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5381-:d:271870
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    References listed on IDEAS

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    1. J-B Wang & Z-Q Xia, 2006. "Flow shop scheduling problems with deteriorating jobs under dominating machines," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(2), pages 220-226, February.
    2. Wang, Ji-Bo & Xia, Zun-Quan, 2006. "Flow shop scheduling with deteriorating jobs under dominating machines," Omega, Elsevier, vol. 34(4), pages 327-336, August.
    3. Aghelinejad, MohammadMohsen & Ouazene, Yassine & Yalaoui, Alice, 2019. "Complexity analysis of energy-efficient single machine scheduling problems," Operations Research Perspectives, Elsevier, vol. 6(C).
    4. Wang, Haibo & Alidaee, Bahram, 2019. "Effective heuristic for large-scale unrelated parallel machines scheduling problems," Omega, Elsevier, vol. 83(C), pages 261-274.
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    Cited by:

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    2. Ovidiu Ivanov & Samiran Chattopadhyay & Soumya Banerjee & Bogdan-Constantin Neagu & Gheorghe Grigoras & Mihai Gavrilas, 2020. "A Novel Algorithm with Multiple Consumer Demand Response Priorities in Residential Unbalanced LV Electricity Distribution Networks," Mathematics, MDPI, vol. 8(8), pages 1-24, July.

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