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Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times

Author

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  • Xin Yang
  • Zhenxiang Zeng
  • Ruidong Wang
  • Xueshan Sun

Abstract

This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems.

Suggested Citation

  • Xin Yang & Zhenxiang Zeng & Ruidong Wang & Xueshan Sun, 2016. "Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-13, December.
    • Handle: RePEc:plo:pone00:0167427
    DOI: 10.1371/journal.pone.0167427
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    References listed on IDEAS

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    1. Shahaboddin Shamshirband & Mohammad Shojafar & A. Hosseinabadi & Maryam Kardgar & M. Nasir & Rodina Ahmad, 2015. "OSGA: genetic-based open-shop scheduling with consideration of machine maintenance in small and medium enterprises," Annals of Operations Research, Springer, vol. 229(1), pages 743-758, June.
    2. Ali Hosseinabadi & Hajar Siar & Shahaboddin Shamshirband & Mohammad Shojafar & Mohd Nasir, 2015. "Using the gravitational emulation local search algorithm to solve the multi-objective flexible dynamic job shop scheduling problem in Small and Medium Enterprises," Annals of Operations Research, Springer, vol. 229(1), pages 451-474, June.
    3. Zahra Pooranian & Mohammad Shojafar & Jemal H. Abawajy & Ajith Abraham, 2015. "An efficient meta-heuristic algorithm for grid computing," Journal of Combinatorial Optimization, Springer, vol. 30(3), pages 413-434, October.
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    Cited by:

    1. João M. R. C. Fernandes & Seyed Mahdi Homayouni & Dalila B. M. M. Fontes, 2022. "Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review," Sustainability, MDPI, vol. 14(10), pages 1-34, May.

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