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Sustainable Scheduling of Cloth Production Processes by Multi-Objective Genetic Algorithm with Tabu-Enhanced Local Search

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  • Rui Zhang

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

Abstract

The dyeing of textile materials is the most critical process in cloth production because of the strict technological requirements. In addition to the technical aspect, there have been increasing concerns over how to minimize the negative environmental impact of the dyeing industry. The emissions of pollutants are mainly caused by frequent cleaning operations which are necessary for initializing the dyeing equipment, as well as idled production capacity which leads to discharge of unconsumed chemicals. Motivated by these facts, we propose a methodology to reduce the pollutant emissions by means of systematic production scheduling. Firstly, we build a three-objective scheduling model that incorporates both the traditional tardiness objective and the environmentally-related objectives. A mixed-integer programming formulation is also provided to accurately define the problem. Then, we present a novel solution method for the sustainable scheduling problem, namely, a multi-objective genetic algorithm with tabu-enhanced iterated greedy local search strategy (MOGA-TIG). Finally, we conduct extensive computational experiments to investigate the actual performance of the MOGA-TIG. Based on a fair comparison with two state-of-the-art multi-objective optimizers, it is concluded that the MOGA-TIG is able to achieve satisfactory solution quality within tight computational time budget for the studied scheduling problem.

Suggested Citation

  • Rui Zhang, 2017. "Sustainable Scheduling of Cloth Production Processes by Multi-Objective Genetic Algorithm with Tabu-Enhanced Local Search," Sustainability, MDPI, vol. 9(10), pages 1-26, September.
  • Handle: RePEc:gam:jsusta:v:9:y:2017:i:10:p:1754-:d:113524
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    References listed on IDEAS

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    1. Tan, K.C. & Goh, C.K. & Yang, Y.J. & Lee, T.H., 2006. "Evolving better population distribution and exploration in evolutionary multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 171(2), pages 463-495, June.
    2. Yifei Tong & Jingwei Li & Shai Li & Dongbo Li, 2016. "Research on Energy-Saving Production Scheduling Based on a Clustering Algorithm for a Forging Enterprise," Sustainability, MDPI, vol. 8(2), pages 1-17, February.
    3. Shih-Wei Lin & Kuo-Ching Ying & Wen-Jie Wu & Yen-I Chiang, 2016. "Multi-objective unrelated parallel machine scheduling: a Tabu-enhanced iterated Pareto greedy algorithm," International Journal of Production Research, Taylor & Francis Journals, vol. 54(4), pages 1110-1121, February.
    4. Cheng-Hsiang Liu & Wan-Ni Tsai, 2016. "Multi-objective parallel machine scheduling problems by considering controllable processing times," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(4), pages 654-663, April.
    5. Loukil, T. & Teghem, J. & Tuyttens, D., 2005. "Solving multi-objective production scheduling problems using metaheuristics," European Journal of Operational Research, Elsevier, vol. 161(1), pages 42-61, February.
    6. Shih-Wei Lin & Kuo-Ching Ying, 2015. "A multi-point simulated annealing heuristic for solving multiple objective unrelated parallel machine scheduling problems," International Journal of Production Research, Taylor & Francis Journals, vol. 53(4), pages 1065-1076, February.
    7. Sang-Oh Shim & KyungBae Park, 2016. "Technology for Production Scheduling of Jobs for Open Innovation and Sustainability with Fixed Processing Property on Parallel Machines," Sustainability, MDPI, vol. 8(9), pages 1-10, September.
    8. Kan Fang & Nelson Uhan & Fu Zhao & John Sutherland, 2013. "Flow shop scheduling with peak power consumption constraints," Annals of Operations Research, Springer, vol. 206(1), pages 115-145, July.
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    Cited by:

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    5. Qingmiao Liao & Jianjun Yang & Yong Zhou, 2019. "Sustainable Scheduling of an Automatic Pallet Changer System by Multi-Objective Evolutionary Algorithm with First Piece Inspection," Sustainability, MDPI, vol. 11(5), pages 1-24, March.

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