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Optimizing fuzzy makespan and tardiness for unrelated parallel machine scheduling with archived metaheuristics

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Abstract

This research presents two simulated annealing (SA) and a greedy randomized adaptive search procedure (GRASP) to solve unrelated parallel machine scheduling problems (UPMSPs) with two fuzzy optimization objectives—makespan and average tardiness. Few studies have employed fuzzy approach to solve multi-objective UPMSPs. In the research, several schemes are incorporated into the algorithm, including (1) matching-based decoding; (2) acceptance rule based on Pareto dominance, objective fitness, or Pareto reference point distance; (3) random or fixed weighted direction search. The matching-based decoding scheme has two phases: first max–min matching and then Hungarian method. Experiments were conducted to evaluate the algorithms’ performance for moderate to large problem size instances. The results indicate that matching-based decoding scheme significantly improves solution quality, but will require more computation time. GRASP with path relinking performs slightly worse than objective fitness-based multi-objective simulated annealing algorithms (MOSA), but better than Pareto dominance-based MOSA in terms of several Pareto performance measures.

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  1. Department of Industrial Engineering and Management, Yuan-Ze University, Jongli, 320, Taiwan

    Chiuh-Cheng Chyu & Wei-Shung Chang

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  1. Chiuh-Cheng Chyu
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  2. Wei-Shung Chang
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Correspondence to Chiuh-Cheng Chyu.

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Chyu, CC., Chang, WS. Optimizing fuzzy makespan and tardiness for unrelated parallel machine scheduling with archived metaheuristics. Int J Adv Manuf Technol 57, 763–776 (2011). https://doi.org/10.1007/s00170-011-3317-3

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