Multi-objective unrelated parallel machine scheduling: a Tabu-enhanced iterated Pareto greedy algorithm

This work proposes a high-performance algorithm for solving the multi-objective unrelated parallel machine scheduling problem. The proposed approach is based on the iterated Pareto greedy (IPG) algorithm but exploits the accessible Tabu list (TL) to enhance its performance. To demonstrate the superi...

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Veröffentlicht in:International journal of production research Jg. 54; H. 4; S. 1110 - 1121
Hauptverfasser: Lin, Shih-Wei, Ying, Kuo-Ching, Wu, Wen-Jie, Chiang, Yen-I
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Taylor & Francis 16.02.2016
Taylor & Francis LLC
Schlagworte:
Accessibility
Algorithms
Benchmarking
Greedy algorithms
Job shops
multi-objective
Optimization
Pareto optimality
Performance measurement
Production scheduling
Scheduling
Tabu-enhanced iterated Pareto greedy algorithm
unrelated parallel machine
ISSN:0020-7543, 1366-588X
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Beschreibung
Zusammenfassung:This work proposes a high-performance algorithm for solving the multi-objective unrelated parallel machine scheduling problem. The proposed approach is based on the iterated Pareto greedy (IPG) algorithm but exploits the accessible Tabu list (TL) to enhance its performance. To demonstrate the superior performance of the proposed Tabu-enhanced iterated Pareto greedy (TIPG) algorithm, its computational results are compared with IPG and existing algorithms on the same benchmark problem set. Experimental results reveal that incorporating the accessible TL can eliminate ineffective job moves, causing the TIPG algorithm to outperform state-of-the-art approaches in the light of five multi-objective performance metrics. This work contributes a useful theoretical and practical optimisation method for solving this problem.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2015.1047981