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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Vydané v:International journal of production research Ročník 54; číslo 4; s. 1110 - 1121
Hlavní autori: Lin, Shih-Wei, Ying, Kuo-Ching, Wu, Wen-Jie, Chiang, Yen-I
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Taylor & Francis 16.02.2016
Taylor & Francis LLC
Predmet:
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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Abstract 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.
AbstractList 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.
Author Wu, Wen-Jie
Lin, Shih-Wei
Chiang, Yen-I
Ying, Kuo-Ching
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  surname: Chiang
  fullname: Chiang, Yen-I
  organization: Department of Information Management, Chang Gung University
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Snippet This work proposes a high-performance algorithm for solving the multi-objective unrelated parallel machine scheduling problem. The proposed approach is based...
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SubjectTerms 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
Title Multi-objective unrelated parallel machine scheduling: a Tabu-enhanced iterated Pareto greedy algorithm
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