A local search heuristic with self-tuning parameter for permutation flow-shop scheduling problem

Date
2009
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Source Title
Proceedings of the IEEE Symposium on Computational Intelligence in Scheduling, CI-Sched 2009
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IEEE
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English
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Conference Paper
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Abstract

In this paper, a new local search metaheuristic is proposed for the permutation flow-shop scheduling problem. In general, metaheuristics are widely used to solve this problem due to its NP-completeness. Although these heuristics are quite effective to solve the problem, they suffer from the need to optimize parameters. The proposed heuristic, named STLS, has a single self-tuning parameter which is calculated and updated dynamically based on both the response surface information of the problem field and the performance measure of the method throughout the search process. Especially, application simplicity of the algorithm is attractive for the users. Results of the experimental study show that STLS generates high quality solutions and outperforms the basic tabu search, simulated annealing, and record-to-record travel algorithms which are well-known local search based metaheuristics.

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Experimental studies, High-quality solutions, Local search, Local search heuristic, Meta heuristics, Metaheuristic, Np-completeness, Performance measure, Permutation flow-shop scheduling, Record-to-record travels, Response surface, Search process, Selftuning, Computational complexity, Heuristic algorithms, Heuristic methods, Simulated annealing, Tabu search, Tuning, Problem solving
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Published Version (Please cite this version)