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A hybrid genetic algorithm for no-wait flowshop scheduling problem
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DOI：10.1016/j.ijpe.2010.06.006.png)
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Abstract
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In this paper, a hybrid genetic algorithm is proposed to solve the no-wait flowshop scheduling problem with the makespan objective. The proposed algorithm hybridizes the genetic algorithm and a novel local search scheme. The OA-crossover operator is designed to enhance the capability of intensification in the genetic algorithm. The proposed local search scheme combines two local search methods: the Insertion Search (IS) and a novel local search method called the Insertion Search with Cut-and-Repair (ISCR). These two local search methods play different roles in the search process. The Insertion Search is responsible for searching a small neighborhood while the Insertion Search with Cut-and-Repair is responsible for searching a large neighborhood. The experimental results show the advantage of combining the two local search methods. Extensive experiments were conducted to evaluate the proposed hybrid genetic algorithm and the results revealed that the proposed algorithm is very competitive. It obtained the same best solutions that were reported in the literature for all problems in the benchmark provided by Carlier (1978). Also, it improved 5 out of the 21 current best solutions reported in the literature and achieved the current best solutions for 14 of the remaining 16 problems in the benchmark presented by Reeves (1995). Furthermore, the proposed algorithm was applied to effectively solve the 120 problems in the benchmark provided by Taillard (1990). (C) 2010 Elsevier B.V. All rights reserved.
Keywords:
Genetic algorithm
Local search
Hybrid genetic algorithm
No-wait flowshop scheduling
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