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A high performing metaheuristic for multi-objective flowshop scheduling problem
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DOI：10.1016/j.cor.2014.01.006.png)
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Genetic algorithm is a powerful procedure for finding an optimal or near optimal solution for the flowshop scheduling problem. This is a simple and efficient algorithm which is used for both single and multi-objective problems. It can easily be utilized for real life applications. The proposed algorithm makes use of the principle of Pareto solutions. It mines the Pareto archive to extract the most repetitive sequences, and constitutes artificial chromosome for generation of the next population. In order to guide the search direction, this approach coupled with variable neighborhood search. This algorithm is applied on the flowshop scheduling problem for minimizing makespan and total weighted tardiness. For the assessment of the algorithm, its performance is compared with the MOGLS [1]. The results of the experiments allow us to claim that the proposed algorithm has a considerable performance in this problem. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
Genetic algorithm
Flowshop scheduling problem
Multi-objective optimization
Pareto archive
Variable Neighborhood Search (VNS)
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