Two new robust genetic algorithms for the flowshop scheduling problem

Research Area: Articulos Year: 2006
Type of Publication: Article
Authors: Ruiz, Rubén; Maroto, C.; Alcaraz, Javier
Journal: Omega-International Journal of Management Science Volume: 34
Number: 5 Pages: 461-476
Times Cited: 16 Article English Ruiz, R Univ Politecn Valencia, Dept Estadist {&} Invest Operat Aplicadas {&} Calidad, Edif 1-3,Camino Vera S-N, Valencia 46021, Spain Cited References Count: 46 005JW PERGAMON-ELSEVIER SCIENCE LTD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND OXFORD
The flowshop scheduling problem (FSP) has been widely studied in the literature and many techniques for its solution have been proposed. Some authors have concluded that genetic algorithms are not suitable for this hard, combinatorial problem unless hybridization. is used. This work proposes new genetic algorithms for solving the permutation FSP that prove to be competitive when compared to many other well known algorithms. The optimization criterion considered is the minimization of the total completion time or makespan (C-max). We show a robust genetic algorithm and a fast hybrid implementation. These algorithms use new genetic operators, advanced techniques like hybridization with local search and an efficient population initialization as well as a new generational scheme. A complete evaluation of the different parameters and operators of the algorithms by means of a Design of Experiments approach is also given. The algorithm's effectiveness is compared against I I other methods, including genetic algorithms, tabu search, simulated annealing and other advanced and recent techniques. For the evaluations we use Taillard's well known standard benchmark. The results show that the proposed algorithms are very effective and at the same time are easy to implement. (c) 2005 Elsevier Ltd. All rights reserved
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