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Performance Evaluation of a Parameter-Free Genetic Algorithm for Job-Shop Scheduling Problems
Shouichi Matsui, Isamu Watanabe, and Ken-ichi Tokoro
Communication & Information Research Laboratory (CIRL)
Central Research Institute of Electric Power Industry (CRIEPI)
2-11-1 Iwado-kita, Komae-shi, Tokyo 201-8511, Japan
{matsui,isamu,tokoro}@criepi.denken.or.jp
Abstract. The job-shop scheduling problem (JSSP) is well known as one of the most difficult NP-hard combinatorial optimization problems. Genetic Algorithms (GAs) for solving the JSSP have been proposed, and they perform well compared with other approaches [1].
LNCS 2724, p. 1598 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003