GENETIC ALGORITHMS FOR FLOWSHOP SCHEDULING PROBLEMS

In this paper, we apply a genetic algorithm to flowshop scheduling pro blems and examine two hybridizations of the genetic algorithm with oth er search algorithms. First we examine various genetic operators to de sign a genetic algorithm for the flowshop scheduling problem with an o bjective of minimizing the makespan. By computer simulations, we show that the two-point crossover and the shift change mutation are effecti ve for this problem. Next we compare the genetic algorithm with other search algorithms such as local search, taboo search and simulated ann ealing. Computer simulations show that the genetic algorithm is a bit inferior to the others. In order to improve the performance of the gen etic algorithm, we examine the hybridization of the genetic algorithms . We show two hybrid genetic algorithms: genetic local search and gene tic simulated annealing. Their high performance is demonstrated by com puter simulations. Copyright (C) 1996 Elsevier Science Ltd