An Asynchronous Genetic LocalSearch Algorithm with Total Flow Time Criterion for Fuzzy Flow Shop Scheduling Problem

With the objective of total flow time, the permutation flow shop scheduling problem is discussed in this paper. The uncertain processing time is described by fuzzy mathematics. Furthermore, an improved genetic algorithm, asynchronous genetic localsearch algorithm (AGLA), is presented. In AGLA, an individual in the initial population is generated by a constructive heuristic method, and the others are randomly yielded. And then, by an enhanced variable neighborhood search strategy and a crossover operator, the asynchronous evolution is executed for each pair of individuals. Besides, a restart strategy is employed to avoid the problem of local minimum. Finally, numerical simulation results show the effectiveness of the AGLA for fuzzy flow shop scheduling problem.