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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Solving Fuzzy Job-Shop Scheduling Problem by a Hybrid PSO Algorithm
verfasst von : Junqing Li, Quan-Ke Pan, P. N. Suganthan, M. Fatih Tasgetiren
Erschienen in: Swarm and Evolutionary Computation
Verlag: Springer Berlin Heidelberg
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This paper proposes a hybrid particle swarm optimization (PSO) algorithm for solving the job-shop scheduling problem with fuzzy processing times. The objective is to minimize the maximum fuzzy completion time, i.e., the fuzzy makespan. In the proposed PSO-based algorithm performs global explorative search, while the tabu search (TS) conducts the local exploitative search. One-point crossover operator is developed for the individual to learn information from the other individuals. Experimental results on three well-known benchmarks and a randomly generated case verify the effectiveness and efficiency of the proposed algorithm.