Abstract
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.
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Li, J., Pan, QK., Suganthan, P.N., Tasgetiren, M.F. (2012). Solving Fuzzy Job-Shop Scheduling Problem by a Hybrid PSO Algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Swarm and Evolutionary Computation. EC SIDE 2012 2012. Lecture Notes in Computer Science, vol 7269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29353-5_32
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DOI: https://doi.org/10.1007/978-3-642-29353-5_32
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