Junqing LiQuan-Ke PanP. N. SuganthanM. Fatih TasgetirenL RutkowskiM KorytkowskiR SchererR TadeusiewiczLA ZadehJM Zurada2025-10-062012978-3-642-29352-8, 978-3-642-29353-50302-9743https://gcris.yasar.edu.tr/handle/123456789/7534This 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.EnglishFuzzy processing time, Job-shop scheduling problem, Particle swarm optimization, Tabu searchPARTICLE SWARM OPTIMIZATION, GENETIC ALGORITHM, PROCESSING TIME, TABU SEARCHConference Object