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• W2093551803 abstract "This paper considers a bi-objective hybrid flowshop scheduling problems with fuzzy tasks’ operation times, due dates and sequence-dependent setup times. To solve this problem, we propose a bi-level algorithm to minimize two criteria, namely makespan, and sum of the earliness and tardiness, simultaneously. In the first level, the population will be decomposed into several sub-populations in parallel and each sub-population is designed for a scalar bi-objective. In the second level, non-dominant solutions obtained from sub-population bi-objective random key genetic algorithm (SBG) in the first level will be unified as one big population. In the second level, for improving the Pareto-front obtained by SBG, based on the search in Pareto space concept, a particle swarm optimization (PSO) is proposed. We use a defuzzification function to rank the Bell-shaped fuzzy numbers. The non-dominated sets obtained from each of levels and an algorithm presented previously in literature are compared. The computational results showed that PSO performs better than others and obtained superior results." @default.
• W2093551803 created "2016-06-24" @default.
• W2093551803 creator A5038196297 @default.
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• W2093551803 date "2014-08-01" @default.
• W2093551803 modified "2023-10-18" @default.
• W2093551803 title "Multi-objective fuzzy multiprocessor flowshop scheduling" @default.
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• W2093551803 doi "https://doi.org/10.1016/j.asoc.2014.03.031" @default.
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