Abstract
AbstractDue to the uncertainty of the processing time in the practical production, no idle flow shop scheduling problem with fuzzy processing time is introduced. The objective is to find a sequence that minimizes the mean makespan and the spread of the makespan by using a method for ranking fuzzy numbers. The particle swarm optimization (PSO) is a population-based optimization technique that has been applied to a wide range of problems, but there is little reported in respect of application to scheduling problems because of its unsuitability for them. In the paper, PSO is redefined and modified by introducing genetic operations such as crossover and mutation to update the particles, which is called GPSO and successfully employed to solve the formulated problem. Several benchmarks with fuzzy processing time are used to test GPSO. Through the comparative simulation results with genetic algorithm, the feasibility and effectiveness of the proposed method are demonstrated.KeywordsParticle Swarm OptimizationSchedule ProblemFuzzy NumberFlow ShopTriangular Fuzzy NumberThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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