Multi-objective fuzzy multiprocessor flowshop scheduling

• This paper considers the bi-objective hybrid flowshop scheduling problems with bell-shaped fuzzy processing and sequence-dependent setup times.
• To minimize two criteria, namely makespan and sum of the earliness and tardiness a bi-level algorithm is proposed in this paper.
• In level 1, the population is decomposed into several sub-populations and genetic algorithm is designed using a scalar bi-objective concept.
• To improve the first level results, after unifying the solutions in big population, a PSO is proposed in level 2.

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.

Multi-objective scheduling algorithm with bi-level solving algorithm.Figure optionsDownload as PowerPoint slide