期刊
APPLIED SOFT COMPUTING
卷 21, 期 -, 页码 139-148出版社
ELSEVIER
DOI: 10.1016/j.asoc.2014.03.031
关键词
Fuzzy hybrid flowshop scheduling; Bi-objective optimization; Pareto optimal solution; Particle swarm optimization; Parallel genetic algorithm; Bell-shaped fuzzy number
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 make span, 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. (C) 2014 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据