期刊
APPLIED SOFT COMPUTING
卷 56, 期 -, 页码 65-81出版社
ELSEVIER
DOI: 10.1016/j.asoc.2017.03.004
关键词
Parallel machine scheduling; Fuzzy number ranking; Ant colony optimization; Hybrid metaheuristic; Spread of fuzziness
This paper studies parallel machine scheduling problems in consideration of real world uncertainty quantified based on fuzzy numbers. Although this study is not the first to study the subject problem, it advances this area of research in two areas: (1) Rather than arbitrarily picking a method, it chooses the most appropriate fuzzy number ranking method based on an in-depth investigation of the effect of spread of fuzziness on the performance of fuzzy ranking methods; (2) It develops the first hybrid ant colony optimization for fuzzy parallel machine scheduling. Randomly generated datasets are used to test the performance of fuzzy ranking methods as well as the proposed algorithm, i.e. hybrid ant colony optimization. The proposed hybrid ant colony optimization outperforms a hybrid particle swarm optimization published recently and two simulated annealing based algorithms modified from our previous work. (C) 2017 Elsevier B.V. All rights reserved.
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