期刊
APPLIED SOFT COMPUTING
卷 52, 期 -, 页码 39-47出版社
ELSEVIER
DOI: 10.1016/j.asoc.2016.11.031
关键词
Iterated local search; Multi-objectiveoptimization; Permutation flowshop scheduling with; sequence-dependent setup times
资金
- National Natural Science Foundation of China [11561036, 71301022]
- Ministry of Science Technology (MOST) of Taiwan [MOST105-2221-E-035053-MY3, MOST 103-2410-H-035-022-MY2]
Due to its simplicity yet powerful search ability, iterated local search (ILS) has been widely used to tackle a variety of single-objective combinatorial optimization problems. However, applying ILS to solve multi-objective combinatorial optimization problems is scanty. In this paper we design a multi-objective ILS (MOILS) to solve the multi-objective permutation flowshop scheduling problem with sequence-dependent setup times to minimize the makespan and total weighted tardiness of all jobs. In the MOILS, we design a Pareto-based variable depth search in the multi-objective local search phase. The search depth is dynamically adjusted during the search process of the MOILS to strike a balance between exploration and exploitation. We incorporate an external archive into the MOILS to store the non-dominated solutions and provide initial search points for the MOILS to escape from local optima traps. We compare the MOILS with several multi-objective evolutionary algorithms (MOEAs) shown to be effective for treating the multi-objective permutation flowshop scheduling problem in the literature. The computational results show that the proposed MOILS outperforms the MOEAs. (C) 2016 Elsevier B. V. All rights reserved.
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