期刊
COMPUTERS & INDUSTRIAL ENGINEERING
卷 56, 期 4, 页码 1309-1318出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2008.07.021
关键词
Multi-objective optimization; Flexible job-shop scheduling; Particle swarm optimization; Tabu search
资金
- 863 High Technology Plan Foundation of China [2006AA04Z131, 2007AA04Z107]
- 973 National Basic Research Program of China [2004CB719405]
- National Natural Science Foundation of China [50305008]
Flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem. Although the traditional optimization algorithms could obtain preferable results in solving the mono-objective FJSP. However, they are very difficult to solve multi-objective FJSP very well. In this paper, a particle swarm optimization (PSO) algorithm and a tabu search (TS) algorithm are combined to solve the multi-objective FJSP with several conflicting and incommensurable objectives. PSO which integrates local search and global search scheme possesses high search efficiency. And, TS is a meta-heuristic which is designed for finding a near optimal solution of combinatorial optimization problems. Through reasonably hybridizing the two optimization algorithms, an effective hybrid approach for the multi-objective FJSP has been proposed. The computational results have proved that the proposed hybrid algorithm is an efficient and effective approach to solve the multi-objective FJSP, especially for the problems on a large scale. (C) 2008 Elsevier Ltd. All rights reserved.
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