An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cie.2008.07.021 Byline: Guohui Zhang, Xinyu Shao, Peigen Li, Liang Gao Keywords: Multi-objective optimization; Flexible job-shop scheduling; Particle swarm optimization; Tabu search Abstract: 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. Author Affiliation: The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science & Technology, Wuhan, Hubei Province 430074, China Article History: Received 20 April 2007; Revised 25 July 2008; Accepted 28 July 2008