doi: 10.7763/IJCTE.2010.V2.139
A Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Flexible Job-Shop Scheduling Problem
Jun-qing Li
, , , ,
- College of Computer Science, Liaocheng University, Liaocheng 252059, People’s Republic of China,.
Abstract
Flexible job-shop scheduling problem (FJSP) is very important in many research fields such as production management and combinatorial optimization. The FJSP problems cover two difficulties namely machine assignment problem and operation sequencing problem. In this paper, a hybrid of particle swarm optimization (PSO) algorithm and tabu search (TS) algorithm are presented to solve the FJSP with the criterion to minimize the maximum completion time (makespan). In the novel hybrid algorithm, PSO was used to produce a swarm of high quality candidate solutions, while TS was used to obtain a near optimal solution around the given good solution. The computational results have proved that the proposed hybrid algorithm is efficient and effective for solving FJSP, especially for the problems with large scale.
Keywords
- Flexible
- job
- shop
- scheduling
- problem;
- Particle
- swarm
- optimization;
- Tabu
- search
- algorithm;
- makespan
How to Cite
Jun-qing Li, Quan-ke Pan, Sheng-xian Xie, Bao-xian Jia, and Yu-ting Wang, "A Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Flexible Job-Shop Scheduling Problem," International Journal of Computer Theory and Engineering, vol. 2, no. 2, pp. 189-194, 2010. https://doi.org/10.7763/IJCTE.2010.V2.139
Copyright & License
Copyright © 2010 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).