A hybrid discrete particle swarm optimization algorithm for solving fuzzy job shop scheduling problem

作者：Li Jun qing^*; Pan Yu xia

来源：International Journal of Advanced Manufacturing Technology, 2013, 66(1-4): 583-596.

DOI：10.1007/s00170-012-4337-3

• Fuzzy processing time
• Job shop scheduling problem
• Particle swarm optimization
• Tabu search

In this paper, a hybrid algorithm combining particle swarm optimization (PSO) and tabu search (TS) is proposed to solve the job shop scheduling problem with fuzzy processing time. The object is to minimize the maximum fuzzy completion time, i.e., the fuzzy makespan. In the proposed algorithm, PSO performs the global search, i.e., the exploration phase, while TS conducts the local search, i.e., the exploitation process. The global best particle is used to direct other particles to optimal search space. Therefore, in the proposed algorithm, TS-based local search approach is applied to the global best particle to conduct find-grained exploitation. In order to share information among particles, one-point crossover operator is embedded in the hybrid algorithm. The proposed algorithm is tested on sets of the well-known benchmark instances. Through the analysis of experimental results, the highly effective performance of the proposed algorithm is shown against the best performing algorithms from the literature.

• 出版日期2013-4
• 单位聊城大学