关键词: 流水车间, 批处理机, 微粒群算法, 变邻域搜索

Abstract: An approach based on swarm intelligence is presented to solve the problem of scheduling tasks on flow-shop with batch processing machines. According to the characteristics of the problem under study, a method based on Palmer and Best Fit heuristic algorithm is developed to form batches. Moreover, an improved Particle Swarm Optimization（PSO） algorithm is presented to sequence the obtained batches. In PSO, the NEH heuristic is employed to improve the quality of the initial population. In order to enhance the search capabilities of the proposed algorithm, a variable neighborhood searching is performed for each iteration before the global best position is updated. The experimental results show that the proposed algorithm has a better effectiveness than the standard PSO algorithm and the NEH heuristic.

Key words: flow-shop, batch processing machines, Particle Swarm Optimization（PSO）, variable neighborhood search