求解柔性Job-shop调度问题的混合粒子群算法

A Hybrid Particle Swarm Optimization Algorithm for Flexible Job-Shop Scheduling Problem

针对柔性Job-shop调度问题,提出了一种混合粒子群算法,该算法对设备分配和工序调度采用不同的编码方法和更新方式,提出了基于设备的初始化算法和基于工件序列的初始化算法来提高PSO初始种群的质量,同时提出了4种不同的邻域结构,分别实现了基于此四种邻域结构的模拟退火搜索算法,将它与粒子群算法进行有效混合来提高粒子群算法的局部搜索能力,实验表明HPSO的有效性.

A hybrid particle swarm optimization algorithm （HPSO） is proposed to solve the flexible job shop scheduling problem.In the algorithm,different encoding methods were proposed for assignment and sequence problem.In order to ensure the legitimacy of code for assignment,the updating formula of particles is changed.In order to improve the efficiency of algorithm,the initialization algorithm based on device and sequence is proposed to improve the quality of the initial population of HPSO.To improve the local search ability of algorithm,four simulated annealing algorithms based on different neighborhood search strategy are proposed and mixed with PSO.The computational results show the effectiveness of the algorithm.