控制理论与应用2012，Vol.29Issue(6)：715-722,8.
混合粒子群算法求解多目标柔性作业车间调度问题
Hybrid particle-swarm optimization for multi-objective flexible job-shop scheduling problem
摘要
Abstract
Flexible job-shop scheduling is a very important branch in both fields of production management and combinatorial optimization. A hybrid particle-swarm optimization algorithm is proposed to study the mutli-objective flexible job-shop scheduling problem based on Pareto-dominance. First, particles are represented based on job operation and machine assignment, and are updated directly in the discrete domain. Then, a multi-objective local search strategy including Baldwinian learning mechanism and simulated annealing technology is introduced to balance global exploration and local exploitation. Third, Pareto-dominance is applied to compare different solutions, and an external archive is employed to hold and update the obtained non-dominated solutions. Finally, the proposed algorithm is simulated on numerical classical benchmark examples and compared with existing methods. It is shown that the proposed method achieves better performance in both convergence and diversity.关键词
粒子群/多目标优化/柔性作业车间调度问题/Baldwinian学习策略Key words
particle swarm optimization/ multi-objective optimization/ flexible job-shop scheduling problem/ Baldwinian learning mechanism分类
信息技术与安全科学引用本文复制引用
张静,王万良,徐新黎,介婧..混合粒子群算法求解多目标柔性作业车间调度问题[J].控制理论与应用,2012,29(6):715-722,8.基金项目
国家自然科学基金资助项目(60874074,61070043) （60874074,61070043）
浙江省自然科学基金资助项目(Y1090592) （Y1090592）
中国博士后科学基金资助项目(20090451486). （20090451486）
