%0 Journal Article
%T Hybrid particle-swarm optimization for multi-objective flexible job-shop scheduling problem<br>混合粒子群算法求解多目标柔性作业车间调度问题
%A ZHANG Jing
%A WANG Wan-liang
%A XU Xin-li
%A JIE Jing
%A <br>张静
%A 王万良
%A 徐新黎
%A 介婧
%J 控制理论与应用
%D 2012
%I 
%X 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.
%K particle swarm optimization
%K multi-objective optimization
%K flexible job-shop scheduling problem
%K Baldwinian learning mechanism<br>粒子群
%K 多目标优化
%K 柔性作业车间调度问题
%K Baldwinian学习策略
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=A56F1C7481D47408BFABFFB207139613&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=B31275AF3241DB2D&sid=EB8E83807F36F05B&eid=D7F3FD6D87AEF622&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=0