引用本文: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.[点击复制]
ZHANG Jing,WANG Wan-liang,XU Xin-li,JIE Jing.Hybrid particle-swarm optimization for multi-objective flexible job-shop scheduling problem[J].Control Theory and Technology,2012,29(6):715~722.[点击复制]
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
摘要点击 3228  全文点击 2435  投稿时间：2011-06-29  修订日期：2012-01-05
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DOI编号  10.7641/j.issn.1000-8152.2012.6.CCTA110745
  2012,29(6):715-722
中文关键词  粒子群  多目标优化  柔性作业车间调度问题  Baldwinian学习策略
英文关键词  particle swarm optimization  multi-objective optimization  flexible job-shop scheduling problem  Baldwinian learning mechanism
基金项目  国家自然科学基金资助项目(60874074, 61070043); 浙江省自然科学基金资助项目(Y1090592); 中国博士后科学基金资助项目(20090451486).
作者单位E-mail
张静 浙江工业大学 计算机科学与技术学院
浙江工业大学 信息工程学院 		bay_229@163.com 
王万良* 浙江工业大学 计算机科学与技术学院 wwl@zjut.edu.cn 
徐新黎 浙江工业大学 计算机科学与技术学院  
介婧 浙江工业大学 计算机科学与技术学院  
中文摘要
      柔性作业车间调度问题是生产管理领域和组合优化领域的重要分支. 本文提出一种基于Pareto支配的混合粒子群优化算法求解多目标柔性作业车间调度问题. 首先采用基于工序排序和机器分配的粒子表达方式, 并直接在离散域进行位置更新. 其次, 提出基于Baldwinian学习策略和模拟退火技术相结合的多目标局部搜索策略, 以平衡算法的全局探索能力和局部开发能力. 然后引入Pareto支配的概念来比较粒子的优劣性, 并采用外部档案保存进化过程中的非支配解. 最后用于求解该类问题的经典算例, 并与已有算法进行比较, 所提算法在收敛性和分布均匀性方面均具有明显优势.
英文摘要
      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.