| 基于混合粒子群算法求解多目标混流装配线排序 |
| |
| 引用本文： | 刘炜琪,刘琼,张超勇,邵新宇.基于混合粒子群算法求解多目标混流装配线排序[J].计算机集成制造系统,2011(12). |
| |
| 作者姓名： | 刘炜琪 刘琼 张超勇 邵新宇 |
| |
| 作者单位： | 华中科技大学数字制造装备与技术国家重点实验室;湖北工业大学机械工程学院; |
| |
| 基金项目： | 国家自然科学基金重点资助项目(51035001); 国家863计划资助项目(2009AA043301)~~ |
| |
| 摘 要： | 针对生产调度中的多目标混流装配线排序问题,建立以最小化超载时间、产品变化率与总切换时间为优化目标的数学模型,并提出一种改进的多目标粒子群算法求解。该算法采用基于工件的编码方式,并提出新的解码方法;应用Pareto排序和小生境数评价个体,在此基础上形成了一种新的适应度函数。在个体最优解的更新中,为避免最优解丢失,对非支配粒子与支配粒子采用差异化方法更新。此外,运用两种策略解决粒子群算法过早收敛的问题:在个体最优解的更新中引入模拟退火思想,并将全局最优解的选择扩大到整个种群。通过数值算例研究了算法的收敛性、分布性和执行效率,结果表明了所提算法的优越性。
|
| 关 键 词： | 混流装配线 多目标排序 多目标优化 粒子群算法 Pareto排序 模拟退火算法 数学模型 |
Hybrid particle swarm optimization for multi-objective sequencing problem in mixed model assembly lines |
| |
| LIU Wei-qi,LIU Qiong,ZHANG Chao-yong,SHAO Xin-yu.Hybrid particle swarm optimization for multi-objective sequencing problem in mixed model assembly lines[J].Computer Integrated Manufacturing Systems,2011(12). |
| |
| Authors: | LIU Wei-qi LIU Qiong ZHANG Chao-yong SHAO Xin-yu |
| |
| Affiliation: | LIU Wei-qi1,2,LIU Qiong1,ZHANG Chao-yong1,SHAO Xin-yu1(1.State Key Laboratory of Digital Manufacturing Equipment and Technology,Huazhong University of Science and Technology,Wuhan 430074,China,2.Mechanical Engineering College,Hubei University of Technology,Wuhan 430068,China) |
| |
| Abstract: | Aiming at the multi-objective sequencing problem in mixed model assembly lines,a mathematical model was proposed with the optimization objectives of minimizing total utility work,total production rate variation and total setup cost.Besides,an improved Multi-Objective Particle Swarm Optimization(MOPSO) was proposed to solve the model.In the algorithm,job-based coding was introduced and a new decoding method was put forward.Pareto ranking and niche count were employed to evaluate an individual,and a new fitne... |
| |
| Keywords: | mixed model assembly line multi-objective sequencing multi-objective optimization particle swarm optimization Pareto ranking simulated annealing algorithm mathematical models |
| 本文献已被 CNKI 等数据库收录！ |
|
