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浙江大学学报(工学版)
J4  2011, Vol. 45 Issue (10): 1746-1752    DOI: 10.3785/j.issn.1008-973X.2011.10.008
自动化技术、信息技术     
基于自适应粒子群的产品再制造拆卸规划
徐进, 张树有, 费少梅
浙江大学 机械工程学系,浙江 杭州 310027
Product remanufacture disassembly planning based on
adaptive particle swarm optimization algorithm
XU Jin, ZHANG Shu-you, FEI Shao-mei
Department of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
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摘要：

采用干涉矩阵和拆卸深度矩阵建立拆卸模型,给出可行拆卸序列生成方法,产生了拆卸可行域.设计一种自适应粒子群算法,该算法基于锯齿波提出惯性权重因子周期线性衰减,实现全局与局部搜索的平衡,给出适应变异策略,避免陷入局部最优解.针对拆卸序列规划问题,使用拆卸序列可行链表对粒子编码,构建区段拆卸序列,应用到粒子变异和基于浮动窗口的粒子更新中,保证每次迭代后粒子序列的可行性,避免对不可行序列的判断,提高了求解效率.通过一个典型实例,验证了该方法的有效性.
Abstract:

Depth matrix and disassembly interference matrix were presented to establish disassembly model. A method of generating feasible disassembly sequence was proposed to produce the feasible region of disassembly. An adaptive particle swarm optimization was presented. The inertia weight with cyclical linear attenuation strategy based on sawtooth wave was designed to strike a balance between global and local searching ability, and adaptive mutation was utilized to avoid falling into local optimal solution. The algorithm used feasible disassembly sequence list to get particle code in order to solve the disassembly sequence planning problem. A strategy of generating section feasible disassembly sequence was used in particle mutation and particle updated based on floating window. Then the feasibility of particle sequence list after every iteration was guaranteed to avoid judging the feasibility of particle, and the efficiency of the algorithm was improved. The effectiveness of the algorithm was proved by a typical example.
出版日期: 2011-10-01
:  TP 391.7  
基金资助:

国家“973”重点基础研究发展规划资助项目(2011CB706506)；浙江省科技计划资助项目（2010C31085）；教育部博士点基金资助项目(200803350031)；浙江省自然科学基金重点资助项目(Z1080339).
通讯作者: 张树有,男,教授.     E-mail: zsy@zju.edu.cn
作者简介: 徐进（1985—）,男,硕士生,从事绿色设计、现代优化算法以及计算机图形学的研究.E-mail: xujinzju@163.com
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引用本文:

徐进, 张树有, 费少梅. 基于自适应粒子群的产品再制造拆卸规划[J]. J4, 2011, 45(10): 1746-1752.

XU Jin, ZHANG Shu-you, FEI Shao-mei. Product remanufacture disassembly planning based on
adaptive particle swarm optimization algorithm. J4, 2011, 45(10): 1746-1752.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2011.10.008    或    https://www.zjujournals.com/eng/CN/Y2011/V45/I10/1746

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