[1]陈伟达,高楠.再制造系统能力约束下拆卸批量计划优化方法[J].东南大学学报(自然科学版),2009,39(5):1081-1086.[doi:10.3969/j.issn.1001-0505.2009.05.042]
 Chen Weida,Gao Nan.Optimization methods for disassembly scheduling problems with capacity constrains in remanufacturing system[J].Journal of Southeast University (Natural Science Edition),2009,39(5):1081-1086.[doi:10.3969/j.issn.1001-0505.2009.05.042]
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再制造系统能力约束下拆卸批量计划优化方法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
39
期数:
2009年第5期
页码:
1081-1086
栏目:
经济与管理
出版日期:
2009-09-20

文章信息/Info

Title:
Optimization methods for disassembly scheduling problems with capacity constrains in remanufacturing system
作者:
陈伟达 高楠
东南大学经济管理学院, 南京 210096
Author(s):
Chen Weida Gao Nan
School of Economics and Management, Southeast University, Nanjing 210096, China
关键词:
拆卸批量计划 能力约束 遗传算法 再制造系统
Keywords:
disassembly scheduling problem capacity constrains genetic algorithm remanufacturing system
分类号:
F270
DOI:
10.3969/j.issn.1001-0505.2009.05.042
摘要:
针对再制造系统中能力约束下的拆卸批量计划问题,应用两阶段启发式遗传算法进行了优化求解.首先对再制造产品结构进行了描述,建立了再制造系统中能力约束下的拆卸批量计划优化模型; 其次在不考虑能力约束情况下应用遗传算法求解出初始的拆卸批量计划,其中,染色体编码采用拆卸决策变量来表示,同时对适应度函数进行了线性变换,设计了具有自适应的交叉概率和变异概率; 然后应用转移算法对初始得到的批量计划进行了修正,使其符合拆卸能力的约束.大量随机算例的仿真实验说明所提出的算法不论在寻找最优解方面还是在求解速度和稳定性方面,都要大大优于精确算法,能够较好地解决实际生产中面临的拆卸批量计划问题.
Abstract:
This paper focuses on optimization methods for disassembly scheduling problems with capacity constrains in remanufacturing system, and a two-stage heuristic genetic algorithm is developed. Firstly, the structure of remanufacturing product is described and an optimization model for the disassembly scheduling problem in remanufacturing system with capacity constrains considered is established, then the genetic algorithm is applied to solve the initial scheme without considering the capacity constrains. The chromosome code is displayed by decision variables and the adaptability function is then transformed linearly. Moreover, the adaptive crossover probability and the mutation probability are designed. Then, the shifting procedure is applied to modify the initial scheme to assure the capacity feasibility. The results of computational experiments which were carried out on randomly generated tests show that the proposed algorithm is better than exact-solution algorithm both on computational efficiency and stability, and it can provide a more effective solution for the disassembly scheduling problem in practice.

参考文献/References:

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备注/Memo

备注/Memo:
作者简介: 陈伟达(1965—),男,博士,教授, cwd@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(70971022).
引文格式: 陈伟达,高楠.再制造系统能力约束下拆卸批量计划优化方法[J].东南大学学报:自然科学版,2009,39(5):1081-1086. [doi:10.3969/j.issn.1001-0505.2009.05.042]
更新日期/Last Update: 2009-09-20