基于遗传算法的再制造逆向物流网络随机选址模型  

A Stochastic Location Model for Remanufacturing Reverse Logistics Network Based on Genetic Algorithm

作　　者：孙浩[1] 

机构地区：[1]东南大学经济管理学院,江苏南京210096

出　　处：《信息与控制》2009年第2期223-228,233,共7页Information and Control

摘　　要：针对再制造逆向物流网络不确定性高的特点,建立了一个混合整数非线性规划(mixed integer nonlinear programming,MINLP)的随机选址模型.模型中将回收中心和再制造工厂分别看作具有M/M/l和M/M/c特征的随机排队系统,考虑了废旧产品在系统中的逗留时间和库存费用.然后提出一种双层遗传算法进行求解:用外层遗传算法搜索0-1整型变量的可行组合,用内层遗传算法解决剩余的运输子问题.最后通过一个算例说明了模型和算法的有效性.Based on the characteristic of high uncertainty in remanufacturing reverse logistics networks, a stochastic location model for mixed integer nonlinear programming （MINLP） is built, in which return centers and remanufacturing factories are seen as stochastic queuing systems that are of characteristics of M/M/1 and MIMIc respectively. Cycle time and inventory costs of the old products in the system are taken into account. Then a bi-level genetic algorithm is proposed. The feasible combinations of 0-1 binary variables are searched by the outer genetic algorithm and the remaining transportation sub-problems are solved by the inner genetic algorithm. Finally, an example is given to prove the validity of the model and algorithm.

关 键 词：再制造 设施选址 排队论 双层遗传算法 

分 类 号：F252.9[经济管理—国民经济]