多周期多原料的供应链网络成本优化及其混合遗传算法  

Multi-material and multi-cycle cost optimization of supply chain network and hybrid genetic algorithm

作　　者：戴卓[1] 

机构地区：[1]九江学院商学院,江西九江332005

出　　处：《计算机应用研究》2014年第9期2620-2624,共5页Application Research of Computers

基　　金：国家自然科学基金资助项目(71162023)

摘　　要：建立并求解一个基于成本最小的供应链网络模型。与以往研究不同,在该模型中生产一种产品需要至少两种原料,每种原料都可以由备选供应商提供。根据模型的特点,用0、1代表对原材料供应商、工厂和分销中心的选择情况,以MATLAB 7.6为平台,运用Sheffield大学的遗传算法工具箱,将遗传算法与线性规划算法相结合,实现了模型的求解。算例结果表明,给出的染色体编码方案正确,混合遗传算法有效,能解决多周期、多原料的供应链网络成本优化问题。还探讨了需求和距离变化,以及需求随机时对最优成本和最优个体的影响。研究表明,需求变化的影响大于距离变化的影响,需求随机对最优成本和最优个体的影响不大。This paper intended to establish and solve a supply chain network model based on minimum cost. Different from the previous studies, at least two kinds of material were needed in order to produce products in this model. Each material could be supplied by selected alternative suppliers. According to the model, zero or one represented the result of choice about suppliers of raw materials, factories and distribution centers. In order to achieve a solution of the model, it used the genetic algorithm toolbox of Sheffield University, combined genetic algorithm with linear programming algorithm on the platform of MATLAB 7.6. Results of examples show that the proposed chromosome encoding scheme is correct and hybrid genetic algorithm is val- id. Chromosome encoding scheme and hybrid genetic algorithm could solve cost optimization problem of the multi-cycle and multi-material supply chain network. It also discussed the impact of the changes of demands and distance and the random de- mand on the optimal cost and the best individual. Studies have shown that the impact of changes in demand is greater than that of the distance chnn^es. The affect of tha rsndom damnnd on the ontimal cost and the hast individuals is little.

关 键 词：多周期 多原料 供应链网络 成本优化 混合遗传算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]