基于混合差分进化算法和熵权TOPSIS的多目标(Q,r)库存模型  

Multi-objective (Q,r) model based on hybrid differential evolution algorithm and entropy-based TOPSIS

作　　者：王林[1] 顿彩霞[1] 杨锐[1] 

机构地区：[1]华中科技大学管理学院,武汉430074

出　　处：《控制与决策》2011年第12期1913-1916,1920,共5页Control and Decision

基　　金：国家自然科学基金项目(70801030);教育部人文社会科学研究青年基金项目(11YJC630275)

摘　　要：讨论随机需求环境下的多目标库存控制问题,构建以成本、缺货率和缺货量3个指标最小化为准则的多目标(Q,r)库存模型.为了求出决策者需要的Pareto前沿,设计了基于遗传和差分进化算法的混合智能算法来产生非支配解,进而利用基于熵权的TOPSIS方法对最优解进行排序,此结果可为管理者提供有益的决策参考.Based on the analysis of inventory multi-objective planning problems under random demand, a multi-objective （Q, r） model is constructed under three criteria of minimization of the annual expected total relevant cost, the annual expected frequency of stock-out occasions, and the annual expected number of stock-outs quality. To find Pareto front, a hybrid algorithm is designed by using generation algorithm（GA） and differential evolution algorithm to generate non-dominated solutions. Then, an entropy theory-based technique for order preference by similarity to ideal solution is adopted to sort the non-dominated solutions, which gives useful advices for decision makers.

关 键 词：库存控制 多目标优化 混合差分进化算法 熵权TOPSIS 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]