运筹与管理 ›› 2011, Vol. 20 ›› Issue (5): 63-72.

• 理论分析与方法探讨 • 上一篇    下一篇

震后应急物流系统中带时间窗的模糊动态LRP

王绍仁1, 马祖军2   

  1. 1.华侨大学 经济与金融学院,福建 泉州 362021;
    2.西南交通大学 经济管理学院,四川 成都 610031
  • 收稿日期:2010-04-22 出版日期:2011-10-25
  • 作者简介:王绍仁(1979-),男,壮族,云南文山人,博士,讲师,主要从事物流系统优化、应急物流等方面的研究;马祖军(1974-),男,浙江开化人,博士,教授,博导,主要从事物流与供应链管理、应急管理等方面的研究。
  • 基金资助:
    国家自然科学基金重大研究计划培育项目(90924012);国家自然科学基金项目(70771094);高等学校博士学科点专项科研基金项目(20090184110029);中国博士后科学基金项目(20090450637);中央高校基本科研业务费专项资金资助项目(SWJTU09CX066)

Fuzzy Dynamic LRP with Time Windows in Post-Earthquake Emergency Logistics Systems

WANG Shao-ren1, MA Zu-jun2   

  1. 1. College of Economics and Finance of Huaqiao University; Quanzhou 362021, China;
    2. School of Economics and Management of Southwest Jiaotong University; Chengdu 610031, China
  • Received:2010-04-22 Online:2011-10-25

摘要: 针对震后应急物流系统中多层次设施定位-运输路线安排问题(LRP),考虑系统中的动态性、时效性、路网连通性、需求不确定性等特点,建立了一个带时间窗的模糊动态LRP优化模型,据此进行救援过程中不同周期灾区外围应急物资集散点和灾区应急配送中心的定位以及应急物资运输路线安排的联合决策。针对该模型的特点,提出了一种基于动态规划的改进遗传算法,为防遗传算法过早收敛问题,使用了随机遍历抽样法、重组策略和变化变异率法,并通过特定实值编码、罚函数法和物资需求量分割策略处理模型中的约束条件。最后,通过算例分析验证了该模型和算法的有效性。

关键词: 应急物流, 模糊优化, 改进遗传算法, 时间窗, 定位-运输路线安排问题

Abstract: The multi-echelon Location-Routing Problem(LRP)in post-earthquake emergency logistics systems is studied. A fuzzy dynamic optimization model for LRP is developed by considering dynamic characteristics, timeliness, connectivity of road networks and uncertain demand in the system. Then the joint decision of locating distributing centers of relief commodities around the disaster area and relief distribution centers in the disaster area, as well as scheduling the routes of emergency vehicle in each period during relief process can be made. According to the characteristics of the model, an improved genetic algorithm(GA)based on dynamic programming is proposed. To overcome the premature problems of GA, stochastic selection, regrouped strategy and changing mutation probability are used, and a special real-valued coding scheme, punishment function method and demand split strategy are adopted to deal with restrictions in the model. Finally, the validity of the model and algorithm is demonstrated by a numerical example.

Key words: emergency logistics, fuzzy optimization, improved genetic algorithm, time windows, location-routing problem

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