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B2C物流配送网络双目标模糊选址模型与算法
引用本文:张晓楠,范厚明,李剑锋.B2C物流配送网络双目标模糊选址模型与算法[J].系统工程理论与实践,2015,35(5):1202-1213.
作者姓名:张晓楠  范厚明  李剑锋
作者单位:大连海事大学 交通运输管理学院, 大连 116026
基金项目:国家自然科学基金(70801007);辽宁省科学技术计划项目(2012401005);大连市科学技术计划项目(2008D12ZC105)
摘    要:基于B2C"配送-退换同时"的物流模式及配送业务特点,集成设施选址-分配和路线优化,研究模糊需求下的B2C物流设施选址问题.针对选址-分配的模糊性和静态性、配送的确定性和动态性特征,以物流总费用为主目标函数,以配送中心流通费用、车辆派遣费用、配送费用总和为子目标函数,建立了有配送中心容量静态约束和车辆动态负载量约束的双目标模糊选址模型,设计了嵌入随机算法和禁忌搜索算法的遗传算法求解.选取合适的测试算例验证了算法的有效性,探讨了客户需求模糊区间宽度和商品退换率对物流选址结果和各项费用值的影响.实验结果表明,所设计的算法对解决这类复杂问题合理有效.客户需求模糊区间宽度与车辆利用率和车辆路线总长的波动区间、平均车辆路线总长度、配送费用正相关,且宽度较窄时,选址结果、车辆派遣费用和配送中心流通费用不变,超过一定范围,选址结果、车辆派遣费用和配送中心流通费用改变.商品退换率与流通费用和物流总费用正相关,但不会影响选址结果和其他费用.

关 键 词:B2C物流网络  双目标模糊选址模型  路线优化  遗传算法  禁忌搜索  随机算法  
收稿时间:2013-10-21

Bi-objective fuzzy location model and algorithm for the design of logistics distribution network in B2C e-commerce
ZHANG Xiao-nan,FAN Hou-ming,LI Jian-feng.Bi-objective fuzzy location model and algorithm for the design of logistics distribution network in B2C e-commerce[J].Systems Engineering —Theory & Practice,2015,35(5):1202-1213.
Authors:ZHANG Xiao-nan  FAN Hou-ming  LI Jian-feng
Institution:School of Transportation Management, Dalian Maritime University, Dalian 116026, China
Abstract:Based on the B2C logistics model with "simultaneous delivery and return" and the distribution characteristics, we study B2C logistics facility location problem with fuzzy demands, in which the facility location and allocation problem (LAP) and the vehicle routing problem (VRP) are observed simultaneously. Focused on the fuzziness and statics in the LAP, as well as determinacy and dynamics in the VRP, a bi-objective fuzzy location model, with static capacity constraints of the depot and fluctuating load constraints of the vehicle, is proposed. The model takes total costs as main-objective and the sum of circulation costs, fixed costs of employing vehicles and travel costs as sub-objective. Then a genetic algorithm embedded stochastic simulation algorithm and tabu search algorithm is developed. To show the performance of the proposed algorithm and to analyze the influences of the interval width of fuzzy demands and return rate on the final solutions, numerical experiments are carried out and associated results are compared. The results show that the proposed algorithm is effective for solving the complex problem. Moreover, the wider the interval of fuzzy demand becomes, the wider the fluctuation intervals of both vehicle utilization and travel length are, and the greater the mean travel length and travel costs are. In using fuzzy demands with a narrow width, location solution, fixed costs of employing vehicles, and circulation costs all remain constant. Otherwise, they are updated. The return rate doesn't affect the location solution or other costs but the circulation costs and the total costs. The circulation costs and the total costs are more in line with the increase of the return rate.
Keywords:B2C logistics network  bi-objective fuzzy location model  vehicle routing problem  genetic algorithm  tabu search algorithm  stochastic simulation algorithm
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