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浙江大学学报(工学版)
JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE)
    
Truck scheduling in concentrative reimbursement centre under B2C E-business environment
ZHANG Xin-yan, ZHOU Jian, LIN Ting
School of Mechanical Engineering，Tongji University, Shanghai 201800, China
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Abstract  

In a business to customer (B2C) E-business concentrative reimbursement centre,the multiple inbound and outbound truck-door assignment problem makes its truck scheduling problem much more complicated. A two-stage truck-door assignment model was proposed with given inbound truck-door and outbound truck-door assignment strategies  to solve the truck scheduling problem with considering truck-door assignment simultaneously. And it was applied to establish a mathematical model with the  target function to minimize makespan. Genetic algorithm was integrated to optimize the sequence of inbound trucks. Set experiments under different numbers of inbound and outbound trucks, doors and goods types, computed random solutions and best solutions by Genetic algorithm, then comparatively analyzed these solutions. Experimental results indicated that the optimal solutions could be given in 60s if  numbers of trucks were no more than 160. Computing time increases with the increase of  numbers of trucks and types of goods. Compared with random solutions, genetic algorithm gives solutions that has been improved to a certain degree．

Published: 28 August 2015
CLC:  F 224  
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Cite this article:

ZHANG Xin-yan, ZHOU Jian, LIN Ting. Truck scheduling in concentrative reimbursement centre under B2C E-business environment. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2015, 49(3): 598-604.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2015.03.028     OR     http://www.zjujournals.com/eng/Y2015/V49/I3/598


B2C电商环境下集中式退货中心的车辆调度

B2C电商环境下集中式退货中心的多出入库车门分配问题增加了车辆调度的复杂度.为解决同时考虑车门分配的集中式退货中心的车辆调度问题,分别给定出入库车门分配策略,提出一种2阶段出入库车门的分配模型.结合此模型以最小化最大完工时间为目标建立数学模型,并结合遗传算法求解最优入库车辆调度序列.设置不同规模的出入库车辆、门以及货物种类进行对比实验,求取随机解和遗传算法得出的最优解,并对2种结果进行对比分析,实验结果表明：在出入库车辆数不超过160的情况下,该模型能够在60 s内给出最优解,且求解时间随着出入库车辆规模和货物种类的增加而增大,遗传算法得出的最优解相对随机解均有一定的改进程度.

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