| 面向电商终端物流配送的电动车配置与路径集成优化 |
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| 引用本文： | 李杰,赵旭东,王玉霞,CHU Chao-hsien.面向电商终端物流配送的电动车配置与路径集成优化[J].运筹与管理,2018,27(10):23-30. |
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| 作者姓名： | 李杰 赵旭东 王玉霞 CHU Chao-hsien |
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| 作者单位： | 1.河北工业大学经济管理学院,天津 300401; 2.美国宾州州立大学信息学院,美国 宾州 16802 |
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| 基金项目： | 国家社科基金资助项目(16FGL014);河北省自然科学基金资助项目(G2014202148) |
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| 摘 要： | 在电子商务终端物流配送方面,存在能力与需求的矛盾。一方面,电动车存在货物容量约束和电池电量约束,配送能力有限;另一方面,一个物流配送点需要为众多的消费者进行门到门的配送,配送任务繁重。针对电子商务环境下终端物流配送规模大、电动车货物容量和行驶里程有限的问题,建立电商终端物流配送的电动车配置与路径规划集成优化模型,并提出一种基于临近城市列表的双策略蚁群算法,实现物流配送电动车辆配置与配送路径集成优化。该模型以电动车辆数最少和总路径最短为目标,以电动车货物容量和电池续航里程为约束,是带容量的车辆路径问题的进一步扩展,属于双容量约束路径规划问题。双策略蚁群算法在货物容量和续航里程的约束下,将蚁群搜索策略分为两类,即基于临近城市列表的局部搜索策略和全局搜索策略,在提高搜索效率的同时防止陷入局部优化。最后,通过阿里巴巴旗下菜鸟网络科技有限公司在上海的30组真实配送数据进行了测试,验证双策略蚁群算法显著优于一般蚁群算法。
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| 关 键 词： | 电商终端配送 双容量约束车辆路径问题 蚁群算法 临近城市列表 双策略蚁群算法 |
| 收稿时间： | 2017-10-11 |
Integrated Optimization of Electric Vehicle Allocation & Routing forLarge Scale E-Commerce Terminal Logistics Distribution |
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| LI Jie,ZHAO Xu-dong,WANG Yu-xia,CHU Chao-hsien.Integrated Optimization of Electric Vehicle Allocation & Routing forLarge Scale E-Commerce Terminal Logistics Distribution[J].Operations Research and Management Science,2018,27(10):23-30. |
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| Authors: | LI Jie ZHAO Xu-dong WANG Yu-xia CHU Chao-hsien |
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| Affiliation: | 1.School of Economics and Management, Hebei University of Technology, Tianjin 300401, China; 2.College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA 16802, USA |
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| Abstract: | There is contradiction between vehicle capacity and delivery demand in e-commerce terminal logistics distribution. On the one hand, electric vehicles have limited distribution capacity, which is caused by the constraint of cargo capacity and battery power. On the other hand, a logistics distribution station needs to deliver packages door to door for a large number of consumers, and the distribution task is heavy. Therefore, we develop an integrated optimization model for electric vehicle allocation and routing in e-commerce terminal logistics distribution with the large scale delivery tasks, the limitations of cargo capacity and electric vehicle battery power. In addition, we propose double strategy ant colony algorithm based on near-city list for solving the proposed model. The objective of this model is to minimize the number of vehicles and the shortest path of the electric vehicle. The constraints are the electric vehicle battery power and cargo capacity. It is an extension of a vehicle routing problem with capacity, which belongs to the double capacitated vehicle routing problem. The double strategy ant colony algorithm divides the ant colony search strategy into local search and global search under the constraints of the cargo capacity and battery power. Finally, we test the model and algorithm with 30 real distribution datasets from Alibaba Group Cainiao Company in Shanghai. It proves that the double strategy ant colony algorithm is significantly better than the general ant colony algorithm. |
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| Keywords: | e-commerce terminal logistics distribution double capacitated vehicle routing problem ant colony algorithm near-city list double strategy ant colony algorithm |
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