考虑多充电桩排队和时间窗的电动货车路径规划

胡路; 乐诗彤; 朱娟秀

doi:10.3969/j.issn.0258-2724.20230084

考虑多充电桩排队和时间窗的电动货车路径规划

doi: 10.3969/j.issn.0258-2724.20230084

胡路^{1, 2,},
乐诗彤^{1, 2},
朱娟秀^{2, 3, ,}

1.
西南交通大学交通运输与物流学院，四川成都 610031
2.
西南交通大学综合交通大数据应用技术国家工程实验室，四川成都 610031
3.
西华大学管理学院，四川成都 610039

基金项目: 国家自然科学基金项目（62203367）

详细信息

作者简介:
胡路（1985—），男，副教授，博士，研究方向为共享交通和城市轨道交通，E-mail：hulu@swjtu.edu.cn

通讯作者:
朱娟秀（1989—），女，讲师，博士，研究方向为物流与交通系统建模与优化，E-mail：1220190011@mail.xhu.edu.cn

中图分类号: U492
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- 文章访问数: 157
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- 被引次数: 0
出版历程
- 收稿日期: 2023-03-02
- 修回日期: 2023-07-02
- 网络出版日期: 2024-11-07

Electric Truck Route Planning Considering Multiple Charging Pile Queues and Time Windows

HU Lu^{1, 2
,},
LE Shitong^{1, 2},
ZHU Juanxiu^{2, 3
, ,}

1.
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China
2.
National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 610031, China
3.
School of Management, Xihua University, Chengdu 610039, China

摘要

摘要:
在带时间窗的电动货车路径规划问题（EVRPTW）中，电动货车（EV）在前往充电站充电时可能需要排队. 为研究不同充电站配置方案对车辆路径和系统性能的影响，首先构建排队模型，刻画充电站中的排队现象；在EVRPTW基础上，综合考虑电量和流量约束，建立路径优化模型，并将充电站排队模型嵌入其中；优化目标包括最小化车辆耗电成本、司机工资、时间窗惩罚成本、充电桩总成本；为求解该模型，提出一种结合节约里程（C-W）和改进大邻域搜索（LNS）的混合启发式算法，其中，充电站的系统性能指标采用递归算法获得. 18组实验结果表明：同步增加充电桩数量可将车辆单次充电的平均排队时间控制在1～5 min，并有效减少2.6%～21.0%的总成本；增加充电站数量可缩短排队时间，但会增加整体路径总成本；当客户时间窗较短或服务时间较长时，充电桩数量变化对时间窗满足的影响更为显著.
- 物流 /
- 电动货车 /
- 充电站 /
- 混合启发式算法 /
- 递归算法
Abstract:
In the electric truck route planning problem with time windows, electric trucks may need to queue up when they go to charging stations for charging. To study the impact of different charging station configuration schemes on vehicle route planning and system performance, the queuing model was first built to describe the queuing phenomenon at charging stations. Then, a route optimization model was established by considering the power and flow constraints based on the electric truck route planning problem with time windows, with the queuing model of charging stations embedded into the optimization model. The optimization goals included minimizing vehicle power consumption costs, driver’s wages, penalty costs of time windows, and total costs of all charging piles. To solve the model, a hybrid heuristic algorithm combining mileage saving (C-W) and improved large neighborhood search (LNS) was designed, and the system performance metrics of charging stations were obtained by a recursive algorithm. 18 sets of experimental results show that increasing the number of charging piles simultaneously can control the average queuing time of a vehicle for charging within 1–5 minutes and effectively reduce the total cost by 2.6%–21.0%; increasing the number of charging stations can reduce the queuing time but increase the total cost of the entire route; when the customer time window is short, or the service time is long, the change in the number of charging piles has a more significant impact on the satisfaction of the time window.
- logistics /
- electric truck /
- charging station /
- hybrid heuristic algorithm /
- recursive algorithm

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图 1 算法流程

Figure 1. Algorithm flowchart

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图 2 不同充电桩数量的影响

Figure 2. Impact of different numbers of charging piles

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图 3 不同充电站数量的影响

Figure 3. Impact of different numbers of charging stations

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图 4 优化解路径对比

Figure 4. Optimized solution path comparison

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表 1 符号说明

Table 1. Symbol descriptions

符号	说明	符号	说明
${d_{i,j}}$	顶点 $i$ 到顶点 $j$ 的行驶距离	${c_{\mathrm{s}}}$	所有充电站安装充电桩的总费用
${t_{i,j}}$	顶点 $i$ 到顶点 $j$ 的行驶时间	${x_{i,j}}$	当顶点 $i$ 与顶点 $j$ 在有向图中的路径被访问时为1，否则为0
${q_i}$	客户点 $i$ 的需求	$y_i^{\mathrm{a}}$	到达顶点 $i$ 时剩余电量
${s_i}$	客户点 $i$ 的服务时间	$y_i^{\text{d}}$	离开顶点 $i$ 时剩余电量
${e_i}$	客户点 $i$ 的最早服务时间	${v_i}$	到达顶点 $i$ 时的迟到时长
${l_i}$	客户点 $i$ 的最晚服务时间	${a_i}$	到达顶点 $i\;（i \in {V_{0,n + 1}}）$ 的时间
$G_0$	车辆装载容量	${f_i}$	在返回仓库之前，以顶点 $i$ 为最后访问节点的路线所花费的总时间
$Q$	车辆电池容量	${u_i}$	抵达顶点 $i$ 时的剩余货物容量
$g$	电池充电速率	${w_{i,k}}$	顶点 $i\;（i \in F）$ 进行第 $k$ 次所服务车辆需要的等待时间
$h$	电池单位距离消耗速率	${{\textit{z}}_{i,k}}$	顶点 $i\;（i \in F）$ 进行第 $k$ 次所服务车辆需要的充电时间
${c_{\mathrm{e}}}$	每单位距离消耗的电量成本	${a_{i,k}}$	顶点 $i\;（i \in F）$ 第 $k$ 次服务的车辆到达时间
${c_{\mathrm{p}}}$	每单位时间惩罚成本	$y_i^{\mathrm{a}}({a_{i,k}})$	当车辆到达顶点 $i$ 时间为 ${a_{i,k}}$ 时的剩余电量
${c_{\mathrm{f}}}$	每个顶点的运行成本，包括维护、购置成本以及在客户点工作的成本	${w_{i,k}}( {a_{i,k}} )$	当车辆到达顶点 $i$ 时间为 ${a_{i,k}}$ 时的等待时间
${c_{\mathrm{d}}}$	每单位时间司机工资

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