2E-VRP-SCS Data

Inspired by the lane-sharing phenomenon in the city logistics practices, a concept named the “sharing-lane crossdock satellite” (SCS) is introduced. We introduce the two-echelon vehicle routing problem with SCSs (2E-VRP-SCS). On the first echelon, 1st-echelon vehicles depart from the city distribution center (CDC) to serve SCSs. At authorized time windows, 1st-echelon vehicles can park at SCSs for cargo transshipment between vehicles. On the second echelon, 2nd-echelon vehicles receive cargoes to service customers. SCSs are used to perform the direct transshipment that is defined as moving cargoes directly from 1st-echelon vehicles to 2nd-echelon vehicles, with no storing. Each SCS has several time windows. The 2E-VRP-SCS network includes one CDC, a number of SCSs, a number of customers, and arcs. A homogeneous fleet of 1st-echelon vehicles is available at the CDC. Second-echelon vehicles departing from each SCS serve customers. At SCSs, there is a constant transshipment speed of cargoes being moved from 1st-echelon vehicles to 2nd-echelon vehicles, and the transshipment speed is determined by the cargo volume per hour. At a time window of one SCS, “the available transshipment capacity” = “the transshipment speed” × “the remaining time of the time window”. In a route, a vehicle can visit an SCS or one customer at most once, and constraints on route duration must be respected. At a time window of an SCS, there parks no more than one 1st-echelon vehicle. Direct transshipment is considered a one-to-one operation. The 2E-VRP-SCS objective is to minimize the vehicle working time. We design 35 small-scale instances. The number (NumS) of included SCSs is 1, 2 or 3. The number (NumC) of customers is 5, 6, 8, 9 or 10. Each small-scale instance is named by S-NumS-NumC-No. (No. is 1, 2, 3, 4 or 5). The network is abstracted on a graph with a grid of 1 km. The CDC is located at the center node of the graph. Other nodes are randomly selected to act as SCS and customer locations. Customer demand is randomly estimated. The whole time window of SCS m is confirmed beforehand. Large-scale instances are designed by referring to practical data. We observe the situation of traffic flows on some roads on several working days. Several lane-spaces are empirically chosen to make up the SCS set. The included SCSs are randomly chosen from the SCS set. The distance between any two nodes on the first echelon is calculated through the latitudes and longitudes of nodes. We supplement some data by the method of generating small-scale instances. We design 42 large-scale instances that are denoted as L-NumS-NumC-No. (No. is 1, 2 or 3). Of the large-scale instances, NumS is 5, 10, 20 or 30. NumC is 50, 75, 100, 150, 200, 250, 300, 400, 500 or 600, which is chosen by referring to NumS.

本研究受城市物流实践中的共享车道现象启发，提出了共享车道型交叉站台卫星点（sharing-lane crossdock satellite，SCS）这一概念，并构建了带SCS的两级车辆路径问题（two-echelon vehicle routing problem with SCSs，2E-VRP-SCS）。在第一层级，一级车辆从城市配送中心（city distribution center，CDC）出发，前往服务各SCS。在指定的时间窗内，一级车辆可停靠于SCS以完成车辆间的货物中转。在第二层级，二级车辆接收货物后为客户提供配送服务。SCS用于执行直接中转作业，即直接将货物从一级车辆转运至二级车辆，全程无需仓储。每个SCS均设有多个时间窗。2E-VRP-SCS网络包含1个CDC、若干SCS、若干客户节点以及弧段。CDC配备有同质化的一级车辆车队。从各SCS出发的二级车辆负责为客户提供配送服务。在SCS处，货物从一级车辆转运至二级车辆的中转速率恒定，该速率由每小时的货物吞吐量决定。在单个SCS的某一时间窗内，“可用中转容量”=“中转速率”ד该时间窗的剩余时长”。在一条配送路径中，车辆最多仅可访问1个SCS或1个客户节点一次，且需遵守路径时长约束。在单个SCS的某一时间窗内，停靠的一级车辆数量不得超过1台。直接中转作业为一对一操作。2E-VRP-SCS的优化目标为最小化车辆总作业时长。本研究共设计35个小规模算例，其中SCS的数量（NumS）为1、2或3，客户数量（NumC）为5、6、8、9或10。每个小规模算例的命名格式为S-NumS-NumC-No.（其中No.取值为1、2、3、4或5）。研究中的网络基于1km网格的图进行抽象构建，CDC位于该图的中心节点，其余节点通过随机选取作为SCS或客户节点。客户配送需求为随机生成的估算值。各SCS m的完整时间窗均预先设定。大规模算例的设计参考了实际运营数据，研究人员统计了多个工作日内部分道路的交通流量状况，并通过经验选取若干车道空间以构建SCS候选集，再从该候选集中随机选取所需的SCS。第一层级任意两节点间的距离通过节点的经纬度计算得出，部分数据通过生成小规模算例的方法进行补充。本研究共设计42个大规模算例，命名格式为L-NumS-NumC-No.（其中No.取值为1、2或3）。在大规模算例中，NumS取值为5、10、20或30，NumC取值为50、75、100、150、200、250、300、400、500或600，具体取值参考NumS进行匹配。

搜集汇总

背景与挑战

背景概述

该数据集专注于两阶段车辆路径问题(2E-VRP-SCS)，包含小规模和大规模实例，用于研究共享车道交叉码头卫星(SCS)在物流中的路径优化应用，旨在最小化车辆工作时间。