TDRP-TW instances

Considering the commercial interest and attention on both trucks and drones carried by trucks for last-mile parcel deliveries, we introduce the TDRP-TW. Solving the TDRP-TW necessitates finding the cost-minimizing routes. Each TDRP-TW network includes a number of customers and one depot housing a fleet of truck–drone combinations. The network is denoted by an undirected complete graph in which the nodes represent the depot and customers, and the arcs represent the possible moves of trucks and drones. The depot is the only parcel source. The truck–drone combination includes one truck carrying several drones. All customer demand should be satisfied. Customer demand cannot be split. Customers are classified into two types: truck customer (TC) or drone customer (UC). Each TC should be serviced by one truck, and each UC should be serviced by one drone. The number of drones carried by each truck is assumed to be the same. A vehicle in use may be a single truck or drone, or a truck–drone combination. Each truck–drone combination works in a “paired” modality. A drone can carry several parcels to serve more than one customer. It is assumed that there are enough trucks and drones. Along its route, a truck can dispatch and retrieve the carried drones only at a TC, which is referred to as the satellite. A drone can be retrieved by its truck at a satellite, or can return directly to the depot. We choose the test instances from the Solomon (1987) VRPTW benchmark problems with 100 customers and convert them to TDRP-TW instances. Let nc denote the number of customers included in the TDRP-TW network. The nc customers are classified into TCs and UCs. Parameter p denotes the percentage of UCs in the nc customers, and p = 25%, 50% or 75%. All customer time windows are scale down by the same constant. The demand of a TC or a UC is a random number in the range of (0, 50 kg) or (0, 2.5 kg), respectively. When nc < 100, the converted instances are specified as small- and medium-scale instances. Each of the small- and medium-scale instances is denoted by C“nc”-25, C“nc”-50, or C“nc”-75; and C“nc”-25, C“nc”-50, or C“nc”-75 has the percentage of UCs 25%, 50%, or 75%, respectively. When nc = 100, the converted instances are specified as large-scale instances. Each of the large-scale instances is denoted by “name of VRPTW benchmark instance”-75, which has the percentage of UCs 75%.考虑到货运卡车与卡载无人机用于末端包裹配送的商业价值与关注度，我们提出了TDRP-TW问题。求解TDRP-TW需要找到成本最小化的配送路径。TDRP-TW网络包含若干客户节点与一个车场（depot），车场中配备多组卡载无人机编队。该网络以无向完全图表示，其中节点代表车场与客户，边代表卡车与无人机的可行移动路径。车场是唯一的包裹货源点。卡载无人机编队指一辆卡车搭载多架无人机。所有客户的配送需求均需得到满足，且需求不可拆分。 客户被分为两类：卡车服务客户（Truck Customer, TC）与无人机服务客户（Drone Customer, UC）。每个TC仅可由一辆卡车完成配送服务，每个UC仅可由一架无人机完成配送服务。假设每辆卡车搭载的无人机数量固定且一致。可用载具可为单独卡车、单独无人机，或卡载无人机编队。每组卡载无人机编队以协同配对模式运行。一架无人机可携带多件包裹，为多位客户提供配送服务。 假设车场配备足够数量的卡车与无人机。在配送路径中，卡车仅可在TC节点调度与回收其所搭载的无人机，该类TC节点被称为卫星节点（satellite）。无人机可由所属卡车在卫星节点回收，亦可直接返回车场。 我们从Solomon（1987）提出的带时间窗车辆路径问题（Vehicle Routing Problem with Time Windows, VRPTW）基准测试集（含100个客户）中选取测试实例，并将其转换为TDRP-TW实例。令nc表示TDRP-TW网络中的客户总数，该nc个客户被划分为TC与UC两类。参数p表示UC在所有客户中的占比，p取值为25%、50%或75%。所有客户的时间窗均通过同一常数进行缩放。TC的需求为(0, 50 kg)区间内的随机数，UC的需求为(0, 2.5 kg)区间内的随机数。 当nc < 100时，转换得到的实例被划分为中小型规模实例。每一组中小型规模实例以"C"nc"-25"、"C"nc"-50"或"C"nc"-75"命名，其中"C"nc"-25"、"C"nc"-50"、"C"nc"-75"分别对应UC占比为25%、50%、75%的实例。当nc = 100时，转换得到的实例被划分为大规模实例。每一组大规模实例以"原VRPTW基准测试实例名称"-75命名，其UC占比为75%。

搜集汇总

背景与挑战

背景概述

该数据集是基于Solomon VRPTW基准问题转换而来的TDRP-TW实例集合，专注于卡车和无人机组合的最后一公里包裹配送路径优化问题。数据集包含不同规模（小/中/大）的实例，客户被分类为卡车客户或无人机客户，其中无人机客户比例设定为25%、50%或75%，并调整了客户需求和时间窗参数，用于支持成本最小化路径求解的研究。