TDRP-TW instances

Published: 6 October 2021| Version 1 | DOI: 10.17632/tn4hkfrn9w.1
Hongqi LI


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%.



Beihang University


Vehicle Routing Problem