A Genetic Algorithm for Solving the Truck-Drone-ATV Routing Problem
In this paper, we introduce and investigate a new style of delivery in last-mile logistics, in which we merge the existing concept of conventional truck-based delivery with emerging technologies, i.e., drones and autonomous robots (autonomous transport vehicles (ATVs)). More precisely, in the Truck-Drone-ATV Routing Problem (TDA-RP), a truck, carrying several drones and ATVs as well as the parcels, departs from a depot, visits a given list of grid points, each of them at most once, and returns to the depot by the end of the mission. In addition, at each visited grid point, a set of drones and ATVs are tasked to deliver the parcels to the customers via circumjacent operations. The objective consists in serving all customers in shortest possible time. However, due to the computational complexity of the problem, we cannot solve it by exact methods. Hence, we suggest a Genetic Algorithm for solving the problem and, through our computational experiments on randomly generated instances, we show the benefits of using a mixed fleet of drones and ATVs assisting a truck.
KeywordsTraveling salesman problem Last-mile logistics Drone Autonomous vehicle Heuristics Metaheuristics Genetic algorithm
The authors would like to acknowledge the Technische Universität Kaiserslautern (Germany) for the financial support through the research program “Forschungsförderung des TU Nachwuchsringes”.
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