Adapt-VRPD: Vehicle Routing Problem with Drones Under Dynamically Changing Traffic Conditions

Myounggyu Won, Navid Mohammad Imran

Abstract: The vehicle routing problem with drones (VRPD) involves determining the optimal routes for trucks and drones to collaboratively deliver parcels to customers, aiming to minimize total operational costs. While various heuristic algorithms have been developed to address the problem, existing solutions are built based on simplistic cost models, overlooking the temporal dynamics of the costs, which fluctuate depending on the dynamically changing traffic conditions. In this paper, we present a novel problem called the vehicle routing problem with drones under dynamically changing traffic conditions (Adapt-VRPD) to address the limitation of existing VRPD solutions. We design a novel cost model that factors in the actual travel distance and projected travel time, computed using a machine learning-driven travel time prediction algorithm. A variable neighborhood descent (VND) algorithm is developed to find the optimal truck-drone routes under the dynamics of traffic conditions through incorporation of the travel time prediction model. A simulation study was performed to compare our algorithm with a state-of-the-art VRPD heuristic. Our algorithm outperformed the benchmark, reducing the average and maximum discrepancies from the actual cost by 37.6% and 27.6%, respectively, across various delivery scenarios.