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

TL;DR

This paper introduces VRPD-DT, a vehicle routing problem with drones considering real-time traffic dynamics, using machine learning for travel time prediction and a VND algorithm to optimize routes, outperforming existing heuristics.

Contribution

It presents a novel VRP-D model incorporating dynamic traffic conditions and a machine learning-based travel time prediction, along with a VND algorithm for improved routing solutions.

Findings

Proposed algorithm outperforms state-of-the-art heuristics in simulations.

Incorporates real-time traffic data into routing decisions.

Demonstrates significant cost reductions in delivery scenarios.

Abstract

The vehicle routing problem with drones (VRP-D) is to determine the optimal routes of trucks and drones such that the total operational cost is minimized in a scenario where the trucks work in tandem with the drones to deliver parcels to customers. 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 (VRPD-DT) to address the limitation of existing VRP-D 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 evaluate the performance compared with a state-of-the-art VRP-D heuristic solution. The results demonstrate that the proposed algorithm outperforms the state-of-the-art algorithm in various delivery scenarios.