Transportation and carbon emissions costs minimization for time-dependent vehicle routing problem with drones

Abstract

Integrating drones in package delivery has emerged as an innovative application of unmanned aerial vehicle (UAV) technology in the logistics and transportation sector. In this context, trucks serve a dual role as delivery vehicles for customers and launch platforms for drones. Drones are deployed for efficient package delivery and can be retrieved from predetermined rendezvous locations using trucks. Our study explicitly targets the collaborative package delivery approach between trucks and drones within urban environments. To optimize this collaboration, we develop a mixed-integer programming (MIP) model for the time-dependent vehicle routing problem with drones (TDVRP-D), which aims to minimize the transportation costs of both trucks and drones, along with the carbon emissions costs associated with trucks. To solve this complex problem efficiently, we propose a highly effective metaheuristic algorithm based on the variable neighborhood search (VNS) technique. Through extensive experimental studies and rigorous comparisons with existing methods, we demonstrate the superiority of our proposed algorithm in terms of solution quality and computational efficiency, particularly for large-scale instances.