Multi-criteria coordinated electric vehicle-drone hybrid delivery framework

Abstract

Recent studies have highlighted the effectiveness of a coordinated delivery strategy, where electric vehicles and drones work in tandem to enhance delivery throughput and energy efficiency. However, the majority of research in logistics and transportation has primarily focused on delivery performance, often overlooking energy efficiency. Several key limitations characterize this oversight: Most studies neglect the geographic information of the delivery route, despite road slope being a crucial factor in energy consumption. The power consumption models for electric vehicles and drones are oversimplified, focusing solely on driving mileage and disregarding delivery time, which is a significant consideration. The battery model is typically simplified to a linear model, ignoring the non-linearity properties inherent in practical batteries. The presence of multi- and heterogeneous agents of vehicles is often not taken into account. In response to these limitations, this work introduces a framework designed to generate energy- and time-efficient delivery schedules for a hybrid delivery service involving electric vehicles and drones. We first establish accurate power and battery models for electric vehicles and drones, drawing from manufacturers’ system specifications and experimental data. We propose a delivery scheduling algorithm to determine the optimal delivery schedule for electric vehicles and drones. Our framework also incorporates various cost functions to evaluate the results of delivery scheduling in terms of time, energy, the weighted sum of time and energy, and an economic model. We consider both uncontrollable elements, such as geographical information of nodes and package weights, and controllable factors, such as the type and quantity of electric vehicles and drones. The proposed framework is validated through randomly implemented delivery missions and delivery scenarios in existing cities. The results demonstrate that our coordinated delivery approach significantly reduces delivery costs in terms of the economic model compared to a delivery schedule that relies solely on electric vehicles.