Optimal Charging Station Deployment for Drone-Assisted Delivery
The optimization aims at minimizing charging station installation costs, drone energy consumption, and operational costs. The aim of this work is to design a model to
Sudbury and Hutchinson (2016) assert that drone technology, replacing labor and traditional delivery methods, holds promise but faces challenges. Limited battery life restricts drone delivery range; however, drone charging stations offer a solution by enabling longer flights and wider delivery areas.
Many firms are investing in drone logistics ventures to capitalize on their capabilities. However, the limited range of drone deliveries, dictated by battery capacity, poses a significant challenge. Hybrid delivery systems combining trucks and drones have gained attention to overcome this challenge.
By strategically deploying a number of these charging stations, it is possible to extend the operating range of the drones to reach farther sites from fewer departing hubs than in the case with only direct deliveries from the hubs (Fig. 1.b). Such a network of charging stations must be designed considering the costs and constraints implied.
We propose establishing dedicated drone charging stations and optimizing drone routing for efficient deliveries to address these issues We present a MINLP (Mixed Integer Non-Linear Programming) model aimed at identifying the most cost-effective solution that optimizes both transportation efficiency and charging infrastructure investment.
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