Location, Scheduling and Energy Optimization in Drone Delivery Systems with Recharge Stations

Abstract

Recent advances in drone logistics have led to research on integrated planning models. These models combine infrastructure design, energy constraints, and temporal scheduling. This paper introduces the Drone Delivery Location and Scheduling Problem with Recharging Stations (DDLSP-R). DDLSP-R advances previous facility-location models by explicitly incorporating time-slot scheduling and delivery due dates. In this problem, each customer must be served within a set time window. Deliveries completed after the due date incur a disutility penalty in the objective function. The proposed model jointly determines the locations of facilities and recharging stations, as well as the allocation and sequencing of drone deliveries. The goal is to minimize total energy consumption and lateness costs, while maximizing coverage and service feasibility. To enable large-scale instances, the approach uses a heuristic decomposition to address strategic location decisions and operational scheduling, each solved via mixed-integer programming within bounded computational time. Experimental results in realistic urban scenarios demonstrate that including recharging stations and temporal scheduling enhances delivery flexibility and expands coverage. These findings highlight the importance of integrating spatial and temporal decision-making for sustainable drone-based logistics in smart cities.