Location Planning of Drone Charging Stations using Geographic Information System (GIS) to Maximize Service Level for on-Demand Food Delivery

Abstract

On-Demand Food Delivery (ODFD) services have witnessed significant growth, driven by advancements in technology and internet accessibility. This has facilitated consumer convenience by enabling food orders from restaurants to be delivered directly to their doorstep. However, this rapid expansion has contributed to an increase in vehicular traffic and associated travel routes, particularly within urban areas. This surge in vehicle usage has resulted in traffic congestion, higher costs, and environmental impacts, hindering the efficiency of the supply chain system. The utilization of drones presents a promising solution to overcome those challenges. However, their limited flight range poses a significant obstacle to widespread implementation. To address this limitation, this research focuses on developing an algorithm to optimize the location of drone charging stations, thereby maximizing service coverage. Ant Colony Optimization is used as the optimization method within the algorithm. A case study is conducted within the Yogyakarta Ring Road area, utilizing OpenStreetMap as the data source, to evaluate the algorithm's performance. The optimization results show a demand coverage of 22357 points, representing 93.56% of the total potential demand points. In addition, three distinct delivery mode scenarios are established: drone with 2 km flight range, drone with 4 km flight range, and motorcycle. These scenarios are implemented to assess the influence of vehicle types on delivery routes and environmental impact.