A Simulation-Heuristic Approach to Optimally Design Drone Delivery Systems in Rural Areas

In recent years, drone delivery has become one of the most widely adopted emerging technologies. Drone delivery systems stemmed from the inefficiencies of trucks within the last mile of the supply chain. The last mile of delivery is the most costly portion of the supply chain for all delivery companies. To solve this problem, commercial companies are looking at drones as a viable source of package transportation. As technology continues to climb at an exponential rate, many supply chains have started to experiment with drone delivery systems, and simulation modeling helps with such investigations a lot.

Under the Covid-19 pandemic, drones greatly improve logistics, especially in rural areas, where inefficient road networks and long distances between customers reduce the delivery capacity of conventional ground vehicles. Considering the limited flight range of drones, charging stations play essential roles in the rural delivery system.

In this study, the researchers utilized simulation to optimize the drone delivery system design in order to minimize the cost of serving the maximum capacity of customers. The simulation model represented customers, drone depots, and charging stations with an agent-based approach, and their locations were modeled on a Geographic Information System (GIS) map in accordance with their latitude and longitude.

As facility siting is usually difficult to optimize, the developers proposed a novel simulation-heuristic framework that incorporated heuristic algorithms into simulation environments and continuously improved the objective to find near-optimal solutions.

As a result, they modeled the drone delivery problem with agent-based simulation and optimized the locations of drone depots and charging stations using genetic algorithm (GA). In addition, the researchers conducted a case study using real-world data collected from Knox County, Tennessee. The results suggested that the proposed approach saved over 15% on total costs compared with the benchmark.

An overview of the agent-based model
An overview of the agent-based model

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