A Genetic Algorithm for Collaborative Truck-Drone Routing and Scheduling Problem in Surveillance Operations
DOI:
https://doi.org/10.23055/ijietap.2025.32.3.10375Keywords:
Routing; Scheduling; Truck–Drone Collaboration; Surveillance; Genetic AlgorithmAbstract
Drones can access areas that are difficult to reach for ground surveillance resources. However, drones have limited surveillance operations over large areas because of their short flight durations. To tackle the limitations of drones, one viable approach to use trucks as mobile platforms for the takeoff and landing of drones, ensuring close proximity to surveillance areas. However, coordinating the trucks and the drones is challenging due to the combinatorial complexity of scheduling their surveillance routes collaboratively. Motivated by this challenge, this study develops a genetic algorithm to solve the truck-drone routing and scheduling problem for surveillance. This algorithm determines the routes and schedules of multiple trucks and drones to monitor a given set of surveillance areas, aiming to minimize the time spent completing all surveillance operations. A set of numerical experiments is performed to validate the performance of the algorithm and discuss the managerial implications of collaborative surveillance.
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