Asymmetric Arc Routing by Coordinating a Truck and Multiple Drones

Area of Science:

• Operations Research
• Transportation Science
• Robotics

Background:

• Unmanned Aerial Vehicles (UAVs), or drones, offer flexibility for tasks like traffic patrolling.
• Existing drone-truck coordination models often overlook the complexities of asymmetric (one-way) road networks.
• Limited drone endurance necessitates efficient deployment strategies alongside ground vehicles.

Purpose of the Study:

• To develop an asymmetric coordinated vehicle-drones arc routing model for traffic patrolling.
• To address the challenge of coordinating a truck and multiple drones on asymmetric road networks.
• To minimize the total patrol time under drone time-limit constraints.

Main Methods:

• Formulation of an asymmetric arc routing problem integrating truck and drone operations.
• Development of a large-scale neighborhood search with simulated annealing (LNS-SA) algorithm.
• Validation through extensive computational experiments and a real-world case study.

Main Results:

• The proposed LNS-SA algorithm demonstrates efficiency in solving the asymmetric coordinated vehicle-drones arc routing problem.
• Experimental results confirm the effectiveness of the coordinated approach for traffic patrolling.
• Sensitivity analysis provides insights into the impact of drone parameters on patrol time.

Conclusions:

• The asymmetric coordinated vehicle-drones arc routing model effectively enhances traffic patrolling efficiency.
• The LNS-SA algorithm is a viable and efficient method for solving this complex routing problem.
• The study highlights the importance of considering road network asymmetry in drone-assisted logistics.