Optimization of Multi-Agent Flying Sidekick Traveling Salesman Problem over Road Networks

• URL: http://arxiv.org/abs/2408.11187v1
• Date: Tue, 20 Aug 2024 20:44:18 GMT
• Title: Optimization of Multi-Agent Flying Sidekick Traveling Salesman Problem over Road Networks
• Authors: Ruixiao Yang, Chuchu Fan,
• Abstract summary: We introduce the multi-agent flying sidekick traveling salesman problem (MA-FSTSP) on road networks. We propose a mixed-integer linear programming model and an efficient three-phase algorithm for this NP-hard problem. Our approach scales to more than 300 customers within a 5-minute time limit, showcasing its potential for large-scale, real-world logistics applications.
• Score: 10.18252143035175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The mixed truck-drone delivery systems have attracted increasing attention for last-mile logistics, but real-world complexities demand a shift from single-agent, fully connected graph models to multi-agent systems operating on actual road networks. We introduce the multi-agent flying sidekick traveling salesman problem (MA-FSTSP) on road networks, extending the single truck-drone model to multiple trucks, each carrying multiple drones while considering full road networks for truck restrictions and flexible drone routes. We propose a mixed-integer linear programming model and an efficient three-phase heuristic algorithm for this NP-hard problem. Our approach decomposes MA-FSTSP into manageable subproblems of one truck with multiple drones. Then, it computes the routes for trucks without drones in subproblems, which are used in the final phase as heuristics to help optimize drone and truck routes simultaneously. Extensive numerical experiments on Manhattan and Boston road networks demonstrate our algorithm's superior effectiveness and efficiency, significantly outperforming both column generation and variable neighborhood search baselines in solution quality and computation time. Notably, our approach scales to more than 300 customers within a 5-minute time limit, showcasing its potential for large-scale, real-world logistics applications.

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