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

TL;DR

This paper introduces a novel multi-agent routing problem for last-mile delivery on real road networks, proposing a MILP model and a heuristic algorithm that outperform existing methods in efficiency and scalability.

Contribution

It extends the single truck-drone model to multiple trucks and drones on actual road networks, providing a scalable solution for large-scale logistics problems.

Findings

Our algorithm outperforms baseline methods in solution quality.

It significantly reduces computation time for large instances.

Scales to over 300 customers within 5 minutes.

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.