Multi-Agent Training-free Urban Food Delivery System using Resilient UMST Network

TL;DR

This paper introduces a novel, training-free urban delivery network design called UMST that balances efficiency, resilience, and scalability by uniting multiple minimum spanning trees.

Contribution

The paper proposes the UMST approach, generating robust delivery networks with fewer edges, competitive performance, and faster execution without the need for training or complex learning models.

Findings

UMST reduces edges by 20-40 times compared to fully connected graphs.

Achieves 75-83% participation rates in order bundling.

Provides 88-96% success rates with 44-53% distance savings.

Abstract

Delivery systems have become a core part of urban life, supporting the demand for food, medicine, and other goods. Yet traditional logistics networks remain fragile, often struggling to adapt to road closures, accidents, and shifting demand. Online Food Delivery (OFD) platforms now represent a cornerstone of urban logistics, with the global market projected to grow to over 500 billion USD by 2030. Designing delivery networks that are efficient and resilient remains a major challenge: fully connected graphs provide flexibility but are computationally infeasible at scale, while single Minimum Spanning Trees (MSTs) are efficient but easily disrupted. We propose the Union of Minimum Spanning Trees (UMST) approach to construct delivery networks that are sparse yet robust. UMST generates multiple MSTs through randomized edge perturbations and unites them, producing graphs with far fewer edges than fully connected networks while maintaining multiple alternative routes between delivery hotspots. Across multiple U.S. cities, UMST achieves 20--40$\times$ fewer edges than fully connected graphs while enabling substantial order bundling with 75--83% participation rates. Compared to learning-based baselines including MADDPG and Graph Neural Networks, UMST delivers competitive performance (88-96% success rates, 44-53% distance savings) without requiring training, achieving 30$\times$ faster execution while maintaining interpretable routing structures. Its combination of structural efficiency and operational flexibility offers a scalable and resilient foundation for urban delivery networks.