Systems-Level Planning and Coordination of Truck-Drone Collaborative Delivery Networks

2026-06-07

AI Summary

This study addresses the challenges of low coordination efficiency, unreliable communication, and limited scalability in heterogeneous truck–drone fleets for urban last-mile delivery. To this end, it proposes the first systems-and-control-oriented five-layer hierarchical coordination framework, encompassing spatial demand alignment, collaborative configuration, resource scheduling, performance evaluation, and scalability analysis. The framework enables efficient and unified control through structured task orchestration and a multi-agent synchronization mechanism. Simulation experiments based on real-world urban delivery data demonstrate that, compared to truck-only delivery, the proposed approach reduces total delivery time by 42.4% and energy consumption by 44.2%, while maintaining substantial coordination benefits even as the system scales up.

Abstract

Urban last-mile parcel delivery increasingly relies on heterogeneous fleets whose performance depends on timely coordination, reliable communication, and scalable control. Truck-drone collaboration has emerged as a networked cyber-physical delivery paradigm that combines the payload capacity and range efficiency of trucks with the agility of drones in congested or access-limited urban environments. This paper proposes a layered planning and coordination framework that structures truck-drone collaborative delivery (TDCD) from a systems and control perspective. The framework consists of five interrelated layers: spatial-demand alignment, collaborative delivery configuration, resource and workflow orchestration, performance evaluation, and scalability analysis, providing a unified view of coordination, control, and system-level performance in networked delivery operations. The proposed framework is evaluated using a realistic urban last-mile delivery scenario derived from the 2021 Amazon Last Mile Routing Research Challenge dataset. The case study demonstrates how coordinated truck-drone operation, enabled by structured task orchestration and inter-agent synchronization, improves end-to-end system efficiency under operational constraints. Results show a 42.4% reduction in total delivery time and a 44.2% reduction in energy consumption compared to a conventional truck-only delivery model. The scalability analysis further highlights how coordination gains persist as system size increases, and shows the importance of efficient control and communication in heterogeneous delivery networks.

Problem

• truck-drone collaboration
• last-mile delivery
• heterogeneous fleets
• systems-level coordination
• urban parcel delivery

Innovation

• truck-drone collaboration
• layered coordination framework
• heterogeneous delivery networks
• system-level planning
• scalable control