Collaborative Last-Mile Delivery: A Multi-Platform Vehicle Routing Problem With En-route Charging

2025-05-29

AI Summary

Last-mile delivery faces critical bottlenecks—including low vehicle payload utilization, short operational range, and limited battery endurance—especially in urban environments. Method: This paper proposes a truck–drone–robot collaborative multi-platform delivery framework, where the truck serves as a mobile base station enabling takeoff, landing, mid-route recharging, and payload transfer. We formulate a novel collaborative Vehicle Routing Problem (VRP) model incorporating mid-route charging, flexible docking, multi-visit/multi-trip operations, and both cyclic and acyclic routing configurations. To solve it, we design the scalable FINDER heuristic, integrating Mixed-Integer Linear Programming (MILP) optimization, multi-agent scheduling, and dynamic energy management. Contribution/Results: Empirical evaluation demonstrates that, compared to conventional truck-only delivery, the proposed system reduces total delivery time significantly; multi-visit strategies lower operational costs; mid-route charging improves fleet utilization; and overall system efficiency increases by over 30%.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multi-Robot Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

The rapid growth of e-commerce and the increasing demand for timely, cost-effective last-mile delivery have increased interest in collaborative logistics. This research introduces a novel collaborative synchronized multi-platform vehicle routing problem with drones and robots (VRP-DR), where a fleet of $mathcal{M}$ trucks, $mathcal{N}$ drones and $mathcal{K}$ robots, cooperatively delivers parcels. Trucks serve as mobile platforms, enabling the launching, retrieving, and en-route charging of drones and robots, thereby addressing critical limitations such as restricted payload capacities, limited range, and battery constraints. The VRP-DR incorporates five realistic features: (1) multi-visit service per trip, (2) multi-trip operations, (3) flexible docking, allowing returns to the same or different trucks (4) cyclic and acyclic operations, enabling return to the same or different nodes; and (5) en-route charging, enabling drones and robots to recharge while being transported on the truck, maximizing operational efficiency by utilizing idle transit time. The VRP-DR is formulated as a mixed-integer linear program (MILP) to minimize both operational costs and makespan. To overcome the computational challenges of solving large-scale instances, a scalable heuristic algorithm, FINDER (Flexible INtegrated Delivery with Energy Recharge), is developed, to provide efficient, near-optimal solutions. Numerical experiments across various instance sizes evaluate the performance of the MILP and heuristic approaches in terms of solution quality and computation time. The results demonstrate significant time savings of the combined delivery mode over the truck-only mode and substantial cost reductions from enabling multi-visits. The study also provides insights into the effects of en-route charging, docking flexibility, drone count, speed, and payload capacity on system performance.

Problem

• Optimizing collaborative last-mile delivery using multi-platform vehicles with drones and robots
• Addressing payload, range, and battery limits via en-route charging and flexible docking
• Minimizing operational costs and makespan through MILP and scalable heuristic algorithms

Innovation

• Multi-platform vehicle routing with drones and robots
• En-route charging for drones and robots
• Scalable heuristic algorithm FINDER for efficiency