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

• URL: http://arxiv.org/abs/2505.23584v1
• Date: Thu, 29 May 2025 15:58:01 GMT
• Title: Collaborative Last-Mile Delivery: A Multi-Platform Vehicle Routing Problem With En-route Charging
• Authors: Sumbal Malik, Majid Khonji, Khaled Elbassioni, Jorge Dias,
• Abstract summary: This research introduces a novel synchronized multi-platform vehicle routing problem with drones and robots.<n>A fleet of $mathcalM$ trucks, $mathcalN$ drones and $mathcalK$ robots cooperatively delivers parcels.<n>Trucks serve as mobile platforms, enabling the launching, retrieving, and en-route charging of drones and robots.
• Score: 5.93228031688634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Large Neighborhood Search and Bitmask Dynamic Programming for Wireless Mobile Charging Electric Vehicle Routing Problems in Medical Transportation [5.740535941960799]
  We propose the Wireless Mobile Charging Electric Vehicle Problem (WMC-EVRP)<n>This problem enables Medical Transportation Electric Vehicles (MTEVs) to be charged while traveling via Mobile Charging Carts (MCTs)<n>We develop a mathematical model and a tailored meta-heuristic algorithm that combines Bit Mask Dynamic Programming (BDP) and Large Neighborhood Search (LNS)
  arXiv  Detail & Related papers  (2025-03-11T14:11:10Z)
• SCoTT: Strategic Chain-of-Thought Tasking for Wireless-Aware Robot Navigation in Digital Twins [78.53885607559958]
  We propose SCoTT, a wireless-aware path planning framework.<n>We show that SCoTT achieves path gains within 2% of DP-WA* while consistently generating shorter trajectories.<n>We also show the practical viability of our approach by deploying SCoTT as a ROS node within Gazebo simulations.
  arXiv  Detail & Related papers  (2024-11-27T10:45:49Z)
• Optimization of Multi-Agent Flying Sidekick Traveling Salesman Problem over Road Networks [10.18252143035175]
  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.
  arXiv  Detail & Related papers  (2024-08-20T20:44:18Z)
• VRPD-DT: Vehicle Routing Problem with Drones Under Dynamically Changing Traffic Conditions [12.323383132739195]
  We present a novel problem called the vehicle routing problem with drones under dynamically changing traffic conditions (VRPD-DT) We design a novel cost model that factors in the actual travel distance and projected travel time, computed using a machine learning-driven travel time prediction algorithm. A variable neighborhood descent (VND) algorithm is developed to find the optimal truck-drone routes under the dynamics of traffic conditions.
  arXiv  Detail & Related papers  (2024-04-13T19:28:24Z)
• Multi-Agent Learning of Efficient Fulfilment and Routing Strategies in E-Commerce [11.421159751635667]
  paper presents an integrated algorithmic framework for minimising product delivery costs in e-commerce. One of the major challenges in e-commerce is the large volume of-temporally diverse orders from multiple customers. We propose an approach that combines graph neural networks and reinforcement learning to train the node selection and vehicle agents.
  arXiv  Detail & Related papers  (2023-11-20T10:32:28Z)
• Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approach [49.00137468773683]
  Collaborative vehicle routing occurs when carriers collaborate through sharing their transportation requests and performing transportation requests on behalf of each other. Traditional game theoretic solution concepts are expensive to calculate as the characteristic function scales exponentially with the number of agents. We propose to model this problem as a coalitional bargaining game solved using deep multi-agent reinforcement learning.
  arXiv  Detail & Related papers  (2023-10-26T15:42:29Z)
• Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays [55.736718475856726]
  Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT) Existing UAV-assisted data harvesting and dissemination schemes require UAVs to frequently fly between the IoTs and access points. We introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination.
  arXiv  Detail & Related papers  (2023-08-03T02:49:50Z)
• A Multi-Objective approach to the Electric Vehicle Routing Problem [0.0]
  The electric vehicle routing problem (EVRP) has garnered great interest from researchers and industrialists in an attempt to move from fuel-based vehicles to healthier and more efficient electric vehicles (EVs) Previous works target logistics and delivery-related solutions wherein a homogeneous fleet of commercial EVs have to return to the initial point after making multiple stops. We perform multi-objective optimization - minimizing the total trip time and the cumulative cost of charging.
  arXiv  Detail & Related papers  (2022-08-26T05:09:59Z)
• Value Function is All You Need: A Unified Learning Framework for Ride Hailing Platforms [57.21078336887961]
  Large ride-hailing platforms, such as DiDi, Uber and Lyft, connect tens of thousands of vehicles in a city to millions of ride demands throughout the day. We propose a unified value-based dynamic learning framework (V1D3) for tackling both tasks.
  arXiv  Detail & Related papers  (2021-05-18T19:22:24Z)
• Efficient UAV Trajectory-Planning using Economic Reinforcement Learning [65.91405908268662]
  We introduce REPlanner, a novel reinforcement learning algorithm inspired by economic transactions to distribute tasks between UAVs. We formulate the path planning problem as a multi-agent economic game, where agents can cooperate and compete for resources. As the system computes task distributions via UAV cooperation, it is highly resilient to any change in the swarm size.
  arXiv  Detail & Related papers  (2021-03-03T20:54:19Z)
• Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning [79.61517670541863]
  How to use AI to provide efficient bicycle dispatching solutions based on dynamic bicycle rental demand is an essential issue for dockless PBS (DL-PBS) We propose a dynamic bicycle dispatching algorithm based on multi-objective reinforcement learning (MORL-BD) to provide the optimal bicycle dispatching solution for DL-PBS.
  arXiv  Detail & Related papers  (2021-01-19T03:09:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.