arXiv AI

Drive, Pack, Fly: The Travelling Thief Problem with Drone

arXiv:2608. 16435v1 Announce Type: new Abstract: In collection operations, accumulating payload progressively slows the vehicle, imposing a cumulative penalty on routing efficiency.

arXiv AI

Jul 8

Reward-Density Heuristic for Dynamic Multi-Vehicle Routing: Performance and Computational Efficiency

arXiv:2607. 06066v1 Announce Type: new Abstract: The Vehicle Routing Problem (VRP) and its variants represent some of the most practically consequential optimization challenges in modern logistics and urban mobility.

By Manish Kolachalam, Rani Malhotra

agents robotics

arXiv AI

Jul 1

Locker-based Truck-Drone Routing with Integrated Considerations of Pickups, Deliveries, and No-Fly Zones

arXiv:2606. 30680v1 Announce Type: cross Abstract: Truck-drone delivery is an emerging last-mile logistics mode combining the long-haul capacity of trucks with the flexible service capability of drones.

By Xuanyu Liu, Hui Hu, Jiao Zhao, Ziliang Wang, Zhengbing He

reinforcement-learning robotics

arXiv AI

Jul 21

A Deep Reinforcement Learning Algorithm for the Vehicle Routing Problem with Stochastic Demands and Outsourcing

arXiv:2607. 16875v1 Announce Type: cross Abstract: We introduce the vehicle routing problem with stochastic demands and outsourcing options (VRP-SDO), in which a logistics service provider partitions customer requests into customers outsourced to a common carrier and customers committed to its fixed fleet.

By Mohsen Dastpak, Fausto Errico, Ola Jabali

reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jul 27

Integrated Order Dispatching and Routing for Last-Mile Pickup via Deep Reinforcement Learning

arXiv:2607. 22356v1 Announce Type: new Abstract: In recent years, the growing complexity of last-mile pickup operations has increased the need for fast and accurate decision-making on logistics platforms.

By Yida Xu, Zhaofang Mao, Yuheng Miao, Jiaxin Zhang, Yiting Sun

reinforcement-learning benchmarks

arXiv AI

Jun 19

Oranits: Mission Assignment and Task Offloading in Open RAN-based ITS using Metaheuristic and Deep Reinforcement Learning

arXiv:2507. 19712v3 Announce Type: replace-cross Abstract: In this paper, we explore mission assignment and task offloading in an Open Radio Access Network (Open RAN)-based intelligent transportation system (ITS), where autonomous vehicles leverage mobile edge computing for efficient processing.

By Ngoc Hung Nguyen, Nguyen Van Thieu, Quang-Trung Luu, Anh Tuan Nguyen, Senura Wanasekara, Nguyen Cong Luong, Fatemeh Kavehmadavani, Van-Dinh Nguyen

agents reinforcement-learning

arXiv AI

Jun 3

ASAP: Exploiting the Satisficing Generalization Edge in Neural Combinatorial Optimization

arXiv:2501. 17377v4 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) has emerged as a promising approach for solving Combinatorial Optimization (CO) problems, such as the 3D Bin Packing Problem (3D-BPP), Traveling Salesman Problem (TSP), or Vehicle Routing Problem (VRP), but these neural solvers often exhibit brittleness when facing distribution shifts.

By Han Fang, Paul Weng, Yutong Ban

reinforcement-learning fine-tuning benchmarks

arXiv AI

1d ago

An Agentic AI Framework with Large Language Models and Chain-of-Thought for UAV-Assisted Logistics Scheduling with Mobile Edge Computing

arXiv:2605. 13221v2 Announce Type: replace Abstract: In cloud manufacturing, unmanned aerial vehicles (UAVs) can support both product collection and mobile edge computing (MEC).

By Hanwen Zhang, Dusit Niyato, Wei Zhang, Xin Lou, Malcolm Yoke Hean Low

llms rag agents reinforcement-learning

arXiv Machine Learning

Jul 8

Deep Reinforcement Learning for Dynamic Battery Management of Autonomous Order Pickers

arXiv:2607. 05683v1 Announce Type: new Abstract: Battery charging of Autonomous Mobile Robots (AMRs) in warehouses is a critical operational challenge that heavily impacts both order processing times and throughput.

By Taniya Shaji, Abhay Sobhanan, Christof Defryn

agents reinforcement-learning robotics efficiency benchmarks

arXiv Machine Learning

Aug 4

Hard Constraints, Smooth Gradients: Learning Feasible Inventory Policies via Differentiable Projection

arXiv:2608. 02343v1 Announce Type: cross Abstract: Many operational problems are constrained sequential decision processes with large, combinatorial action spaces and interdependent feasibility constraints.

By Patrick Helm, Jan-Niklas Doerr, Joren Gijsbrechts, Stefan Minner

rag reinforcement-learning benchmarks

arXiv Machine Learning

Jul 8

Deep Reinforcement Learning for Dynamic Origin-Destination Matrix Estimation in Microscopic Traffic Simulations Considering Credit Assignment

arXiv:2511. 06229v3 Announce Type: replace Abstract: This paper focuses on dynamic origin-destination matrix estimation (DODE), a crucial calibration process necessary for the effective application of microscopic traffic simulations.

By Donggyu Min, Seongjin Choi, Dong-Kyu Kim

agents reinforcement-learning

arXiv AI

Jun 18

A Distributionally Robust Reinforcement Learning Framework for Constrained Urban EV Dispatch

arXiv:2604. 25848v2 Announce Type: replace Abstract: We study city-scale control of electric-vehicle (EV) ride-hailing fleets where dispatch, repositioning, and charging decisions must respect charger and feeder limits under uncertain, spatially correlated demand and travel times.

By An Nguyen, Hoang Nguyen, Phuong Le, Hung Pham, Cuong Do, Laurent El Ghaoui

agents reinforcement-learning

arXiv Machine Learning

Jul 22

Reinforcement Learning for Delivery Drone-Based Participatory Sensing in Dynamic Environments

arXiv:2607. 18874v1 Announce Type: new Abstract: Using Unmanned Aerial Vehicle (UAV) for urban sensing has emerged as a powerful paradigm to monitor the status of the city, e.

By Xin Ouyang, Songxin Lei, Xusen Guo, Yutian Jiang, Sijie Ruan, Yuxuan Liang

rag reinforcement-learning robotics

Related stories