arXiv AI July 21, 2026 By Mohsen Dastpak, Fausto Errico, Ola Jabali

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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

Aug 10

Vehicle routing problem using deep reinforcement learning - A case study about truck planning in the industry

arXiv:2608. 06668v1 Announce Type: new Abstract: As an important component of the supply chain industry, transportation has experienced rapid development in the past decade with the assistance of digital platforms and intelligent algorithms.

By Siliang Lu, Dan Hu, Lili Wu

agents reinforcement-learning

arXiv Machine Learning

Aug 5

Beyond Solving: Prescriptive Probing for Neural Routing Solvers

arXiv:2602. 07216v2 Announce Type: replace Abstract: Neural combinatorial optimization (NCO) trains fast heuristics for routing problems, but planners often need more than a single solve: they ask which stop to drop, which transition to preserve, or which subset of stops to remove if a route is infeasible.

By Reuben Narad, L\'eonard Boussioux, Michael Wagner

reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 25

Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application

arXiv:2606. 25362v1 Announce Type: cross Abstract: Sequential contextual stochastic programs model real-time decision systems in which each time epoch commits to an action under uncertainty whose consequences propagate into future decisions.

By Tinghan Ye, Shuaicheng Tong, Changkun Guan, Beste Basciftci, Pascal Van Hentenryck

safety

arXiv AI

Jun 30

Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing Solver

arXiv:2405. 01906v3 Announce Type: replace Abstract: In modern intelligent transportation systems (ITS), particularly in freight transportation and logistics, real-time route planning is crucial.

By Changliang Zhou, Xi Lin, Zhenkun Wang, Xialiang Tong, Mingxuan Yuan, Qingfu Zhang

efficiency benchmarks

arXiv Machine Learning

2d ago

Dynamic Multi-Depot Vehicle Routing with Online Requests: Event-Driven Transformer--DRL and Rolling-Horizon Benchmarking

arXiv:2608. 13799v1 Announce Type: new Abstract: This paper presents an event-driven learning and benchmarking framework for the Dynamic Multi-Depot Vehicle Routing Problem with progressively revealed requests and evolving vehicle states.

By Faezeh Ardali, Gerald M. Knapp

llms reinforcement-learning benchmarks

Related stories

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

Vehicle routing problem using deep reinforcement learning - A case study about truck planning in the industry

Beyond Solving: Prescriptive Probing for Neural Routing Solvers

Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application

Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing Solver

Dynamic Multi-Depot Vehicle Routing with Online Requests: Event-Driven Transformer--DRL and Rolling-Horizon Benchmarking