arXiv AI
GREAT-ESDD: Graph Edge Attention Network for Effectively Steered Delivery Decisions
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
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.
arXiv AI
4d ago
GREAT: A Graph Edge Attention Network for the Electric Vehicle Routing Problem with Asymmetric Costs
The paper introduces GREAT, a Graph Edge Attention Network that operates directly on edge space to handle asymmetric energy costs in the Electric Vehicle Routing Problem (EVRP). Integrated into a reinforcement learning framework, GREAT jointly optimizes routing and charging decisions, outperforming a strong HGS-based heuristic by up to 8.78% in energy savings and reducing runtime by a factor of 60. It also shows competitive performance against a custom adaptive large neighborhood search and achieves state‑of‑the‑art results on other asymmetric routing benchmarks such as TSP, CVRP, and Orienteering.
arXiv AI
4d ago
Size Generalization for Diffusion Routing Solvers
arXiv:2610.17192v1 Announce Type: new Abstract: Routing problems, like the Traveling Salesman Problem (TSP) and the Capacitated Vehicle Routing Problem (CVRP), are NP-hard and slow to solve for large...
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.
arXiv AI
Sep 30
Just Initialize: A Training-Free Initialization Component for Large-Scale Routing Optimization
arXiv:2609.35443v2 Announce Type: replace Abstract: Large-scale routing problems are difficult to solve efficiently as their search spaces grow rapidly with problem size. Existing approaches primaril...
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.