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Graph Learning for Smart Delivery Prediction

BACKGROUND
2 SIGNALSFIRST DETECTED 8 April 2026UPDATED 16 October 2026
Narrative synthesis pending — 2 signals collected.
How the story developed
15 Oct
2.3
8 Apr
3.8
Other feeds and industry voices
20 Oct
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Vehicle routing problem using deep reinforcement learning - A case study about truck planning in the industry

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4 Sept
PPO-STGNN: A Proximal Policy Optimization Approach with Spatio-Temporal Graph Neural Networks for DAG Task Scheduling in Cloud-Edge-End Computing

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20 Mar
Single Agent Robust Deep Reinforcement Learning for Bus Fleet Control

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21 Aug
ORBITER: Conflict-Aware Decision-Making for Agentic Last-Mile Delivery

arXiv:2608.18846v1 Announce Type: new Abstract: Last-mile delivery aims to handle dynamically arriving orders with couriers while modeling complex spatial and temporal

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4 Aug
HetGPS: Scalable Graph Multi-Agent Reinforcement Learning with Physics-Anchored Adaptive Safety for EV Charging

arXiv:2608.00679v1 Announce Type: new Abstract: Safety interventions for large populations of network-coupled agents must protect shared constraints without unnecessarily

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22 Jul
On the Effectiveness of Pretraining for Graph Combinatorial Optimization

arXiv:2607.19072v1 Announce Type: new Abstract: This paper introduces a self-supervised pretraining framework for graph combinatorial optimization specifically designed to address

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24 Jul
AI-Driven Multi-Hop Relay Selection for Smart Urban NR-V2X Networks via Learning-to-Optimize Graph Neural Networks

arXiv:2607.20554v1 Announce Type: new Abstract: Reliable and low-latency NR-V2X communications are essential for smart mobility in dense urban environments. However, limited

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