collaborators

7 papers

stat.ML2026

SSPO: Structure-Aware Similarity-Weighted Preference Optimization for Neural Combinatorial Optimization

Yuanyu Li, Jintao Xu, Zijiang Liu +6

Neural combinatorial optimization (NCO) relies on parallel solution sampling for training, yet existing methods fail to fully exploit the rich information latent in a co-sampled so…

cs.AI2026

DualCert: A Solver for the Traveling Salesman Problem with Constraint-Coupled Learning

Yancheng Song, Yongzhi Qi, Wei Qi +1

Large traveling salesman problem (TSP) instances require a solver to allocate limited computation while preserving the validity of its outputs. Existing neural--operations-research…

cs.AI2026

SCOPE: Supply-Chain Operations through Coupled Policies for End-to-End Coordination

Yunhao Liang, Xianqi Cao, Pujun Zhang +4

Can supply-chain AI move beyond isolated decision modules toward unified operational planning? A complete replenishment plan specifies which products each location carries, which u…

cs.AI2026

SPORD: A Simulation-Propose-then-OR-Dispose Approach for Supply Chain Planning

Jiayin He, Yutong Pan, Sen Yang +5

For years, supply chain planning at e-commerce firms has operated as a collection of isolated projects. Each planning task from static network planning to dynamic warehouse assortm…

cs.AI2026

ORPR: An OR-Guided Pretrain-then-Reinforce Learning Model for Inventory Management

Lingjie Zhao, Xue Yu, Yongzhi Qi +6

As the pursuit of synergy between Artificial Intelligence (AI) and Operations Research (OR) gains momentum in handling complex inventory systems, a critical challenge persists: how…

cs.AI2025

Rethinking Supply Chain Planning: A Generative Paradigm

Jiaheng Yin, Yongzhi Qi, Jianshen Zhang +5

Supply chain planning is the critical process of anticipating future demand and coordinating operational activities across the logistics network. However, within the context of con…

cs.LG2025

TimeHF: Billion-Scale Time Series Models Guided by Human Feedback

Yongzhi Qi, Hao Hu, Dazhou Lei +6

Time series neural networks perform exceptionally well in real-world applications but encounter challenges such as limited scalability, poor generalization, and suboptimal zero-sho…