61 citations · 129 across the 20 of their papers we have counts for
9 papers · 1 filter
LLM-Based Exogenous World Modeling for Dynamic Scheduling
Ni Zhang, Haofeng Yuan, Jianan Zhou +2
Dynamic scheduling requires dispatching online arrivals while anticipating future jobs, as poor decisions can compound downstream costs under nonlinear tardiness objectives. We sho…
Self-Guided LLM Reasoning for Error Diagnosis in Vehicle Routing Problems
Ni Zhang, Jianan Zhou, Yue-Jiao Gong +1
Vehicle Routing Problems (VRPs) are fundamental to many real-world applications. Recent advances in large language models (LLMs) have spurred progress in solving these NP-hard prob…
Task Specialization Fine-Tuning for Contextual Reinforcement Learning
Jianan Zhou, Jung-Hoon Cho, Tianyue Zhou +5
Contextual Reinforcement Learning (CRL) seeks to generalize classical RL by maximizing task coverage across a context space of related tasks. While prior works often train from scr…
Learning to Solve Compositional Geometry Routing Problems
Mingfeng Fan, Jianan Zhou, Jiaqi Cheng +3
We study the Compositional Geometry Routing Problem (CGRP), a unified superclass of traditional routing problems that covers point-only, line-only, area-only, and arbitrary hybrid…
Learning Scenario Reduction for Two-Stage Robust Optimization with Discrete Uncertainty
Tianjue Lin, Jianan Zhou, Jieyi Bi +4
Two-Stage Robust Optimization (2RO) with discrete uncertainty is challenging, often rendering exact solutions prohibitive. Scenario reduction alleviates this issue by selecting a s…
Enhancing Cross-Problem Vehicle Routing via Federated Learning
Xiangchi Meng, Jianan Zhou, Jie Gao +4
Vehicle routing problems (VRPs) constitute a core optimization challenge in modern logistics and supply chain management. The recent neural combinatorial optimization (NCO) has dem…
PyVRP: LLM-Driven Metacognitive Heuristic Evolution for Hybrid Genetic Search in Vehicle Routing Problems
Manuj Malik, Jianan Zhou, Shashank Reddy Chirra +1
Designing high-performing metaheuristics for NP-hard combinatorial optimization problems, such as the Vehicle Routing Problem (VRP), remains a significant challenge, often requirin…
DRAGON: LLM-Driven Decomposition and Reconstruction Agents for Large-Scale Combinatorial Optimization
Shengkai Chen, Zhiguang Cao, Jianan Zhou +5
Large Language Models (LLMs) have recently shown promise in addressing combinatorial optimization problems (COPs) through prompt-based strategies. However, their scalability and ge…
Towards Efficient Constraint Handling in Neural Solvers for Routing Problems
Jieyi Bi, Zhiguang Cao, Jianan Zhou +5
Neural solvers have achieved impressive progress in addressing simple routing problems, particularly excelling in computational efficiency. However, their advantages under complex…