Copertina

OR-R1: Automating Modeling and Solving of Operations Research Optimization Problem via Test-Time Reinforcement Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ding, Zezhen, Tan, Zhen, Zhang, Jiheng, Chen, Tianlong
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Artificial Intelligence Optimization and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

OR-LLM-Agent: Automating Modeling and Solving of Operations Research Optimization Problems with Reasoning LLM
di: Zhang, Bowen, et al.
Pubblicazione: (2025)

Deep Reinforcement Learning for Solving the Fleet Size and Mix Vehicle Routing Problem
di: Wan, Pengfu, et al.
Pubblicazione: (2025)

Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows
di: Li, Wenhao, et al.
Pubblicazione: (2025)

Solving Integrated Process Planning and Scheduling Problem via Graph Neural Network Based Deep Reinforcement Learning
di: Li, Hongpei, et al.
Pubblicazione: (2024)

Solving the Pod Repositioning Problem with Deep Reinforced Adaptive Large Neighborhood Search
di: Xie, Lin, et al.
Pubblicazione: (2025)

Large-Scale LLM Inference with Heterogeneous Workloads: Prefill-Decode Contention and Asymptotically Optimal Control
di: Lin, Ruihan, et al.
Pubblicazione: (2026)

CORL: Reinforcement Learning of MILP Policies Solved via Branch and Bound
di: Anand, Akhil S, et al.
Pubblicazione: (2025)

Integrated Offline and Online Learning to Solve a Large Class of Scheduling Problems
di: Liu, Anbang, et al.
Pubblicazione: (2025)

Test-Time Search in Neural Graph Coarsening Procedures for the Capacitated Vehicle Routing Problem
di: Sim, Yoonju, et al.
Pubblicazione: (2025)

Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process
di: Fan, Zhenan, et al.
Pubblicazione: (2024)

Safe Decentralized Operation of EV Virtual Power Plant with Limited Network Visibility via Multi-Agent Reinforcement Learning
di: Huang, Chenghao, et al.
Pubblicazione: (2026)

A Method to Improve the Performance of Reinforcement Learning Based on the Y Operator for a Class of Stochastic Differential Equation-Based Child-Mother Systems
di: Yin, Cheng, et al.
Pubblicazione: (2023)

Deep Reinforcement Learning for Traveling Purchaser Problems
di: Yuan, Haofeng, et al.
Pubblicazione: (2024)

Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems
di: Huang, Yilie, et al.
Pubblicazione: (2024)

Deep Reinforcement Learning Optimization for Uncertain Nonlinear Systems via Event-Triggered Robust Adaptive Dynamic Programming
di: Bai, Ningwei, et al.
Pubblicazione: (2025)

Select-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets
di: Masiha, Saeed, et al.
Pubblicazione: (2026)

Data-Driven Exploration for a Class of Continuous-Time Indefinite Linear--Quadratic Reinforcement Learning Problems
di: Huang, Yilie, et al.
Pubblicazione: (2025)

Solving General Natural-Language-Description Optimization Problems with Large Language Models
di: Zhang, Jihai, et al.
Pubblicazione: (2024)

Trilevel and Multilevel Optimization using Monotone Operator Theory
di: Shafiei, Allahkaram, et al.
Pubblicazione: (2021)

Reinforcement Learning for Multi-Truck Vehicle Routing Problems
di: Levin, Joshua, et al.
Pubblicazione: (2022)

Solving Truly Massive Budgeted Monotonic POMDPs with Oracle-Guided Meta-Reinforcement Learning
di: Vora, Manav, et al.
Pubblicazione: (2024)

R2L: Reliable Reinforcement Learning: Guaranteed Return & Reliable Policies in Reinforcement Learning
di: Farhi, Nadir
Pubblicazione: (2025)

Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman Problem
di: Goh, Yong Liang, et al.
Pubblicazione: (2022)

ResearchEVO: An End-to-End Framework for Automated Scientific Discovery and Documentation
di: Zhao, Zhe, et al.
Pubblicazione: (2026)

Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning
di: Tan, Jing, et al.
Pubblicazione: (2024)

Learning to Cut: Reinforcement Learning for Benders Decomposition
di: Cai, Haochen, et al.
Pubblicazione: (2026)

Quantum-Inspired Multi Agent Reinforcement Learning for Exploration Exploitation Optimization in UAV-Assisted 6G Network Deployment
di: Taghavi, Mazyar, et al.
Pubblicazione: (2025)

Optimization Learning
di: Van Hentenryck, Pascal
Pubblicazione: (2025)

Deep Reinforcement Learning for Multi-Objective Optimization: Enhancing Wind Turbine Energy Generation while Mitigating Noise Emissions
di: de Frutos, Martín, et al.
Pubblicazione: (2024)

Action Dependency Graphs for Globally Optimal Coordinated Reinforcement Learning
di: Ding, Jianglin, et al.
Pubblicazione: (2025)

R2 v2: The Pareto-compliant R2 Indicator for Better Benchmarking in Bi-objective Optimization
di: Schäpermeier, Lennart, et al.
Pubblicazione: (2024)

Model Predictive Control and Reinforcement Learning: A Unified Framework Based on Dynamic Programming
di: Bertsekas, Dimitri P.
Pubblicazione: (2024)

Hyperparameter Optimization for Driving Strategies Based on Reinforcement Learning
di: Adde, Nihal Acharya, et al.
Pubblicazione: (2024)

Optimizing Service Operations via LLM-Powered Multi-Agent Simulation
di: Wang, Yanyuan, et al.
Pubblicazione: (2026)

Structural Segmentation of the Minimum Set Cover Problem: Exploiting Universe Decomposability for Metaheuristic Optimization
di: Hernández, Isidora, et al.
Pubblicazione: (2026)

Lion Secretly Solves Constrained Optimization: As Lyapunov Predicts
di: Chen, Lizhang, et al.
Pubblicazione: (2023)

Solving Functional Optimization with Deep Networks and Variational Principles
di: Kamtue, Kawisorn, et al.
Pubblicazione: (2024)

Learning the Riccati solution operator for time-varying LQR via Deep Operator Networks
di: Chen, Jun, et al.
Pubblicazione: (2026)

Data-driven Projection Generation for Efficiently Solving Heterogeneous Quadratic Programming Problems
di: Iwata, Tomoharu, et al.
Pubblicazione: (2025)

MUSIC: Accelerated Convergence for Distributed Optimization With Inexact and Exact Methods
di: Wu, Mou, et al.
Pubblicazione: (2024)