Operations Research

Latest papers

36 papers10 papers in the last four weeks, up 67% on the four weeks before. 0.1% of all new papers.

ORPilot: A Production-Oriented Agentic LLM-for-OR Tool for Optimization Modeling
May 4, 2026Guangrui XieMulti-Agent LLM Systems Large Language Model-Guided Optimization
AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems
Apr 18, 2026Sumeet Ramesh Motwani, Chuan Du, Aleksander Petrov +4LLM Post-Training RL Fine-Tuning
1X · University of Oxford
Endogenous Information in Routing Games: Memory-Constrained Equilibria, Recall Braess Paradoxes, and Memory Design
Apr 13, 2026Saad AlqithamiGame Theory Operations Research
ConstraintBench: Benchmarking LLM Constraint Reasoning on Direct Optimization
Feb 25, 2026Joseph Tso, Preston Schmittou, Quan Huynh +1Constraint-Guided Reasoning in Language Models Constrained Optimization
Haladir Research Team
SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems
Jun 2, 2025Asha Ramanujam, Adam Elyoumi, Hao Chen +5RL Benchmarks Constrained RL
Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN · Energy Sciences, ExxonMobil Technology and Engineering Company, Annandale, NJ · Energy Sciences, ExxonMobil Technology and Engineering Company, Annadale, NJ
Online Linear Programming with Batching
Aug 1, 2024Haoran Xu, Peter W. Glynn, Yinyu YeSequential Decision Making Regret Minimization
Department of Management Science and Engineering, Stanford University

May 4, 2026J/K move · Enter open · S save

ORPilot: A Production-Oriented Agentic LLM-for-OR Tool for Optimization Modeling

Guangrui Xie

Multi-Agent LLM Systems Large Language Model-Guided Optimization Natural Language to Optimization Modeling Operations Research

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This paper presents ORPilot, an open-source agentic AI system that translates real-world business problems into solver-ready optimization models. Unlike academic LLM-for-OR tools that assume clean problem specifications with preformatted inline data, ORPilot is designed for production conditions: ambiguous descriptions, large-scale raw operational data, and the need for portability across solver backends. The system introduces four novel components: (1) a conversational interview agent to elicit complete problem specifications, (2) a data collection agent that retrieves data independently of prompts, (3) a parameter computation agent to bridge raw tabular data and model-ready parameters, and (4) a solver-agnostic Intermediate Representation (IR) for deterministic, zero-LLM-call recompilation to Gurobi, CPLEX, PuLP, Pyomo, or OR-Tools solvers. Additionally, self-correcting retry loops utilize solver tracebacks for targeted repairs. ORPilot represents the first attempt to target production-level business problems rather than textbook operations research (OR) cases. Evaluation on real-world problems demonstrates promising results. When tested against traditional academic benchmarks: IndustryOR, NL4OPT and NLP4LP, ORPilot outperformed state-of-the-art tools in accuracy on the IndustryOR benchmark and delivered comparable performance on NL4OPT and NLP4LP.

Latest papers

ORPilot: A Production-Oriented Agentic LLM-for-OR Tool for Optimization Modeling

AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems

Endogenous Information in Routing Games: Memory-Constrained Equilibria, Recall Braess Paradoxes, and Memory Design

ConstraintBench: Benchmarking LLM Constraint Reasoning on Direct Optimization

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems

Online Linear Programming with Batching