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

TL;DR
ORPilot is an open-source AI system designed for production-level optimization modeling, capable of handling ambiguous data and multiple solvers, outperforming academic benchmarks in real-world business problems.
Contribution
It introduces a novel, production-oriented approach with four key components, enabling robust, portable, and accurate optimization modeling from raw business data.
Findings
Outperforms state-of-the-art tools on IndustryOR benchmark.
Achieves comparable results on NL4OPT and NLP4LP benchmarks.
Demonstrates effectiveness on real-world business problems.
Abstract
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…
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