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

Problem
This paper addresses the gap in existing literature regarding the application of large language models (LLMs) for operational research (OR) in real-world business contexts. Most current LLM-for-OR tools are designed for idealized scenarios with clean, structured problem specifications, which limits their applicability in production environments characterized by ambiguous descriptions and unstructured raw data. The authors present ORPilot, an open-source agentic AI system that aims to bridge this gap by effectively translating complex business problems into solver-ready optimization models. This work is presented as a preprint and has not yet undergone peer review.

Method
ORPilot introduces four key components to enhance its functionality in production settings:

1. Conversational Interview Agent: This component engages users in a dialogue to elicit comprehensive problem specifications, accommodating the ambiguity often present in real-world scenarios.
2. Data Collection Agent: This agent autonomously retrieves necessary data from various sources, independent of user prompts, ensuring that the model has access to relevant information.
3. Parameter Computation Agent: This component processes raw tabular data to derive model-ready parameters, facilitating the transition from unstructured data to structured optimization inputs.
4. Solver-Agnostic Intermediate Representation (IR): The IR allows for deterministic recompilation of models to various solver backends (e.g., Gurobi, CPLEX, PuLP, Pyomo, OR-Tools) without requiring additional LLM calls, enhancing efficiency.
   Additionally, ORPilot incorporates self-correcting retry loops that utilize solver tracebacks to identify and rectify errors in the optimization process. The architecture is designed to be robust against the complexities of real-world data and problem specifications.

Results
In evaluations against traditional academic benchmarks—IndustryOR, NL4OPT, and NLP4LP—ORPilot demonstrated superior performance. Specifically, it outperformed state-of-the-art tools in accuracy on the IndustryOR benchmark, achieving a notable improvement in model fidelity. On the NL4OPT and NLP4LP benchmarks, ORPilot delivered performance that was comparable to existing solutions, indicating its effectiveness in handling diverse optimization tasks. The paper provides quantitative metrics, although specific numerical results are not detailed in the abstract.

Limitations
The authors acknowledge several limitations, including the potential for the conversational agent to misinterpret user inputs, which could lead to incomplete or incorrect problem specifications. Additionally, while the system is designed for a wide range of solvers, the performance may vary depending on the specific characteristics of the optimization problem and the solver used. The paper does not address the computational overhead associated with the self-correcting retry loops, which may impact efficiency in time-sensitive applications.

Why it matters
The development of ORPilot has significant implications for the field of operational research and AI applications in business. By enabling the translation of complex, real-world problems into optimization models, ORPilot enhances the accessibility and usability of OR techniques for practitioners. This work paves the way for future research on integrating LLMs with operational research, particularly in environments where data is messy and problem specifications are not well-defined. The system’s open-source nature also encourages further exploration and adaptation by the research community.

Authors: Guangrui Xie
Source: arXiv:2605.02728
URL: https://arxiv.org/abs/2605.02728v1