LLM-OR optimization method (LLMOR-Opt) for generating optimal solutions in asset maintenance decision-making

Authors:
Preprint
Publisher website
Google Scholar
Abstract
Asset maintenance decision-making in complex systems requires the integration of heterogeneous operational information, formal optimization models, and reliable decision-support mechanisms. However, a significant portion of maintenance knowledge is expressed in natural language, creating a gap between real-world problem descriptions and the structured inputs required by Operations Research (OR) models. This paper presents LLM-OR Optimization (LLMOR-Opt), a reproducible method that combines Large Language Models (LLMs) and OR to support the generation of optimal asset maintenance decisions. The method uses LLMs to interpret maintenance requirements, formulate optimization problems, and create a structured intermediate representation in JavaScript Object Notation (JSON). Human validation is incorporated before generating executable Python code, while computational validation is performed before solver execution. OR solvers are then responsible for obtaining feasible or optimal solutions, which are subsequently translated into natural language explanations to support maintenance planners. By separating semantic interpretation, mathematical formulation, computational implementation, optimization, and explanation, LLMOR-Opt improves traceability and provides a structured approach for integrating generative AI with formal optimization methods in asset maintenance decision-making.• Integrates LLM-based interpretation with formal OR optimization.• Introduces human and computational validation checkpoints.• Provides traceable and explainable maintenance decision support.