Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches

2026-05-18

AI Summary

This work addresses the challenge of rapidly adapting deployed operations research optimization models to new constraints or disturbances in dynamic real-world environments, where current approaches heavily rely on expert intervention. We propose a novel large language model (LLM)-based agent framework that embeds an LLM as an operations research expert within the reoptimization pipeline. The framework leverages natural language interaction to interpret evolving requirements, automatically generates structured model patches, and incorporates information from prior solutions to design acceleration strategies, enabling efficient and interpretable continuous adjustment. By integrating valid inequalities, solver tuning, and metaheuristics, the method demonstrates strong empirical performance in both online supply chain reoptimization and offline university exam timetabling, significantly improving computational efficiency while preserving solution quality and enabling rapid response with minimal expert dependency.

Abstract

Optimization models developed by operations research (OR) experts are often deployed as decision-support systems in industrial settings. However, real-world environments are dynamic, with evolving business rules, previously overlooked constraints, and unforeseen perturbations. In such contexts, end users must rapidly re-optimize models to recover feasible and implementable solutions. This paper introduces an agentic re-optimization framework in which a large language model (LLM) acts as an OR expert, dynamically supporting end users through natural-language interaction. The LLM translates user prompts into structured updates of the underlying optimization model, selects suitable re-optimization techniques from an optimization toolbox, and solves the resulting instance to return implementable solutions. The toolbox leverages primal information, including historical solutions, valid inequalities, solver configurations, and metaheuristics, to accelerate re-optimization while preserving solution quality. The proposed framework enables interactive and continuous adaptation of deployed optimization models, reducing dependence on OR experts and improving the sustainability of decision-support systems. Extensive experiments on two complementary large-scale real-world case studies demonstrate the effectiveness and scalability of the proposed framework. The first considers online supply chain re-optimization, where solutions must be generated rapidly while remaining close to the deployed plan, whereas the second focuses on offline university exam scheduling, where solution quality is prioritized over runtime. Results show that the toolbox-driven architecture significantly improves computational efficiency through primal-based and solver-aware re-optimization techniques, while the structured patch-based updates improve interpretability and traceability of model modifications.

Problem

• re-optimization
• dynamic environments
• decision-support systems
• model adaptation
• large-scale optimization

Innovation

• LLM-guided re-optimization
• model patches
• primal-based acceleration
• optimization toolbox
• interactive decision support