Democratizing Large-Scale Re-Optimization With LLM-Guided Model Patche…

Abstract:Optimization fashions developed by operations analysis (OR) experts are often deployed as decision-support programs in industrial settings. However, actual-world environments are dynamic, with evolving enterprise rules and unexpected perturbations. In such contexts, finish customers should ideally re-optimize models to get well feasible and implementable options, usually with out access to the unique model builders. This paper introduces an agentic re-optimization framework wherein a large language mannequin (LLM) acts as an OR expert, dynamically supporting end customers by pure-language interplay. 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 occasion to return implementable solutions. The toolbox leverages primal info, including historical solutions, legitimate inequalities, solver configurations, and metaheuristics, to accelerate re-optimization whereas preserving resolution high quality. The proposed framework allows interactive and steady adaptation of deployed optimization models, reducing dependence on OR experts, and bettering the sustainability of determination-support techniques. Extensive experiments on two complementary large-scale actual-world case studies exhibit the effectiveness and scalability of the proposed framework. The first considers on-line provide chain re-optimization, the place options have to be generated quickly while remaining near the deployed plan, whereas the second focuses on offline university exam scheduling, the place solution quality is prioritized over runtime. Results show that the toolbox-driven structure considerably improves computational efficiency by way of primal-based mostly and solver-conscious re-optimization strategies, USA whereas the structured patch-based mostly updates improve interpretability and traceability of model modifications.

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