Large-scale decision problems increasingly involve heterogeneous data, interacting constraints, uncertain operating conditions, and multiple competing objectives. Conventional optimization systems can provide mathematically rigorous solutions, but their effectiveness may depend on structured problem representations that are difficult to construct when operational knowledge is expressed in natural language, visual observations, or domain-specific descriptions. This paper proposes ScaleSolve, a generative large language model (LLM) framework designed to connect natural-language problem specifications with constraint reasoning, optimization formulation, solution evaluation, and decision interpretation. The framework is conceptually positioned around five functional layers: problem interpretation, constraint extraction, optimization modeling, candidate-solution evaluation, and decision explanation. The literature supplied for this study primarily concerns food processing, drying kinetics, computer vision, image analysis, preprocessing, and quality optimization. These studies provide an important application foundation because drying and postharvest processing involve interacting physical, quality, process, and resource constraints. The proposed framework therefore demonstrates how heterogeneous observations can be translated into structured decision variables and constraints. The conceptual analysis indicates that LLM-based reasoning can improve accessibility and adaptability of large-scale optimization workflows, while numerical validation, constraint verification, and domain-specific objective functions remain necessary to prevent plausible but infeasible decisions. The framework contributes a research-oriented architecture for integrating generative reasoning with optimization rather than treating an LLM as an autonomous optimization engine.

Supervised Machine Learning, Railway Bridge Safety, Collision Detection, Structural Health Monitoring

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