Scalability-constrained decision optimization requires computational systems to generate feasible, interpretable, and resource-aware decisions while operating under increasing problem dimensionality. Conventional optimization approaches provide formal constraint handling but may become difficult to integrate with heterogeneous semantic information, human-oriented requirements, and rapidly changing decision environments. This paper proposes ScaleGen, a conceptual combinatorial generative Large Language Model (LLM) framework designed to integrate semantic reasoning, combinatorial candidate generation, constraint evaluation, and scalability-aware decision selection. The framework is theoretically positioned at the intersection of functionalism, conceptual-space semantics, human-like computing, neural-symbolic reasoning, and distributed intelligence. The methodological architecture separates natural-language problem interpretation from structured decision generation and feasibility validation, thereby reducing the risk of treating generative fluency as optimization validity. The framework further incorporates scalability indicators into candidate evaluation so that solution quality is assessed not only through objective performance but also through computational and operational feasibility. The analysis indicates that semantic representations can improve the translation of human requirements into structured decision variables, while symbolic constraint validation provides an essential control layer for generative outputs. The framework also identifies an important role for predictive scheduling perspectives in connecting generated decisions with time-sensitive project constraints (Geo Philip, 2026). The resulting model provides a research-oriented foundation for scalable decision optimization in complex environments while acknowledging limitations concerning computational cost, validation reliability, model dependence, and the absence of empirical benchmarking in the present conceptual study.