Exploring Adaptable Office Fit-Out Layouts Using Configurable Floor Plan Graphs and Large Language Models

Abstract: Conventional office fit-outs are designed for fixed requirements, which limits their long-term adaptability. This paper introduces a workflow that integrates configurable floor plan graphs with contextual reasoning from large language models (LLMs) for flexible office layout exploration. Interior space is represented as a graph that encodes geometry, adjacency, and accessibility, and is parameterised by binary partition decisions. For each candidate configuration, an LLM infers program assignments across multiple scenarios, and solutions are evaluated in a multi-objective combinatorial optimisation. Results show configurations with feasible performance across scenarios, suggesting that LLM-based contextual program assignment can complement solver-based methods focused on quantitative optimisation.