Generative Artificial Intelligence-Enabled Facility Layout Design Paradigm

Facility layout design (FLD) is critical for optimizing manufacturing efficiency, yet traditional approaches struggle with complexity, dynamic constraints, and fragmented data integration. This study proposes a generative-AI-enabled facility layout design, a novel paradigm aligning with Industry 4.0, to address these challenges by integrating generative artificial intelligence (AI), semantic models, and data-driven optimization. The proposed method evolves from three historical paradigms: experience-based methods, operations research, and simulation-based engineering. The metamodels supporting the generative-AI-enabled facility layout design is the Asset Administration Shell (AAS), which digitizes physical assets and their relationships, enabling interoperability across systems. Domain-specific knowledge graphs, constructed by parsing AAS metadata and enriched by large language models (LLMs), capture multifaceted relationships (e.g., spatial adjacency, process dependencies, safety constraints) to guide layout generation. The convolutional knowledge graph embedding (ConvE) method is employed for link prediction, converting entities and relationships into low-dimensional vectors to infer optimal spatial arrangements while addressing data sparsity through negative sampling. The proposed reference architecture for generative-AI-enabled facility layout design supports end-to-end layout design, featuring a 3D visualization engine, AI-driven optimization, and real-time digital twins. Prototype testing demonstrates the system’s end-to-end generation ability from requirement-driven contextual prompts and extensively reduced complexity of modeling, integration, and optimization. Key innovations include the fusion of AAS with LLM-derived contextual knowledge, dynamic adaptation via big data streams, and a hybrid optimization approach balancing competing objectives. The 3D layout generation results demonstrate a scalable, adaptive solution for storage workshops, bridging gaps between isolated data models and human–AI collaboration. This research establishes a foundational framework for AI-driven facility planning, offering actionable insights for AI-enabled facility layout design adoption and highlighting future directions in the generative design of complex engineering.