# Generative Artificial Intelligence-Enabled Facility Layout Design Paradigm
## 2 Evolution of Facility Layout Design Paradigms
### Facility Layout Design 4.0: AI-Based Generation Methods  
The origin of facility layout design 4.0 can be traced to generative design, an innovative methodology utilizing algorithms and computational power to automatically generate a diverse array of design alternatives based on specified parameters and constraints. Each generative design (GD) implementation project presents unique requirements, leading to distinct pathways and digital workflows without a generalized GD framework. The algorithms utilized in GD are profoundly influenced by the defined solution space and the nature of the user input or interaction. Moreover, formulating the design space requires tacit knowledge, interdisciplinary expertise, and the careful consideration of the trade-offs among multiple objectives [62]. A number of studies have explored layout generation, motivated by the various aims of simulation and design, as well as a diverse array of methodologies. A broad differentiation can be made between the methodologies focused on generation for planning and construction purposes and those designed for visual realism, such as in motion pictures and video games. The former group is pragmatically oriented, empowering designers to control options to achieve the optimal layout. This process involves reducing the extensive number of choices available to a defined set of parameters that guide the decision-making process, often grounded in the analysis of existing processes and layouts, with an emphasis on simplifying complexity. For instance, [PERSON] [63] introduced a system designed to generate the spatial arrangement of objects based on domain constraints and relationships. Similarly, [PERSON] et al. [64] represented indoor scenes as dense graphs modeled by graph neural networks. In contrast, generation for visual realism seeks to create experiences with a high level of detail and textured materiality, allowing audiences to become fully immersed [65]. [PERSON] and [PERSON] [66] proposed generative and reinforcement learning frameworks for manufacturing layouts. These works highlight the potential of AI in layout generation but lack integration with Industry 4.0 methodologies. The emergence of Industry 4.0 has brought about a paradigm shift that integrates smart technologies, cyber-physical systems, and semantic interoperability into the heart of manufacturing. In this context, the next evolution of facility layout design, FLD 4.0, is fundamentally AI-based and suitable for reference architecture models for Industry 4.0. Therefore, FLD 4.0 should be based on an I4.0 component-first approach, instead of just taking facilities in physical space as controllable objects. The term ontology originates from philosophy and means the theory of existence. In computer science, ontology is basically defined as a formal structured representation of concepts and their relations [67]. From this point, the AAS explicitly serves as a semantic standard framework for resource-level ontology, providing a uniform digital interface that enables communication between physical assets--such as machines and robots--and higher-level systems, including digital twins and cloud platforms. This interoperability is crucial for enhancing data exchange and integration across the various components of Industry 4.0. Undoubtedly, at the core of FLD 4.0 is the ability of AI algorithms to autonomously generate layout designs, optimize processes, and make real-time adjustments based on continuous streams of data. AI-driven FLD 4.0 is strongly constrained and pragmatically oriented. Furthermore, AI-powered FLD 4.0 can simulate, analyze, and optimize facility layouts based on insights from digital twins [68], predicting future disruptions and adapting in real time. Furthermore, the continuous feedback loop between physical and digital systems allows FLD 4.0 to autonomously adjust layouts as conditions evolve, ensuring a dynamic and responsive manufacturing environment.