# Generative Artificial Intelligence-Enabled Facility Layout Design Paradigm
## 1 Introduction
In this environment, a DT serves as a virtual replica of a physical entity, continuously updated to reflect the state of its real-world counterpart, further enabling operational efficiency and innovation [30; 31; 32; 33]. However, as industrial environments undergo rapid transformation, the increasing complexity and connectivity of these systems have introduced new challenges [34; 35]. This evolving environment calls for more advanced, flexible, and data-driven approaches to decision making, ensuring that smart factories and cyber-physical production systems can thrive in the face of continuous change [36]. Following the vision of Industry 4.0, we believe that the following gaps remain in the FLD field. First, in the existing methods, facility layout, data, models, information, and algorithms are highly isolated. The isolation or fragmentation of digital 3D product models, the functional capacity of equipment, and the process data or domain knowledge in the current facility layout methods has led to inefficiencies and missed optimization opportunities. Functional data, including machine capabilities and production processes, are typically maintained separately, hindering efficient space utilization and workflow optimization [37]. Furthermore, fragmented equipment models and static layouts fail to adapt to changes, while disconnects between strategic and operational planning stages lead to suboptimal facility designs [38]. Optimization models often focus on isolated sub-problems and lack real-time adaptability. Second, the existing facility layout methods are primarily human-driven, with limited integration between human intelligence and artificial intelligence (AI). Whether using a top-down or bottom-up approach, these methods involve stakeholders from various levels with their own localized concerns, information, and expertise. As a result, planners who cannot grasp global information and knowledge are required to make multiple iterations, which delays the final design and introduces inefficiencies. Traditional approaches often use rule-based or heuristic methods, with limited automation or AI assistance. This lack of synergy limits the effectiveness of AI. AI agentic workflows will drive significant progress in tool use, planning and multiagent collaboration, simulation capabilities, and automated optimization [39]. Third, the existing facility layout methods lack standardized frameworks, leading to inconsistencies and difficulties in the integration and interoperability of multi-source heterogeneous information. This lack of standardization and interoperability hinders the effective integration of modern technologies and best practices, preventing facilities from fully utilizing the capabilities of Industry 4.0 [40]. Specifically, the absence of clear specifications or adaptable models makes it challenging to incorporate automation, IoT devices, and real-time data streams. Without AI-driven tools and standardized processes, the existing layouts often struggle to deliver the efficiency, flexibility, and scalability required to stay competitive in a rapidly changing technological landscape. To address these challenges, it is essential to adopt integrated, intelligent systems that can automate and optimize the layout planning process. These systems would ensure greater standardization, facilitate real-time end-to-end design, and enable seamless integration with advanced technologies such as digital twins and the industrial IoT, allowing facilities to better meet the demands of the Industry 4.0 era. To narrow the above gaps, this work provides contributions from three aspects:  
* This work outlines the evolution of facility layout design through three historical paradigms: experience-based methods (FLD 1.0), operations-research-based approaches (FLD 2.0), and simulation-based engineering (FLD 3.0), especially positioning FLD 4.0 as a generative-AI-driven and Industry 4.0-compliant approach.