## 1 Introduction  
In factory layout planning (FLP), the strategic arrangement of resources and equipment within manufacturing facilities is crucial to ensure efficient manufacturing processes. Systematic layout planning (SLP) is widely recognized as the state-of-the-art method for factory layout design and planning [1]. Traditionally, SLP focuses on how to plan and optimize positions of rectangular or quadratic resources and equipment in a 2D top-view environment; emphasizing the shortest distance between rectangular and quadratic resources as the primary optimization objective in such layouts, see, e.g., [2, 3, 4, 5, 6, 7]. While effective for space management in two dimensions, these approaches neglect critical considerations of the vertical dimensions of layout planning, limiting their ability to fully account for human-centric factors, spatial aspects related to the height of resources and operational flexibility.  
FLP involves multiple, often conflicting, objectives, such as minimizing space utilization, improving production throughput, optimizing material flow, reducing bottlenecks, and ensuring worker safety and well-being. These factors must be balanced alongside equipment requirements, production needs, capacity, logistics, and safety considerations. The inherent complexity of FLP makes it a classic example of a multi-objective optimization (MOO) problem, hence resulting in solutions that often represent trade-offs between competing objectives [8].  
The increasing focus on human-centricity in design emphasizes the need to incorporate human factors into the process. Prioritizing worker well-being and safety alongside productivity is essential, particularly in the context of Industry 5.0, which motivates the creation of more human-centered, sustainable, and resilient industrial environments [9]. Unlike Industry 4.0, which focuses primarily on automation and data exchange, Industry 5.0 emphasizes collaboration between humans and machines, integrating social and environmental considerations to enhance worker well-being and system performance [10]. Integrating human factors early in FLP allows for proactive assessments of worker well-being, mitigating risks such as musculoskeletal disorders (MSDs).  
Human simulation tools, known as digital human modeling (DHM) tools, can facilitate sophisticated simulations to evaluate, verify, and visualize potential worker well-being in virtual environments. DHM tools like Siemens Jack [11], RAMSIS [12], SAMMIE [13], SANTOS [14], and IPS IMMA [15], each offer unique simulation capabilities that contribute valuable insights into worker well-being assessments within virtual environments [16]. While such simulations provide foundational insights into human well-being optimization, they frequently focus on smaller environments, such as cockpits or workbenches, rather than expansive settings such as factory layouts. Although contemporary DHM tools primarily focus on simulating concerns related to human well-being, e.g., simulating humans performing work tasks and assessing consequential risks for MSDs, it is important to recognize that human factors and ergonomics (HFE) encompass both human well-being and system performance [17]. This broader scope underscores the commitment of the HFE discipline to enhancing both individual and systemic outcomes.