In the context of Industry 5.0, our study advances manufacturing factory layout planning by integrating multi-objective optimization with nature-inspired algorithms and a digital human modeling tool. This approach aims to overcome the limitations of traditional planning methods, which often rely on engineers' expertise and inputs from various functions in a company, leading to slow processes and risk of human errors. By focusing the multi-objective optimization on three primary targets, our methodology promotes objective and efficient layout planning, simultaneously considering worker well-being and system performance efficiency. Illustrated through a pedal car assembly station layout case, we demonstrate how layout planning can transition into a transparent, cross-disciplinary, and automated activity. This methodology provides multi-objective decision support, showcasing a significant step forward in manufacturing factory layout design practices. Rationale: Integrating multi-objective optimization in manufacturing layout planning addresses simultaneous considerations of productivity, worker well-being, and space efficiency, moving beyond traditional, expert-reliant methods that often overlook critical design aspects. Leveraging nature-inspired algorithms and a digital human modeling tool, this study advances a holistic, automated design process in line with Industry 5.0. Purpose: This research demonstrates an innovative approach to manufacturing layout optimization that simultaneously considers worker well-being and system performance. Utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Particle Swarm Optimization (PSO) alongside a Digital Human Modeling (DHM) tool, the study proposes layouts that equally prioritize ergonomic factors, productivity, and area utilization. Methods: Through a pedal car assembly station case, the study illustrates the transition of layout planning into a transparent, cross-disciplinary, and automated process. This method offers objective decision support, balancing diverse objectives concurrently. Results: The optimization results obtained from the NSGA-II and PSO algorithms represent feasible non-dominated solutions of layout proposals, with the NSGA-II algorithm finding a solution superior in all objectives compared to the expert engineer-designed start solution for the layout. This demonstrates the presented method's capacity to refine layout planning practices significantly. Conclusions: The study validates the effectiveness of combining multi-objective optimization with digital human modeling in manufacturing layout planning, aligning with Industry 5.0's emphasis on human-centric processes. It proves that operational efficiency and worker well-being can be simultaneously considered and presents future potential manufacturing design advancements. This approach underscores the necessity of multi-objective consideration for optimal layout achievement, marking a progressive step in meeting modern manufacturing's complex demands.

The planning and design process of manufacturing factory layouts is commonly performed using digital tools, enabling engineers to define and test proposals in virtual environments before implementing them physically. However, this approach often relies on the experience of the engineers involved and input from various cross-disciplinary functions, leading to a time-consuming and subjective process with a high risk of human error. To address these challenges, new tools and methods are needed. The Industry 5.0 initiative aims to further automate and assist human tasks, reinforcing the human-centric perspective when making decisions that influence production environments and working conditions. This includes improving the layout planning process by making it more objective, efficient, and capable of considering multiple objectives simultaneously. This research presents a demonstrator solution for layout planning using digital support, incorporating a virtual multi-objective optimization approach to consider safety regulations, area boundaries, workers’ well-being, and walking distance. The demonstrator provides a cross-disciplinary and transparent approach to layout planning for an assembly station in the context of battery production. The demonstrator solution illustrates how layout planning can become a cross-disciplinary and transparent activity while being automated to a higher degree, providing results that support decision-making and balance cross-disciplinary requirements.

This paper presents a decision support approach to enable decision-makers to identify no-preference solutions in multi-objective optimization for factory layout planning. Using a set of trade-off solutions for a battery production assembly station, a decision support method is introduced to select three solutions that balance all conflicting objectives, namely, the solution closest to the ideal point, the solution furthest from the nadir point, and the one that is best performing along the ideal nadir vector. To further support decision-making, additional analyses of system performance and worker well-being metrics are integrated. This approach emphasizes balancing operational efficiency with human-centric design, aligning with human factors and ergonomics (HFE) principles and Industry 4.0–5.0. The findings demonstrate that objective decision support based on Pareto front analysis can effectively guide stakeholders in selecting optimal solutions that enhance both system performance and worker well-being. Future work could explore applying this framework with alternative multi-objective optimization algorithms.