Högskolan i Skövde

Factory layout planning involves allocating resources and arranging equipment in manufacturing facilities to enhance system performance and ensure a safe work environment. Integrating digital human modeling tools into factory layout planning facilitates early worker well-being analysis, mitigating musculoskeletal disorders. This paper presents methods for modeling factory layout planning as a multi-objective reinforcement learning problem, leveraging digital human modeling-based simulations. 

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.

Planning and designing factory layouts are frequently performed within virtual environments, relying on inputs from various cross-disciplinary activities e.g., product development, manufacturing process planning, resource descriptions, ergonomics, and safety. The success of this process heavily relies on the expertise of the practitioners performing the task. Studies have shown that layout planning often hinges on the practitioners’ knowledge and interpretation of current rules and requirements. As there is significant variability in this knowledge and interpretation, there is a risk that decisions are made on incorrect grounds. Consequently, the layout planning process depends on individual proficiency. In alignment with Industry 4.0 and Industry 5.0 principles, there is a growing emphasis on providing practitioners involved in industrial development processes with efficient decision support tools. This paper presents a digital support function integrated into a virtual layout planning tool, developed to support practitioners in considering current rules and requirements for space claims in the layout planning process. This digital support function was evaluated by industry practitioners and stakeholders involved in the factory layout planning process. This initiative forms part of a broader strategy to provide advanced digital support to layout planners, enhancing objectivity and efficiency in the layout planning process while bridging cross-disciplinary gaps.

The manufacturing factory layout planning process is commonly supported by the use of digital tools, enabling creation and testing of potential layouts before being realised in the real world. The process relies on engineers’ experience and inputs from several cross-disciplinary functions, meaning that it is subjective, iterative and prone to errors and delays. To address this issue, new tools and methods are needed to make the planning process more objective, efficient and able to consider multiple objectives simultaneously. This work suggests and demonstrates a simulation-based multi-objective optimisation approach that assists the generation and assessment of factory layout proposals, where objectives and constraints related to safety regulations, workers’ well-being and walking distance are considered simultaneously. The paper illustrates how layout planning for a logistics area can become a cross-disciplinary and transparent activity, while being automated to a higher degree, providing objective results to facilitate informed decision-making.

OCCUPATIONAL APPLICATIONS

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.

TECHNICAL ABSTRACT

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 overall objective of the planning and design process for a factory layout is to generate and assess layout design proposals and choose the alternative that enables the factory to operate according to set performance targets while providing a safe work environment. The factory layout is frequently planned and designed in a virtual environment. This facilitates creation, simulation, visualization, and assessing potential future outcomes of the factory setup, without the need of intervening with physical objects. However, the planning and design of factory layouts is typically based on the experience of the expert and software tool user undertaking the planning and design activity. The activity depends on information generated by several cross-disciplinary functions and experts in, for example, product development, process planning, resource descriptions, ergonomics, and safety. The information provided by these functions and experts is also frequently generated with several software applications and depends on the experience of the software tool user performing their specific activity. This experience-based, manual, and serial approach to plan and design factory layouts, considering a wide range of parameters, is a cumbersome, non-integrated, and subjective process with a high risk of human error and faulty inputs and updates. The aim of this research is to develop methods, demonstrators, and a framework to support multiobjective planning and design of factory layouts. The purpose is to bridge gaps between the cross-disciplinary functions and experts involved in the planning and design of factory layouts. The research presents and tests ways to assist the software tool user when performing factory layout tasks. One approach is by adding rules and regulations to resources and equipment in the virtual environment. Further, the research demonstrates how simulation-based multi-objective optimization can assist the planning and design of factory layouts, supporting the generation and assessment of a multitude of layout design proposals, based on defined objectives and constraints of factory layouts. The methods, demonstrators, and framework developed in the research enhance quality and objectivity and provide risk mitigation in the process of planning and designing factory layouts.

Det övergripande målet med planerings- och designprocessen för fabrikslayouter är att generera och bedöma layoutlösningar och välja den lösning som uppfyller fastställda prestandamål samtidigt som en säker arbetsmiljö säkerställs. Fabrikslayouter planeras och designas oftast med hjälp av virtuella verktyg och miljöer, vilket möjliggör simulering, visualisering och utvärdering av potentiella framtida förslag långt innan de förverkligas fysiskt. Planering och design av fabrikslayouter baseras vanligtvis på erfarenheten hos de experter och mjukvaruanvändare som utför planeringsuppgifterna. Processen är ofta beroende av information från flera tvärdisciplinära funktioner och experter, såsom produktutveckling, processplanering, resursbeskrivningar, ergonomi och säkerhet. Informationen från dessa funktioner och experter genereras också med hjälp av olika mjukvaruapplikationer och bygger på den erfarenhet som experterna har inom sina respektive områden. Detta erfarenhetsbaserade, manuella och sekventiella tillvägagångssätt för att planera och designa fabrikslayouter, med hänsyn till ett brett spektrum av parametrar, är ofta en krävande, icke-integrerad och subjektiv process med hög risk för mänskliga fel samt felaktiga inmatningar och uppdateringar. Syftet med denna forskning är att utveckla metoder, demonstratorer och ramverk för att stödja tvärdisciplinär planering och design av fabrikslayouter. Målet är att överbrygga klyftorna mellan de funktioner och experter som är involverade i planerings- och designprocessen. Forskningen presenterar och testar sätt att assistera mjukvaruanvändare när fabrikslayoutuppgifter utförs. Ett exempel är att integrera regler och föreskrifter för resurser och utrustning i den virtuella miljön under layoutplaneringen. Vidare demonstreras hur simuleringsbaserad multivariabel optimering kan stödja planering och design av fabrikslayouter genom att möjliggöra generering och bedömning av ett flertal layoutförslag baserade på definierade mål och begränsningar. De metoder, demonstratorer och det ramverk som utvecklats inom forskningen förbättrar kvaliteten och objektiviteten och minskar riskerna i processen för planering och design av fabrikslayouter.

Factory layouts are frequently planned and designed in virtual environments, based on the experience of the layout planner. This planning and design process depends on information from several cross-disciplinary activities performed by several functions and experts, e.g., product development, manufacturing process planning, resource descriptions, ergonomics, and safety. Additionally, the layout planner also needs to consider applicable rules and regulations. This experience-based and manual approach to plan and design factory layouts, considering a multitude of inputs and parameters, is a cumbersome iterative process with a high risk of human error and faulty inputs and updates. The general trend in industry is to automate and assist users with their tasks and activities, deriving from concepts such as Industry 4.0 and Industry 5.0. This paper presents and demonstrates how digital support for rules and regulations can assist layout planners in factory layout work. The objective is to support the layout planner in accounting for area/volume reservations required to comply with rules and regulations for workers and equipment in the factory layout. This is a step in a wider initiative to provide enhanced digital support to layout planners, making the layout planning and design process more objective and efficient, and bridge gaps between cross-disciplinary planning and design activities.

The factory layout is frequently planned in virtual environments, based on the experience of software tool users. This planning process is cumbersome and iterative to collect the necessary information, with a high risk of faulty inputs and updates. The digital twin concept has been introduced in order to speed up information sharing within a company; it relies on connectivity. However, the concept is often misunderstood as just a 3D model of a virtual object, not including connectivity. The aim of this paper is to present an extended virtual and physical engineering communication framework including four concepts: digital model, digital pre-runner, digital shadow, and digital twin. The four concepts are demonstrated and described in order to facilitate understanding how data exchange between virtual and physical objects can work in the future and having up-to date virtual environments enables simulating, analysing, and improving on more realistic and accurate datasets.

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.

In the design process of products and production systems, the activity to systematically evaluate initial alternative design concepts is an important step. The digital human modeling (DHM) tools include several different types of assessment methods in order to evaluate product and production systems. Despite this, and due to the fact that a DHM tool in essence is a computer-supported design and analysis tool, none of the DHM tools provide the functionality to, in a systematic way, use the results generated in the DHM tool to compare design concepts between each other. The aim of this paper is to illustrate how a systematic concept evaluation method is integrated in a DHM tool, and to exemplify how it can be used to systematically assess design alternatives. Pugh´s method was integrated into the IPS software with LUA scripting to systematically compare design concepts. Four workstation layout concepts were generated by four engineers. The four concepts were systematically evaluated with two methods focusing on human well-being and two methods focusing on system performance and cost. The result is very promising. The demonstrator illustrates that it is possible to perform a systematic concept evaluation based on human well-being, overall system performance, and other parameters, where some of the data is automatically provided by the DHM tool and other data manually. The demonstrator can also be used to evaluate only one design concept, where it provides the software user and the decision maker with an objective and visible overview of the success of the design proposal from the perspective of several evaluation methods.