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Multi-Objective Optimization of an Assembly Layout Using Nature-Inspired Algorithms and a Digital Human Modeling Tool
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. Scania CV AB, Södertälje, Sweden. (User Centred Product Design (UCPD))ORCID iD: 0000-0002-7985-0010
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. Scania CV AB, Södertälje, Sweden. (User Centred Product Design (UCPD))ORCID iD: 0000-0003-1390-8803
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (User Centred Product Design (UCPD))ORCID iD: 0000-0002-7232-9353
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (User Centred Product Design (UCPD))ORCID iD: 0000-0003-4596-3815
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2024 (English)In: IISE Transactions on Occupational Ergonomics and Human Factors, ISSN 2472-5838, Vol. 12, no 3, p. 175-188Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2024. Vol. 12, no 3, p. 175-188
Keywords [en]
Multi-objective, optimization, assembly, industry 5.0, factory layouts
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
User Centred Product Design; Virtual Production Development (VPD); VF-KDO
Identifiers
URN: urn:nbn:se:his:diva-23938DOI: 10.1080/24725838.2024.2362726ISI: 001247664700001PubMedID: 38865136Scopus ID: 2-s2.0-85195777525OAI: oai:DiVA.org:his-23938DiVA, id: diva2:1869108
Part of project
Virtual factories with knowledge-driven optimization (VF-KDO), Knowledge Foundation
Funder
Knowledge Foundation, 2018-0011
Note

CC BY 4.0

Taylor & Francis Group an informa business

CONTACT Andreas Lind andreas.lind@scania.com, alt. andreas.lind@his.se Scania CV AB, Södertälje, Sweden

The authors appreciatively thank the support of Scania CV AB, the research school Smart Industry Sweden (20200044) and the research project Virtual Factories with Knowledge-Driven Optimization (2018-0011) funded by the Knowledge Foundation via the University of Skövde. With this support the research was made possible.

Available from: 2024-06-12 Created: 2024-06-12 Last updated: 2024-11-21Bibliographically approved
In thesis
1. Planning and designing manufacturing factory layouts: Applying multi-objective optimization and digital support
Open this publication in new window or tab >>Planning and designing manufacturing factory layouts: Applying multi-objective optimization and digital support
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Abstract [sv]

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.

Place, publisher, year, edition, pages
Skövde: University of Skövde, 2024. p. 224
Series
Dissertation Series ; 62
Keywords
Factory layouts, cross-disciplinary, multi-objective, optimization
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
User Centred Product Design; VF-KDO
Identifiers
urn:nbn:se:his:diva-24728 (URN)978-91-987907-8-8 (ISBN)978-91-987907-7-1 (ISBN)
Public defence
2025-01-17, ASSAR stora scenen, Skövde, 09:00 (English)
Opponent
Supervisors
Funder
Knowledge Foundation
Note

This research was made possible by the research school Smart Industry Sweden and the Virtual Factory with Knowledge-Driven Optimization (VF-KDO) research profile project, both funded by the Knowledge Foundation via the University of Skövde and Scania CV AB. Their support is gratefully acknowledged.

Available from: 2024-11-22 Created: 2024-11-21 Last updated: 2025-01-08Bibliographically approved

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Lind, AndreasElango, VeereshHanson, LarsHögberg, DanLämkull, DanSyberfeldt, Anna

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