Abstract：
OCCUPATIONAL APPLICATIONSIn 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.
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.
摘要：
在工业5.0的背景下，我们的研究通过将多目标优化与自然启发的算法和数字人体建模工具相结合来推进制造工厂布局规划。这种方法旨在克服传统规划方法的局限性，通常依赖于工程师的专业知识和公司各种职能的投入，导致缓慢的过程和人为错误的风险。通过将多目标优化集中在三个主要目标上，我们的方法促进客观有效的布局规划，同时考虑工人的福祉和系统性能效率。通过踏板车组装站布局案例进行说明，我们展示了布局规划如何转变为透明的，跨学科,和自动化活动。该方法提供了多目标决策支持，展示了制造工厂布局设计实践的重要一步。
原理：在制造布局计划中集成多目标优化可同时考虑生产率，工人福祉,和空间效率，超越传统，依赖专家的方法，往往忽视关键的设计方面。利用自然启发的算法和数字人体建模工具，这项研究提出了一个整体，自动化设计过程符合工业5.0。目的：本研究展示了一种创新的制造布局优化方法，该方法同时考虑了工人的福祉和系统性能。利用非支配排序遗传算法II（NSGA-II）和粒子群优化（PSO）以及数字人体建模（DHM）工具，这项研究提出了同样优先考虑人体工程学因素的布局，生产力,和面积利用。方法:通过一个踏板车装配站案例,这项研究说明了布局规划向透明的过渡，跨学科,和自动化的过程。该方法提供了客观的决策支持，同时平衡不同的目标。结果：从NSGA-II和PSO算法获得的优化结果代表了布局建议的可行非主导解决方案，与NSGA-II算法在所有目标中找到优于专家工程师设计的布局开始解决方案的解决方案。这证明了所提出的方法可以显着完善布局规划实践。结论:该研究验证了多目标优化与数字人建模相结合在制造布局规划中的有效性。与工业5.0强调以人为本的流程保持一致。它证明了运营效率和工人福祉可以同时考虑，并提出了未来潜在的制造设计进步。这种方法强调了多目标考虑优化布局实现的必要性，标志着在满足现代制造业复杂需求方面迈出了一步。
原理：在制造布局计划中集成多目标优化可同时考虑生产率，工人福祉,和空间效率，超越传统，依赖专家的方法，往往忽视关键的设计方面。利用自然启发的算法和数字人体建模工具，这项研究提出了一个整体，自动化设计过程符合工业5.0。目的：本研究展示了一种创新的制造布局优化方法，该方法同时考虑了工人的福祉和系统性能。利用非支配排序遗传算法II（NSGA-II）和粒子群优化（PSO）以及数字人体建模（DHM）工具，这项研究提出了同样优先考虑人体工程学因素的布局，生产力,和面积利用。方法:通过一个踏板车装配站案例,这项研究说明了布局规划向透明的过渡，跨学科,和自动化的过程。该方法提供了客观的决策支持，同时平衡不同的目标。结果：从NSGA-II和PSO算法获得的优化结果代表了布局建议的可行非主导解决方案，与NSGA-II算法在所有目标中找到优于专家工程师设计的布局开始解决方案的解决方案。这证明了所提出的方法可以显着完善布局规划实践。结论:该研究验证了多目标优化与数字人建模相结合在制造布局规划中的有效性。与工业5.0强调以人为本的流程保持一致。它证明了运营效率和工人福祉可以同时考虑，并提出了未来潜在的制造设计进步。这种方法强调了多目标考虑优化布局实现的必要性，标志着在满足现代制造业复杂需求方面迈出了一步。
