利用自然启发算法和数字人体建模工具对装配布局进行多目标优化。

Andreas Lind, V Elango, L Hanson, D Högberg, D Lämkull, P Mårtensson, A Syberfeldt
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引用次数: 0

摘要

职业应用在工业 5.0 的背景下,我们的研究通过将多目标优化与自然启发算法和数字人类建模工具相结合,推进了制造工厂的布局规划。这种方法旨在克服传统规划方法的局限性,因为传统规划方法通常依赖于工程师的专业知识和来自公司各职能部门的输入,从而导致流程缓慢和人为错误风险。通过将多目标优化集中在三个主要目标上,我们的方法促进了客观高效的布局规划,同时考虑到了工人的福利和系统性能效率。通过一个踏板车装配工位布局案例,我们展示了如何将布局规划转变为透明、跨学科和自动化的活动。该方法提供了多目标决策支持,在制造工厂布局设计实践中迈出了重要一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Objective Optimization of an Assembly Layout Using Nature-Inspired Algorithms and a Digital Human Modeling Tool.

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.

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