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基于自然启发式算法和数字人体建模工具的装配布局多目标优化。

Multi-Objective Optimization of an Assembly Layout Using Nature-Inspired Algorithms and a Digital Human Modeling Tool.

机构信息

Scania CV AB, Södertälje, Sweden.

School of Engineering Science, University of Skövde, Skövde, Sweden.

出版信息

IISE Trans Occup Ergon Hum Factors. 2024 Jul-Sep;12(3):175-188. doi: 10.1080/24725838.2024.2362726. Epub 2024 Jun 12.

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.

摘要

职业应用 在工业 5.0 的背景下,我们通过将多目标优化与受自然启发的算法和数字人体建模工具相结合,推进了制造工厂布局规划。这种方法旨在克服传统规划方法的局限性,传统方法通常依赖于工程师的专业知识和公司各部门的输入,导致流程缓慢且容易出现人为错误。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有效的布局规划,同时考虑了工人的幸福感和系统性能效率。通过一个脚踏车组装站布局案例来说明,我们展示了布局规划如何转变为透明、跨学科和自动化的活动。该方法提供了多目标决策支持,是制造工厂布局设计实践的重大进步。

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