Multi-Objective Design Optimization for Product Platform and Product Family Design Using Genetic Algorithms
Multi-Objective Design Optimization for Product Platform and Product Family Design Using Genetic Algorithms
复制标题
使用遗传算法进行产品平台和产品族设计的多目标设计优化
DOI：
10.1115/detc2005-84905
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
P. Reed
中科院分区：
文献类型：
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作者：
Satish V. K. Akundi;T. Simpson;P. Reed
许多公司正在使用产品系列和基于平台的产品开发来降低成本和上市时间，同时增加产品种类和定制。多目标优化正日益成为支持产品平台和产品族设计的有力工具。提出了一种基于遗传算法的产品族优化设计方法，并以通用电机为例说明了该方法的应用。使用一个适当的设计变量的表示，并通过采用一个合适的配方的遗传算法，一个阶段的方法，产品族设计可以实现，不需要先验的平台决策，消除了需要更高层次的问题特定的领域知识。使用多目标算法优化产品平台为设计师提供了一个帕累托解决方案集，可用于根据不同目标之间的权衡做出更好的决策。介绍了两种非支配排序遗传算法，即NSGA-Ⅱ和e-NSGA-Ⅱ，并比较了它们的性能。与使用这些算法的实施挑战进行了讨论。与现有的基准设计结果的比较表明，所提出的多目标遗传算法的性能优于传统的单目标优化技术，同时为设计人员提供更多的信息，以支持产品族设计过程中的决策。
Many companies are using product families and platform-based product development to reduce costs and time-to-market while increasing product variety and customization. Multi-objective optimization is increasingly becoming a powerful tool to support product platform and product family design. In this paper, a genetic algorithm-based optimization method for product family design is suggested, and its application is demonstrated using a family of universal electric motors. Using an appropriate representation for the design variables and by adopting a suitable formulation for the genetic algorithm, a one-stage approach for product family design can be realized that requires no a priori platform decision-making, eliminating the need for higher-level problem-specific domain knowledge. Optimizing product platforms using multi-objective algorithms gives the designer a Pareto solution set, which can be used to make better decisions based on the trade-offs present across different objectives. Two Non-Dominated Sorting Genetic Algorithms, namely, NSGA-II and e-NSGA-II, are described, and their performance is compared. Implementation challenges associated with the use of these algorithms are also discussed. Comparison of the results with existing benchmark designs suggests that the proposed multi-objective genetic algorithms perform better than conventional single-objective optimization techniques, while providing designers with more information to support decision making during product family design.Copyright © 2005 by ASME