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A Weighted Set Cover Problem for Product Family Design to Maximize the Commonality of Products

Part of: Product Architecture and Modularization

Published online by Cambridge University Press:  26 July 2019

Hyeongmin Han ,
Sehyun Chang  and
Harrison Kim
Hyeongmin Han
Affiliation:
University of Illinois at Urbana-Champaign;
Sehyun Chang
Affiliation:
Hyundai Motor Company
Harrison Kim*
Affiliation:
University of Illinois at Urbana-Champaign;
*
Contact: Kim, Harrison, University of Illinois at Urbana-Champaign, Industrial and Enterprise Systems Engineering, United States of America, hmkim@uiuc.edu

Abstract

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In product family design, the commonality of products and performance are competing objectives when designers build platforms. The commonality makes it efficient to manufacture products while it will cause performance loss of products. In this paper, we assume that performance functions evaluate the performance of a product. Targets of performance functions are set for each product depending on the product's property. The designs that satisfy the target of performance functions are denoted as ‘good’ design points. By using ‘good’ design points, a weighted set cover problem (WSC) is applied to formulate the combinatorial optimization problem, which maximizes the commonality by minimizing the number component attributes. A recursive greedy algorithm is proposed to handle the general cost function in the problem for product family design. The formulation and the algorithm are tested for a linear three-degree-of-freedom (3DOF) model. In numerical experiment, the proposed method determines optimal values of the components which are suspensions, stabilizer bars, and tires in the vehicle model.

Keywords

Product familiesPlatform strategiesOptimisation
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 2951 - 2960
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

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