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TWO-LEVEL OPTIMIZATION OF PRODUCT FAMILIES: APPLICATION TO A PRODUCT FAMILY OF WATER HOSE BOXES

Published online by Cambridge University Press:  27 July 2021

Sebastian Rötzer ,
Martin Le Bourgeois ,
Dominik Thoma  and
Markus Zimmermann
Sebastian Rötzer*
Affiliation:
Technische Universität München, Germany
Martin Le Bourgeois
Affiliation:
ID-Consult GmbH, Germany
Dominik Thoma
Affiliation:
ID-Consult GmbH, Germany
Markus Zimmermann
Affiliation:
Technische Universität München, Germany
*
Rötzer, Sebastian Josef, Technische Universität München, Mechanical Engineering, Germany, roetzer@pl.mw.tum.de

Abstract

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Increasing product complexity and individual customer requirements make the design of optimal product families difficult. Numerical optimization supports optimal design but must deal with the following challenges: many design variables, non-linear or discrete dependencies, and many possibilities of assigning shared components to products. Existing approaches use simplifications to alleviate those challenges. However, for use in industrial practice, they often use irrelevant commonality metrics, do not rely on the actual design variables of the product, or are unable to treat discrete variables. We present a two-level approach: (1) a genetic algorithm (GA) to find the best commonality scheme (i.e., assignment scheme of shared components to products) and (2) a particle swarm optimization (PSO) to optimize the design variables for one specific commonality scheme. It measures total cost, comprising manufacturing costs, economies of scales and complexity costs. The approach was applied to a product family consisting of five water hose boxes, each of them being subject to individual technical requirements. The results are discussed in the context of the product family design process.

Keywords

Product familiesNumerical methodsProduct modelling / modelsComplexityOptimisation
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 3259 - 3268
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), 2021. Published by Cambridge University Press

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