Improved Clustering Algorithm for Design Structure Matrix

Fredrik Börjesson · Katja Hölttä‐Otto

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Product Development and Customization Design Education and Practice Advanced Clustering Algorithms Research

Abstract

For clustering a large Design Structure Matrix (DSM), computerized algorithms are necessary. A common algorithm by Thebeau uses stochastic hill-climbing to avoid local optima. The output of the algorithm is stochastic, and to be certain a very good clustering solution has been obtained, it may be necessary to run the algorithm thousands of times. To make this feasible in practice, the algorithm must be computationally efficient. Two algorithmic improvements are presented. Together they improve the quality of the results obtained and increase speed significantly for normal clustering problems. The proposed new algorithm is applied to a cordless handheld vacuum cleaner.

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References

2001Applying the design structure matrix to system decomposition and integration problems: a review and new directions 1994A model-based method for organizing tasks in product development 2002Design for variety: developing standardized and modularized product platform architectures 1997The Power of Product Platforms 2001Modular product architecture 2007An information theoretic method for developing modular architectures using genetic algorithms 2007Change Propagation Analysis in Complex Technical Systems 2008Systematic module and interface definition using component design structure matrix

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2017Product modular analysis with design structure matrix using a hybrid approach based on MDS and clustering 2021Understanding design change propagation in complex engineering systems using a digital twin and design structure matrix 2017Multilevel Flow-Based Markov Clustering for Design Structure Matrices 2013Optimizing product architecture for complex design 2013Linking 10 Years of Modular Design Research: Alternative Methods and Tool Chain Sequences to Support Product Platform Design

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