A Clustering Method Using New Modularity Indices and Genetic Algorithm with Extended Chromosomes

DSM 14 Proceedings of the 16th International DSM conference: Risk and Change management in complex systems

Year: 2014
Editor: Marle, F.; Jankovic, M.; Maurer, M.; Schmidt, D. M.; Lindemann, U.
Author: Jung, S.; Simpson, T. W.
Series: DSM
Section: Clustering and Optimization
Page(s): 167-176

Abstract

Module definition entails clustering an original product architecture into independent or coordinated modules. Clustering algorithms based on Design Structure Matrices (DSMs) for defining modules have been widely studied. After reviewing existing clustering algorithms, we introduce simple new metrics that can be used as modularity indices bounded between 0 and 1 and also utilized as the objective functions to obtain optimal DSMs including the maximized interactions within modules and the minimized interactions between modules. As a search strategy for clustering modules, a combinatorial genetic algorithm using a new extended chromosome approach and modified operators for the chromosome is suggested. The module definition results indicated that the proposed clustering method using new modularity indices and genetic algorithm helps obtain optimal modular product architectures more logically.

Keywords: Design Structure Matrix, Module Definition, Modularity, Genetic Algorithm