Multi-Level Flow-Based Markov Clustering for Design Structure Matrices

2016 ◽  
Author(s):  
Tim (T.) Wilschut ◽  
Pascal (L. F. P.) Etman ◽  
Jacobus (J. E.) Rooda ◽  
Ivo (I. J. B. F.) Adan
Keyword(s):  
Clustering Algorithm ◽  
Visual Representation ◽  
System Structure ◽  
Underlying Structure ◽  
Design Structure Matrix ◽  
Structure Matrix ◽  
Design Structure ◽  
Input Parameters ◽  
Markov Clustering ◽  
Multi Level

For decomposition and integration of systems one requires extensive knowledge on system structure. A Design Structure Matrix (DSM) can provide a simple, compact and visual representation of dependencies between system elements. By permuting the rows and columns of a DSM using a clustering algorithm, the underlying structure of a system can be revealed. In this paper, we present a new DSM clustering algorithm based upon Markov clustering. The developed clustering algorithm is able to cope with the presence of ‘bus’ elements, returns multilevel clusters, is capable of clustering both directed as well as undirected DSMs, and allows the user to control the cluster results by tuning only three input parameters.

Multilevel Flow-Based Markov Clustering for Design Structure Matrices

2017 ◽  
Vol 139 (12) ◽  
Author(s):  
T. Wilschut ◽  
L. F. P. Etman ◽  
J. E. Rooda ◽  
I. J. B. F. Adan
Keyword(s):  
Clustering Algorithm ◽  
Central Processing Unit ◽  
System Structure ◽  
Underlying Structure ◽  
Design Structure Matrix ◽  
Processing Unit ◽  
Central Processing ◽  
Cpu Time ◽  
Design Structure ◽  
Markov Clustering

For decomposition and integration of systems, one needs extensive knowledge of system structure. A design structure matrix (DSM) model provides a simple, compact, and visual representation of dependencies between system elements. By permuting the rows and columns of a DSM using a clustering algorithm, the underlying structure of a system can be revealed. In this paper, we present a new DSM clustering algorithm based upon Markov clustering, that is able to cope with the presence of “bus” elements, returns multilevel clusters, is capable of clustering weighted, directed, and undirected DSMs, and allows the user to control the cluster results by tuning only three input parameters. Comparison with two algorithms from the literature shows that the proposed algorithm provides clusterings of similar quality at the expense of less central processing unit (CPU) time.

Improved Clustering Algorithm for Design Structure Matrix

2012 ◽  
Author(s):  
Fredrik Borjesson ◽  
Katja Hölttä-Otto
Keyword(s):  
Clustering Algorithm ◽  
Hill Climbing ◽  
Design Structure Matrix ◽  
Computationally Efficient ◽  
Vacuum Cleaner ◽  
Local Optima ◽  
Structure Matrix ◽  
Design Structure ◽  
Clustering Problems

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.

Fast Hybrid Genetic Clustering Algorithm for Design Structure Matrix

2013 ◽  
Author(s):  
Fredrik Borjesson ◽  
Ulf Sellgren
Keyword(s):  
Clustering Algorithm ◽  
Minimum Description Length ◽  
Clustering Algorithms ◽  
Optimal Solution ◽  
Design Structure Matrix ◽  
Computer Algorithms ◽  
Structure Matrix ◽  
Design Structure ◽  
Genetic Clustering ◽  
Time Required

Module formation is the step in which a product’s architecture is established in such a way that complex interactions are intra-modular and inter-modular interactions are more simple. If a matrix representation exists, such as a Design Structure Matrix, this involves clustering system entities into groups with strong intra-dependencies. For simple products, clustering may be done manually, but for complex products, computer tools are required. Existing clustering algorithms are either slow, or unable to guarantee a globally optimal solution. To enable iterative work and to make cluster analysis useful also in the detailing steps, efficient and effective computer algorithms are required. This paper presents an efficient and effective Genetic clustering algorithm, with the Minimum Description Length measure. To significantly reduce the time required for the algorithm to find a good clustering result, a knowledge aware heuristic element is included in the GA process. The efficiency and effectiveness of the algorithm is verified with four case studies.

Analysis of Engineering Change Based on Product Development Network

2011 ◽  
Vol 314-316 ◽  
pp. 1607-1611
Author(s):  
Zhong Wei Gong ◽  
Hai Cheng Yang ◽  
Rong Mo ◽  
Tao Chen
Keyword(s):  
Product Development ◽  
Design Structure Matrix ◽  
Complex Activity ◽  
Engineering Change ◽  
Structure Matrix ◽  
Manufacturing Enterprises ◽  
Design Structure ◽  
Development Network ◽  
Motorcycle Engine

Engineering change is an important and complex activity for manufacturing enterprises. In order to improve the efficiency of engineering change, designers should pay different attentions to different nodes of product development network. In that case, a method of classifying the nodes was proposed. First, we proposed a method to cluster the nodes based on design structure matrix; then, we analyzed the indexes for evaluating the importance of nodes and studied the method of classifying the nodes of product development network; finally, the experiment of managing a type of motorcycle engine was employed to validate our method and it showed the correctness of the proposed method.

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