Incorporating Constraints in System Modularization by Interactive Clustering of Design Structure Matrices

2016 ◽  
Author(s):  
Roozbeh Sanaei ◽  
Kevin N. Otto ◽  
Katja Hölttä-Otto ◽  
Kristin L. Wood
Keyword(s):  
Complex Systems ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Design Structure Matrix ◽  
Complex Products ◽  
Pressure Fields ◽  
Design Structure ◽  
Computational Capability ◽  
New Type ◽  
Interactive Clustering

Modularity is an approach to manage the design of complex systems by partitioning and assigning elements of a concept to simpler subsystems according to a planned architecture. Functional-flow heuristics suggest possible modules that have been demonstrated in past products, but using them still leaves it to the designer to choose which heuristics make sense in a certain architecture. This constitutes an opportunity for a designer to take other constraints and objectives into account. With large complex systems, the number of alternative groupings of elements into modular chunks becomes exponentially large and some form of automation would be beneficial to accomplish this task. Clustering algorithms using the design structure matrix (DSM) representation search the space of alternative relative positioning of elements and present one ideal outcome ordering which “optimizes” a modularity metric. Beyond the problems of lack of interactive exploration around the optimized result, such approaches also partition the elements in an unconstrained manner. Yet, typical complex products are subject to constraints which invalidate the unconstrained optimization. Such architectural partitioning constraints include those associated with external force fields including electric, magnetic, or pressure fields that constrain some functions to perform or not perform in different regions of the field. There are also supplier constraints where some components cannot be easily provided with others. Overall, it is difficult to simply embed all objectives of modular thinking into one metric to optimize. We develop a new type of interactive clustering algorithm approach considering multiple objectives and partitioning constraints. Partitioning options are offered to a designer interactively as a sequence of clustering choices between elements in the architecture. A designer can incorporate constraints that determine the compatibility or incompatibility of elements by choosing among alternative groupings progressively. Our aim is to combine computational capability of clustering algorithms with the flexibility of manual approaches. Through applying these algorithms to a MRI machine injector, we demonstrate the benefits of interactive cooperation between a designer and modularity algorithms, where constraints can be naturally considered.

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.

Modularization Method for Adaptable Products

2008 ◽  
Author(s):  
Lieke Arts ◽  
Magdalena K. Chmarra ◽  
Tetsuo Tomiyama
Keyword(s):  
Complex Systems ◽  
Design Methodology ◽  
Large Scale ◽  
Action Plan ◽  
Design Structure Matrix ◽  
Modular Architecture ◽  
Complex Products ◽  
Design Structure ◽  
Standard Design ◽  
New States

Adaptable products are gaining interests. Those products are able to adapt themselves to new environments, new states or new user defined tasks. There is not yet a standard design methodology for designing those products. This paper focuses on making large complex products (e.g. printers) more adaptable. Large-scale complex systems need to have modular architecture to some extent in order for engineers to be able to clearly comprehend the product. Therefore, a method to cluster components of an adaptable system is developed based on Design Structure Matrix (DSM) which stores information about connections between components. For each scenario or action plan to perform adaptability, the importance of component interconnections is rated in a separate DSM structure. By combining the original DSM with the adaptability DSM the engineers can group components. Finally, an example of a coffee maker is illustrated.

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.

Towards a Decentralised Approach to Modelling Connectivity in Complex Products

2010 ◽  
Author(s):  
Owolabi Ariyo ◽  
Claudia M. Eckert ◽  
P. John Clarkson
Keyword(s):  
Model Building ◽  
Design Structure Matrix ◽  
Support Design ◽  
Structure Matrix ◽  
Complex Products ◽  
Distributed Approach ◽  
Design Structure ◽  
Connectivity Model ◽  
Vital Component ◽  
Effort Reduction

Connectivity models are useful aids to support design reviews but building the models is an extremely effort intensive process. Connectivity models help to minimise incidents of unexpected rework by drawing attention to vital component interfaces and dependencies. The uptake of connectivity models can be enhanced if effort burden associated model building is significantly reduced. This paper describes an investigation into the use of a distributed approach to building connectivity models as a means of effort reduction. The Design Structure Matrix is broken down into sub-sections which are L-shaped or cross-shaped. These sub-sections are referred to as modules of the connectivity model. The effort required for building a model is compartmentalised within the modules. In other words, the minimisation of model building effort is attained by distributing the modules such that each individual’s effort contribution is limited to within a single module.

Metrics for Assessing Coupling Density and Modularity in Complex Products and Systems

2007 ◽  
Author(s):  
Katja Ho¨ltta¨-Otto ◽  
Olivier de Weck
Keyword(s):  
Singular Value ◽  
Equal Size ◽  
Design Structure Matrix ◽  
Structure Matrix ◽  
Complex Products ◽  
Design Structure ◽  
Yield Values ◽  
High Degree ◽  
Modularity Index

The central role of modularity is becoming more and more apparent in design of complex products and systems. The question frequently arises how modularity can be measured. To better understand the degree of modularity, we developed two metrics based on a design structure matrix (DSM). The non-zero fraction (NZF) captures the coupling density of interconnections between components, while the singular value modularity index (SMI) measures the degree of modularity. Both metrics yield values between 0 and 1. These metrics are applied to 15 systems and products. We show that real products typically have NZF values between 0.05 and 0.4 and an SMI between 0.05 (very integral) and 0.95 (very modular). A randomly generated DSM population of equal size and density exhibits SMI values that are bounded in the range from 0.25 to 0.45. We conclude that neither a high degree of modularity nor strong integrality occurs accidentally; but are the result of deliberate design. In particular, we show a more integral design will emerge if a functionally-equivalent product is designed to be portable. The main advantage of SMI is that it enables analysis of the degree of modularity of any product or system independent of subjective module choices.

scholarly journals An Enhanced Collaborative Optimization Approach with Design Structure Matrix Algorithms to Group and Decouple Multidisciplines

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Xifan Yao ◽  
Jifeng Zhou ◽  
Yongxiang Li ◽  
Erhui Liu
Keyword(s):  
Multidisciplinary Design Optimization ◽  
Clustering Algorithms ◽  
Collaborative Optimization ◽  
Design Structure Matrix ◽  
Optimization Approach ◽  
Structure Matrix ◽  
Matrix Algorithms ◽  
Design Structure ◽  
Solution Efficiency ◽  
Efficiency And Reliability

To improve the solution efficiency and reliability of multidisciplinary design optimization (MDO), an enhanced MDO approach, called sequenced collaborative optimization (SCO), is proposed. The proposed approach introduces the design structure matrix (DSM) to describe the coupling effects among disciplines and aggregates those mutually coupling disciplines into the strong tie groups among similar ones and the weak tie among heterogeneous ones through clustering algorithms. Further, those in the same group are sequenced by the DSM division algorithm. Moreover, by adding constraints, the groups are made independent, resulting in a tree structure without loops, thus decoupling the original multidisciplinary problem into several independent collaborative optimization modules. In the end, an example is employed to verify the efficiency and reliability of the approach.

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