The architecture of a product is determined by both the elements that compose the product and the way in which they interact with each other. In this paper, we use the design structure matrix (DSM) as a tool to capture this architecture. Designing modular products can result in many benefits to both consumers and manufacturers. The development of modular products requires the identification of highly interactive groups of elements and arranging (i.e. clustering) them into modules. However, no rigorous DSM clustering technique can be found in product development literature. This paper presets a review of the basic DSM building blocks used in the identification of product modules. The DSM representation and building blocks are used to develop a new DSM clustering tool based on a genetic algorithm (GA) and the minimum description length (MDL) principle. The new tool is capable of partitioning the product architecture into an “optimal” set of modules or sub-systems. We demonstrate this new clustering method using an example of a complex product architecture for an industrial gas turbine.
In many different fields, researchers are often confronted by problems arising from complex systems. Simple heuristics or even enumeration works quite well on small and easy problems; however, to efficiently solve large and difficult problems, proper decomposition is the key. In this paper, investigating and analyzing interactions between components of complex systems shed some light on problem decomposition. By recognizing three bare-bones interactions—modularity, hierarchy, and overlap, facet-wise models are developed to dissect and inspect problem decomposition in the context of genetic algorithms. The proposed genetic algorithm design utilizes a matrix representation of an interaction graph to analyze and explicitly decompose the problem. The results from this paper should benefit research both technically and scientifically. Technically, this paper develops an automated dependency structure matrix clustering technique and utilizes it to design a model-building genetic algorithm that learns and delivers the problem structure. Scientifically, the explicit interaction model describes the problem structure very well and helps researchers gain important insights through the explicitness of the procedure.
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