Architectural Optimisation Using Real Options Theory and Dependency Structure Matrices

Sharman, David; Yassine, Ali A.; Carlile, Paul R.

doi:10.1115/detc2002/dac-34119

Cited by 9 publications

(8 citation statements)

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“…This is because it is easiest to inspect, and because the physical domain often maps closely to the task domain so arranging it in this way eases the process of comparison. This point is expanded upon by Sharman, Yassine, and Carlile [2002], regarding interdomain mapping. Third, many runs of the Thebeau clustering algorithms failed to reveal this clustering solution.…”

Section: Applying Dimensionality To the Gas Turbinementioning

confidence: 98%

Characterizing complex product architectures

Sharman

Yassine

2003

Systems Engineering

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Due to the large-scale nature of complex product architectures, it is necessary to develop some form of abstraction in order to be able to describe and grasp the structure of the product, facilitating product modularization. In this paper we develop three methods for describing product architectures: (a) the Dependency Structure Matrix (DSM), (b) Molecular Diagrams (MD), and (c) Visibility-Dependency (VD) signature diagrams. Each method has its own language (and abstraction), which can be used to qualitatively or quantitatively characterize any given architecture spanning the modular-integrated continuum. A consequence of abstraction is the loss of some detail. So, it is important to choose the correct method (and resolution) to characterize the architecture in order to retain the salient details. The proposed methods are suited for describing architectures of varying levels of complexity and detail. The three methods are demonstrated using a sequence of illustrative simple examples and a case-study analysis of a complex product architecture for an industrial gas turbine.

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Section: Applying Dimensionality To the Gas Turbinementioning

confidence: 98%

Characterizing complex product architectures

Sharman

Yassine

2003

Systems Engineering

Self Cite

194

118

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“…The objective function is therefore a trade-off between the cost of being inside a module and the overall system benefit. Sharman et al (2002) attempted using Fernandez's algorithm on an industrial gas turbine. However, they showed that this algorithm is incapable of predicting the formation of good clustering arrangements for complex product architectures due to the oversimplification of the objective function.…”

Section: Dependency Structure Matrix (Dsm) Methodsmentioning

confidence: 99%

Dependency Structure Matrix, Genetic Algorithms, and Effective Recombination

Goldberg

Sastry

et al. 2009

Evolutionary Computation

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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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“…Eppinger and Salminen [8] discuss the possible relationship between the product, tasks, and organizational domains in product development. Sharman et al [22] suggest that elements in one domain need to map to the same element in another domain in a one-to-one manner. They propose a hypothetical optimization of a multiple-domain PD project resulting in an optimal design structure matrix (DSM) showing the relational arrangement of elements within the domains.…”

Section: Literature Reviewmentioning

confidence: 99%

A Knowledge-Driven, Network-Based Computational Framework for Product Development Systems

Yassine

Bradley

2013

Journal of Computing and Information Science in Engineering

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Today's fast-paced product development (PD) environment brings many new challenges to the PD community. These challenges are mainly due to a drastic increase in the scale and complexity of engineered systems, which require the collaboration of functionally and geographically distributed resources within and outside a firm's boundary. To address these new challenges, this paper proposes a novel theoretical and computational framework for an enterprise-wide PD management system. The proposed framework considers an integrative view of the various dependencies that co-exist in three PD domains (i.e., people, products, and processes). Additionally, it provides a computational tool that links them together in a succinct and tractable way and provides an analysis method for assessing their influence on shaping the product development process. Using this framework, we suggest that the characteristics of how an organization acquire data, interpret information, and apply knowledge will impact the final architecture of a product. We demonstrate this framework by analyzing the development efforts for a software project called robocode.

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Architectural Optimisation Using Real Options Theory and Dependency Structure Matrices

Cited by 9 publications

References 0 publications

Characterizing complex product architectures

Characterizing complex product architectures

Dependency Structure Matrix, Genetic Algorithms, and Effective Recombination

A Knowledge-Driven, Network-Based Computational Framework for Product Development Systems

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