TY - JOUR
T1 - Fuzzy set based multi-attribute conceptual design evaluation
AU - Verma, Dinesh
AU - Smith, Caroline
AU - Fabrycky, Wolter
PY - 1999
Y1 - 1999
N2 - Identification of a need or functional/performance deficiency initiates conceptual design. Methods such as Quality Function Deployment (QFD), Checklists and Taxonomies (CAT), and Input/Output Matrices (IOM) provide a framework for translating a need into specific qualitative and quantitative customer requirements. Design concepts and technical solutions are then generated to address these requirements. This paper represents an extension of ongoing research in the application of fuzzy set methods to evaluate design concepts. Since imprecision and vagueness characterize this nascent design phase, the QFD method and Pugh's concept selection process are modified and extended with concepts from fuzzy set theory [Verma, 1994; Verma and Fabrycky, 1995]. This fuzzy set based extension provides a rigorous mechanism for dealing with imprecise requirements and priorities, as well as the subjective correlation between customer and design requirements. Furthermore, the approach presented herein is discussed from the perspective of invoking Taguchi's loss function during design concept feasibility analysis within conceptual system design.
AB - Identification of a need or functional/performance deficiency initiates conceptual design. Methods such as Quality Function Deployment (QFD), Checklists and Taxonomies (CAT), and Input/Output Matrices (IOM) provide a framework for translating a need into specific qualitative and quantitative customer requirements. Design concepts and technical solutions are then generated to address these requirements. This paper represents an extension of ongoing research in the application of fuzzy set methods to evaluate design concepts. Since imprecision and vagueness characterize this nascent design phase, the QFD method and Pugh's concept selection process are modified and extended with concepts from fuzzy set theory [Verma, 1994; Verma and Fabrycky, 1995]. This fuzzy set based extension provides a rigorous mechanism for dealing with imprecise requirements and priorities, as well as the subjective correlation between customer and design requirements. Furthermore, the approach presented herein is discussed from the perspective of invoking Taguchi's loss function during design concept feasibility analysis within conceptual system design.
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U2 - 10.1002/(SICI)1520-6858(1999)2:4<187::AID-SYS1>3.0.CO;2-E
DO - 10.1002/(SICI)1520-6858(1999)2:4<187::AID-SYS1>3.0.CO;2-E
M3 - Article
AN - SCOPUS:0010685469
SN - 1098-1241
VL - 2
SP - 187
EP - 197
JO - Systems Engineering
JF - Systems Engineering
IS - 4
ER -