Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75717
Title: Two uncertain chance-constrained programming models to setting target levels of design attributes in quality function deployment
Authors: Miao, YW
Liu, YY
Chen, YZ
Zhou, J
Ji, P 
Keywords: Quality function deployment
Design attribute
Uncertain variable
Uncertain chance-constrained programming
Issue Date: 2017
Publisher: Elsevier
Source: Information sciences, 2017, v. 415, p. 156-170 How to cite?
Journal: Information sciences 
Abstract: Quality function deployment (QFD), is widely acknowledged as a customer-oriented product design tool, which is generated by translating consumer demands into design attributes of a product. In order to depict the internal ambiguous factors in the development process more appropriately, uncertain variables with a specialized kind of regular uncertainty distributions based on uncertainty theory are applied. Subsequently, two, uncertain chance constrained programming (CCP) models used for formulating the QFD procedure are set forth, whose objectives are maximizing the consumer satisfaction and minimizing the design cost, respectively. To demonstrate the feasibility of the proposed modelling approach, an example of the motorcycle design problem is illustrated, in which the new target levels of design attributes are selected and analyzed according to the decision-makers' subjectivity and preference at different confidence levels. Additionally, a comparative study between the uncertain CCP approach and another uncertain expected value modelling approach is conducted. The results indicate that uncertain CCP models are more suitable for optimization in the QFD procedure.
URI: http://hdl.handle.net/10397/75717
ISSN: 0020-0255
EISSN: 1872-6291
DOI: 10.1016/j.ins.2017.06.025
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