Application of fuzzy distributions on project management
Fuzzy Sets and Systems
A generalized fuzzy weighted least-squares regression
Fuzzy Sets and Systems
Fuzzy logic and optimization models for implementing QFD
Proceedings of the 23rd international conference on on Computers and industrial engineering
Evaluating weapon systems using ranking fuzzy numbers
Fuzzy Sets and Systems
Fuzzy Sets, Fuzzy Logic, Applications
Fuzzy Sets, Fuzzy Logic, Applications
Fuzzy Mathematical Models in Engineering and Management Science
Fuzzy Mathematical Models in Engineering and Management Science
Coplanarity analysis and validation of PBGA and T2-BGA packages
Finite Elements in Analysis and Design
Analysis of linear systems with fuzzy parametric uncertainty
Fuzzy Sets and Systems - Special issue: Interfaces between fuzzy set theory and interval analysis
Computers and Industrial Engineering
A fuzzy model for exploiting quality function deployment
Mathematical and Computer Modelling: An International Journal
Fuzzy approaches to quality function deployment for new product design
Fuzzy Sets and Systems
Integrating preference analysis and balanced scorecard to product planning house of quality
Computers and Industrial Engineering
Computers and Industrial Engineering
Computers & Mathematics with Applications
Managing logistics customer service under uncertainty: An integrative fuzzy Kano framework
Information Sciences: an International Journal
An integrated linguistic-based group decision-making approach for quality function deployment
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
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Quality function deployment (QFD) is a customer-driven approach for processing new product developments in order to maximize customer satisfaction. Each engineering design characteristic is maximized for product performance according to the level of customer satisfaction. To cope with the vague nature of product development processes, fuzzy approaches are used to represent the importance scores of customer requirements (CRs), the relationship between CRs and design requirements (DRs) and relationship between the DRs themselves. Considering Kano's category of design requirements, this paper extends Chen and Weng's model (2003), and presents a fuzzy nonlinear model to determine the performance level of each DR for maximizing customer satisfaction, under the same group of constraints as that in Chen and Weng's model. The results, from an illustrative example, indicate that the total degree of customer satisfaction achieved by the proposed nonlinear model is greater than that by the original fuzzy model.