Group decision making with a fuzzy linguistic majority
Fuzzy Sets and Systems
Group decision making and consensus under fuzzy preferences and fuzzy majority
Fuzzy Sets and Systems - Special issue dedicated to Professor Claude Ponsard
Fuzzy Sets and Systems
A sequential selection process in group decision making with a linguistic assessment approach
Information Sciences—Intelligent Systems: An International Journal
Ranking and defuzzification methods based on area compensation
Fuzzy Sets and Systems
Applicability of the fuzzy operators in the design of fuzzy logic controllers
Fuzzy Sets and Systems
On a canonical representation of fuzzy numbers
Fuzzy Sets and Systems
Combining numerical and linguistic information in group decision making
Information Sciences: an International Journal
Computers and Operations Research
Reasonable properties for the ordering of fuzzy quantities (I)
Fuzzy Sets and Systems
A new approach based on soft computing to accelerate the selection of new product ideas
Computers in Industry
Information Sciences—Informatics and Computer Science: An International Journal
A structural component-based approach for designing product family
Computers in Industry
International Journal of Intelligent Systems
A minimax disparity approach for obtaining OWA operator weights
Information Sciences: an International Journal
A hybrid approach to concept selection through fuzzy analytic network process
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
International Journal of Computer Integrated Manufacturing
The extension of fuzzy QFD: From product planning to part deployment
Expert Systems with Applications: An International Journal
Application of quality function deployment in the semiconductor industry: A case study
Computers and Industrial Engineering
A mobile decision support system for dynamic group decision-making problems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Modelling group decision making problems in changeable conditions
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
Engineering Applications of Artificial Intelligence
Review: Industrial applications of type-2 fuzzy sets and systems: A concise review
Computers in Industry
Estimating the quality of process yield by fuzzy sets and systems
Expert Systems with Applications: An International Journal
Customer-driven product design and evaluation method for collaborative design environments
Journal of Intelligent Manufacturing
Information Sciences: an International Journal
Green supply implementation based on fuzzy QFD: an application in GPLM system
WSEAS TRANSACTIONS on SYSTEMS
A new incomplete preference relations based approach to quality function deployment
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
Use of ANP weighted crisp and fuzzy QFD for product development
Expert Systems with Applications: An International Journal
An integrated fuzzy decision approach for product design and evaluation
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In a competitive and global business environment, it is certainly a distinct advantage to capture the genuine and major customer's requirements effectively. To take advantage of this, the unique way is to analyze customer's requirements systematically and to transform them into the appropriate product features properly. Quality function deployment (QFD) is a well-known planning methodology for translating customer needs (CNs) into relevant design requirements (DRs). The intent of applying QFD is to consolidate the customers' preferences to the various phases of the product development cycle for a new product, or a new version of an existing product. However, it is more difficult to assess the performance of this process with accurate quantitative evaluation due to its uncertain nature. Moreover, people tend to give information about their personal preferences in many different ways, numerically or linguistically, depending on their background and value systems. In this study, a new fuzzy group decision-making approach is presented to fuse multiple preference styles to respond CNs in product development with QFD in a better way. The approach is illustrated with a numerical example concerning the development of the hatch door of a car.