Kansei engineering has been developed as an effective methodology to deal with customers’ feeling and demands and further translate them into the design elements of a product. It is very important to determine and substantiate the measure of Kansei preferences before its utilization and performance. Kansei evaluation plays a vital role in the implementation of Kansei engineering; however; it is difficult to quantitatively evaluate customers’ preferences on Kansei attributes of products as such preferences involve the human perceptual interpretation with certain subjectivity; uncertainty; and imprecision. This study presents a fuzzy linguistic preference approach for Kansei evaluation. The proposed approach is based on fuzzy linguistic variables associated with the fuzzy weighted average techniques for aggregating Kansei preference information. A case study was conducted to illustrate the implementation of the proposed approach.
Kansei Evaluation; Fuzzy Linguistic Variables; Kansei Preferences; Aggregation; Fuzzy Weighted Average
Bellman; R. E.; & Zadeh; L. A. (1970). Decision-making in a fuzzy environment. Management Science; 17; 141-164.
Chang; H. C.; Lai; H. H.; & Chang; Y. M. (2006). Expression models used by consumers in conveying desire for product form: a case study of a car. International Journal of Industrial Ergonomics; 36; 3-10.
Das; S. (2002). Quantifying fuzziness due to the scale of measurement in response systems. Fuzzy Sets and Systems; 132; 317-333.
Delgado; M.; Verdegay; J. L.; & Vila; M. A. (1992). Linguistic decision-making models. International Journal of Intelligent Systems; 7; 479-492.
Dong; W. M.; & Wong; F. S. (1987). Fuzzy weighted averages and implementation of the extension principle. Fuzzy Sets and Systems; 21; 183-199.
Fukushima; K.; Kawata; H.; Fujiwara; Y.; & Genno; H. (1995). Human sensory perception oriented image processing in a color copy system. International Journal of Industrial Ergonomics; 15; 63-74.
Herrera; F.; & Herrera-Viedma; E. (2000). Linguistic decision analysis: steps for solving decision problems under linguistic information. Fuzzy Sets and Systems; 115; 67-82.
Hsiao; S. W.; & Tsai; H. C. (2005). Applying and hybrid approach based on fuzzy neural network and genetic algorithm to product form design. International Journal of Industrial Ergonomics; 35; 411-428.
Hsiao; S. W.; Chiu; F. Y.; & Lu; S. H. (2010). Product-form design model based on genetic algorithms. International Journal of Industrial Ergonomics; 40; 237-246.
Hsu; S. H.; Chuang; M. C.; & Chang; C. C. (2000). A semantic differential study of designers’ and users’ product form perception. International Journal of Industrial Ergonomics; 25; 375-391.
Huang; M. S.; Tsai; H. C.; & Huang; T. H. (2011). Applying Kansei engineering to industrial machinery trade show booth design. International Journal of Industrial Ergonomics; 41; 72-78.
Ishihara; S.; Ishihara; K.; Nagamachi; M.; & Matsubara; Y. (1997). An analysis of Kansei structure on shoes using self-organizing neural networks. International Journal of Industrial Ergonomics; 19; 93-104.
Jindo; T.; & Hirasago; K. (1997). Application studies to car interior of Kansei engineering. International Journal of Industrial Ergonomics; 19; 105-114.
Kao; C.; & Liu; S. T. (2001). Fractional programming approach to fuzzy weighted average. Fuzzy Sets and Systems; 120; 435-444.
Klir; G. J.; & Folger; T. A. (1988). Fuzzy Sets; Uncertainty; and Information. New Jersey; Englewood Cliffs: Prentice-Hall International; Inc.
Klir; G. J.; & Yuan; B. (1995). Fuzzy Sets and Fuzzy logic: Theory and Applications. New Jersey; Englewood Cliffs: Prentice-Hall International; Inc.
Lin; Y. C.; Lai; H. H.; & Yeh; C. H. (2007). Consumer-oriented product form design based on fuzzy logic: a case study of mobile phones. International Journal of Industrial Ergonomics; 37; 531-543.
Lin; L.; Yang; M. Q.; Li; J.; & Wang; Y. (2012). A systematic approach for deducing multi-dimensional modeling features design rules based on user-oriented experiments. International Journal of Industrial Ergonomics; 42; 347-358.
Lai; H. H.; Lin; Y. C.; Yeh; C. H.; & Wei; C. H. (2006). User-oriented design for the optimal combination on product design. International Journal of Production Economics; 100; 253-267.
Lawry; J. (2001). A methodology for computing with words. International Journal of Approximate Reasoning; 28; 51-89.
Llinares; C.; & Page; A. (2007). Application of product differential semantics to quantify purchaser perceptions in housing assessment. Building and Environment; 42; 2488-2497.
Martínez; L. (2007). Sensory evaluation based on linguistic decision analysis. International Journal of Approximate Reasoning; 44; 148-164.
Mondragón; S.; Company; P.; & Vergara; M. (2005). Semantic differential applied to the evaluation of machine tool design. International Journal of Industrial Ergonomics; 35; 1021-1029.
Nagamachi; M. (2002). Kansei engineering as a powerful consumer-oriented technology for product development. Applied Ergonomics; 33; 289-294.
Nakada; K. (1997). Kansei engineering research on the design of construction machinery. International Journal of Industrial Ergonomics; 19; 129-146.
Park; J.; & Han; S. H. (2004). A fuzzy rule-based approach to modeling affective user satisfaction towards office chair design. International Journal of Industrial Ergonomics; 34; 31-47.
Ruan; D.; & Zeng; X. (2004). Intelligent sensory evaluations: Methodologies and applications. Berlin; Springer-Verlag.
Shimizu; Y.; & Jindo; T. (1995). A fuzzy logic analysis method for evaluation human sensitivities. International Journal of Industrial Ergonomics; 15; 39-47.
Smith; S.; & Fu; S. H. (2011). The relationships between automobile head-up display presentation images and drivers’ Kansei. Displays; 32; 58-68.
Tanoue; C.; Ishizaka; K.; & Nagamachi; M. (1997). Kansei engineering: a study on perception of vehicle interior image. International Journal of Industrial Ergonomics; 19; 115-128.
Tsai; H. C.; & Hsiao; S. W. (2004). Evaluation of alternatives for product customization using fuzzy logic. Information Sciences; 158; 233-262.
Tsuchiya; T.; Maeda; T.; Matsubara; Y.; & Nagamachi; M. (1996). A fuzzy rule induction method using genetic algorithm. International Journal of Industrial Ergonomics; 18; 135-145.
Vanegas; L. V.; & Labib; A. W. (2001). Application of new fuzzy-weighted average (NFWA) method to engineering design evaluation. International Journal of Production Research; 39; 1147-1162.
Yan; H. B.; Huynh; V. N.; Murai; T.; & Nakamori; Y. (2008). Kansei evaluation based on prioritized multi-attribute fuzzy target-oriented decision analysis. Information Sciences; 178; 4080-4093.
Zadeh; L. A. (1975). The concept of linguistic variable and its application to approximate reasoning; Parts 1–2. Information Sciences; 8; 199-249 & 301-357.
Zadeh; L. A. (1976). The concept of linguistic variable and its application to approximate reasoning; Parts 3. Information Sciences; 9; 43-80.
Zadeh; L. A. (1996). Fuzzy logic=computing with words. IEEE Trans; Fuzzy Systems; 4; 103-111.
Zeng; X.; Ruan; D.; & Koehl; L. (2008). Intelligent sensory evaluation: concepts; implementations; and applications. Mathematics and Computers in Simulation; 77; 443-452.
