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Fuzzy multicriteria models for quality function deployment. (English) Zbl 0968.90045
Summary: An integrated formulation and solution approach to quality function deployment is presented. Various models are developed by defining the major model components (namely, system parameters, objectives, and constraints) in a crisp or fuzzy way using multiattribute value theory combined with fuzzy regression and fuzzy optimization theory. The proposed approach would allow a design team to reconcile tradeoffs among the various performance characteristics representing customer satisfaction as well as the inherent fuzziness in the system. In addition, the modeling approach presented makes it possible to assess separately the effects of possibility and flexibility inherent or permitted in the design process on the overall design. Knowledge of the impact of the possibility and flexibility on customer satisfaction can also serve as a guideline for acquiring additional information to reduce fuzziness in the system parameters as well as determine how much flexibility is warranted or possible to improve a design. The proposed modeling approach would be applicable to a wide spectrum of design problems where multiple design criteria and functional design relationships are interacting and/or conflicting in an uncertain, qualitative, and fuzzy way.

90B50Management decision making, including multiple objectives
03E72Fuzzy set theory
Full Text: DOI
[1] Armacost, R.; Componation, P.; Mullens, M.; Swart, W.: An AHP framework for prioritizing customer requirements in QFD: an industrialized housing application. IIE transactions 26, No. 4, 72-79 (1994)
[2] ASI, 1989--1996. Transactions from the Symposium on Quality Function Deployment, American Supplier Institute/GOAL/QPC
[3] Bardossy, A.: Note on fuzzy regression. Fuzzy sets and systems 37, 65-75 (1990)
[4] Bellman, R.; Zadeh, L.: Decision-making in a fuzzy environment. Management science 17, No. 4, B141-B164 (1970) · Zbl 0224.90032
[5] Bosserman, S., 1992. Quality function deployment: The competitive advantage, Private Trunked Systems Division, Motorola
[6] Bossert, J., 1991. Quality function deployment -- A practitioner’s approach, ASQC Quality Press, Milwaukee, WI
[7] Clausing, D., 1994. Total quality development, ASME Press, Dover, NH
[8] Dhingra, A.; Moskowitz, H.: Application of fuzzy theories to multiple objective decision making in system design. European journal of operational research 53, 348-361 (1991) · Zbl 0729.90593
[9] Dubois, D.; Prade, H.: Systems of linear fuzzy constraints. Fuzzy sets and systems 3, 37-48 (1980) · Zbl 0425.94029
[10] Eureka, W.E., 1987. Introduction to quality function deployment, Quality function deployment: A collection of presentations and QFD case studies, Section III, ASI Inc., Dearborn, MI
[11] Evans, G., Moskowitz, H., Dhingra, A., 1990. Multicriteria methodologies for quality function deployment, Presented at ORSA/TIMS Conference, Philadelphia
[12] Gharpuray, M.; Tanaka, H.; Fan, L.; Lai, F.: Fuzzy linear regression analysis of cellulose hydrolysis. Chemical engineering communications 41, 299-314 (1986)
[13] Griffin, A.: Evaluating qfd’s use in US firms as a process for developing products. Journal of product innovation management 9, 171-187 (1992)
[14] Griffin, A.; Hauser, J.: Voice of the customer. Marketing science 12, 1-27 (1993)
[15] Hauser, J. R.; Clausing, D.: The house of quality. Harvard business review 66, 63-73 (1988)
[16] Heshmaty, B.; Kandel, A.: Fuzzy linear regression and its applications to forecasting in uncertain environment. Fuzzy sets and systems 15, 159-191 (1985) · Zbl 0566.62099
[17] Keeney, R., Raiffa, H., 1976. Decisions with Multiple Objectives, Wiley, New York · Zbl 0488.90001
[18] Khoo, L.; Ho, N.: Framework of a fuzzy quality function deployment system. International journal of production research 34, No. 2, 299-311 (1996) · Zbl 0924.90083
[19] Kim, K.; Moskowitz, H.; Koksalan, M.: Fuzzy versus statistical regression. European journal of operational research 92, 417-434 (1996) · Zbl 0912.90064
[20] Lai, Y., Ho, E., Chang, S., 1998. Identifying customer preferences in quality function deployment using group decision making techniques, in: Usher, J., Roy, U., Parsaei, H. (Eds.), Integrated Product and Process Development: Methods, Tools, and Techniques, Wiley, New York, pp. 1--28
[21] Moskowitz, H., 1993. Fjorde motor company: Revitalizing product development, Krannert Graduate School of Management, Purdue University, West Lafayette, IN
[22] Moskowitz, H., 1998. Quality function deployment for manufacturing planning and strategy, Presented at INFORMS International Israel, Tel Aviv
[23] Moskowitz, H.; Kim, K.: QFD optimizer: A novice friendly quality function deployment decision support system for optimizing product designs. Computers and industrial engineering 32, 641-655 (1997)
[24] Moskowitz, H.; Kim, K.: On assessing the H value in fuzzy linear regression. Fuzzy sets and systems 58, 303-327 (1993) · Zbl 0791.62073
[25] Moskowitz, H.; Ward, J.: A three-phase approach to installing a continuous learning culture in manufacturing education and training. Production and operations management 7, 201-211 (1998)
[26] Shipley, T., 1992. Quality function deployment: Translating customer needs into product specifications, Working paper, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL
[27] Sullivan, L.P., 1986. Quality function deployment, Quality Progress, p. 39
[28] Tanaka, H.; Uejima, S.; Asai, K.: Linear regression analysis with fuzzy model. IEEE transactions on SMC 12, 903-907 (1982) · Zbl 0501.90060
[29] Tanaka, H.; Watada, J.: Possibilistic linear systems and their applications to the linear regression model. Fuzzy sets and systems 27, 275-289 (1988) · Zbl 0662.93066
[30] Thurston, D., Locascio, A., 1993. Multiattribute design optimization and concurrent engineering, in: Parsaei, H., Sullivan, W. (Eds.), Concurrent Engineering, Chapman, London
[31] Wasserman, G. S.: On how to prioritize design requirements during the QFD planning process. IIE transactions 25, No. 3, 59-65 (1993)
[32] Yager, R.: Multiple objective decision-making using fuzzy sets. International journal of man-machine studies 9, 375-382 (1977) · Zbl 0371.90005
[33] Zimmermann, H.: Description and optimization of fuzzy systems. International journal of general systems 2, No. 4, 209-215 (1976) · Zbl 0338.90055
[34] Zimmermann, H.: Fuzzy programming and linear programming with several objective functions. Fuzzy sets and systems 1, 45-55 (1978) · Zbl 0364.90065