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http://dx.doi.org/10.11627/jkise.2016.39.2.138

Using Fuzzy Numbers in Quality Function Deployment Optimization  

Yoo, Jaewook (Department of Business Administration, Dong-A University)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.39, no.2, 2016 , pp. 138-149 More about this Journal
Abstract
Quality function deployment (QFD) is a widely adopted customer-oriented product development methodology by translating customer requirements (CRs) into technical attributes (TAs), and subsequently into parts characteristics, process plans, and manufacturing operations. A main activity in QFD planning process is the determination of the target levels of TAs of a product so as to achieve a high level of customer satisfaction using the data or information included in the houses of quality (HoQ). Gathering the information or data for a HoQ may involve various inputs in the form of linguistic data which are inherently vague, or human perception, judgement and evaluation for the information and data. This research focuses on how to deal with this kind of impreciseness in QFD optimization. In this paper, it is assumed as more realistic situation that the values of TAs are taken as discrete, which means each TA has a few alternatives, as well as the customer satisfaction level acquired by each alternative of TAs and related cost are determined based on subjective or imprecise information and/or data. To handle these imprecise information and/or data, an approach using some basic definitions of fuzzy sets and the signed distance method for ranking fuzzy numbers is proposed. An example of a washing machine under two-segment market is provided for illustrating the proposed approach, and in this example, the difference between the optimal solution from the fuzzy model and that from the crisp model is compared as well as the advantage of using the fuzzy model is drawn.
Keywords
Quality Function Deployment; Impreciseness; Fuzzy Sets; Signed Distance Ranking;
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1 Chen, L.H. and Ko, W.C., Fuzzy linear programming models for NPD using a four-phase QFD activity process based on the means-end chain concept, European Journal of Operations Research, 2010, Vol. 201, No. 2, pp. 619-632.   DOI
2 Chen, L.H. and Weng, M.C., A fuzzy model for exploiting quality function deployment, Mathematical and Computer Modelling, 2003, Vol. 38, No. 5-6, pp. 559-570.   DOI
3 Chen, Y.J., Tang, J., and Fung, R.Y.K., Fuzzy regression- based mathematical programming model for quality function deployment, International Journal of Production Research, 2004, Vol. 42, pp. 1009-1027.   DOI
4 Chen, Y., Fung, R.Y.K., and Tang, J., Fuzzy expected value modelling approach for determining target value of engineering characteristics in QFD, Int. J. Prod. Res., 2005, Vol. 43, No. 17, pp. 3583-3604.   DOI
5 Delice, E.K. and Gungor, Z., Determining design requirements in QFD using fuzzy mixed-integer goal programming : application of a decision support system, International Journal of Production Research, 2013, Vol. 51, No. 21, pp. 6378-6396.   DOI
6 Erol, I. and Ferrel, W.G., A methodology for selection problems with multiple, conflicting objectives and both qualitative and quantitative criteria, International Journal of Production Economics, 2003, Vol. 86, No. 3, pp. 187-199.   DOI
7 Fung, R.Y.K., Tang, J., Tu, Y., and Wang, D., Product design resources optimization using a non-linear fuzzy quality function deployment model, Int. J. of Production Research, 2002, Vol. 40, No. 3, pp. 585-599.   DOI
8 Hauser, J.R. and Clausing, D., The house of quality, Harvard Business Review, 1988, Vol. 66, No. 3, pp. 63- 73.
9 Kahraman, C., Ertay, T., and Buyukozkan, G., A fuzzy optimization model for QFD planning process using analytic network approach, European Journal of Operations Research, 2006, Vol. 171, No. 2, pp. 390-411.   DOI
10 Karsak, E.E., Fuzzy multiple objective decision making approach to prioritize design requirements in quality function deployment, International Journal of Production Research, 2004a, Vol. 42, No. 18, pp. 3957-3974.   DOI
11 Lai, X., Xie, M., and Tan, K.C., Dynamic Programming for QFD Optimization, Quality and Reliability Engineering, 2005, Vol. 21, No. 8, pp. 769-780.   DOI
12 Karsak, E.E., Fuzzy multiple objective programming framework to prioritize design requirements in quality function deployment, Computers and Industrial Engineering, 2004b, Vol. 47, pp. 149-163.   DOI
13 Kaufmann, A. and Gupta, M.M., Introduction to fuzzy arithmetic theory and applications, van Nostrand Reinhold, New York, 1991.
14 Kim, K.J., Moskowitz, H., Dhingra, A., and Evans, G., Fuzzy multicriteria models for quality function deployment, Eur. J. Oper. Res., 2000, Vol. 121, No. 3, pp. 504- 518.   DOI
15 Lin, F.T. and Yao, J.S., Using fuzzy numbers in knapsack problems, European Journal of Operations Research, 2001, Vol. 135, pp. 158-176.   DOI
16 Liu, S.H., Rating Design Requirements in fuzzy quality function deployment via a mathematical programming approach, International Journal of Production Research, 2005, Vol. 43, No. 3, pp. 497-513.   DOI
17 Vanegas, L.V. and Labib, A.W., Fuzzy Quality function deployment Model for deriving optimum targets, International Journal of Production Research, 2001, Vol. 39, No. 2, pp. 99-120.   DOI
18 Sener, Z. and E. E. Karsak, A decision model for setting target levels in quality function deployment using nonlinear programming-based fuzzy regression and optimization, The International Journal of Advanced Manufacturing Technology, 2010, Vol. 48, pp. 1173-1184.   DOI
19 Sohn, S.Y. and Choi, I.S., Fuzzy QFD for supply chain management with reliability consideration, Reliability Engineering and System Safety, 2001, Vol. 72, No. 3, pp. 327-334.   DOI
20 Tang, J., Richard, Y.K., Baodong, X., and Wang, D., A new approach to quality function deployment planning with financial consideration, Computers and Operations Research, 2002, Vol. 29, pp. 1447-1463.   DOI
21 Zhou, M., Fuzzy logic and optimization models for implementing QFD, Computers and Industrial Engineering, 1998, Vol. 35, No. 1-2, pp. 237-240.   DOI
22 Yao, J.S. and Wu, K.M., Ranking fuzzy numbers based on decomposition principle and signed distance, Fuzzy Sets and Systems, 2000, Vol. 116, pp. 275-288.   DOI
23 Yoo, J., Dynamic programming approach for determining optimal levels of technical attributes in QFD under multi-segment market, Journal of Society of Korea Industrial and Systems Engineering, 2015, Vol. 38, No. 2, pp. 120-128.   DOI
24 Yu, E.J. and Kwak, C., Service Development using Fuzzy QFD in the banking industry, Journal of the Korean Society for Quality Management, 2015, Vol. 43, No. 1, pp. 103-124.   DOI
25 Chen, L.H. and Ko, W.C., Fuzzy linear programming models for new product design using QFD with FMEA, Applied Mathematical Modelling, 2009, Vol. 33, No. 2, pp.633-647.   DOI
26 Zimmermann, H.-J., Fuzzy Set Theory and its Application, Fourth Edition, Springer Science+Business Media, LLC, New York, 2001.
27 Akao, Y., Quality function deployment : integrating customer requirements into product design, Cambridge, MA, Productivity Press, 1990.
28 Buyukozkan, G., Feyzioglu, O., and Ruan, D., Fuzzy group decision-making to multiple preference formats in quality function deployment, Computers in Industry, 2007, Vol. 58, No. 5, pp. 392-402.   DOI