隨著市場日趨競爭，如何於短時間內開發符合顧客需求的創新產品為一項關鍵性議題。品質機能展開(Quality Function Deployment, QFD)是企業常用以掌握顧客需求，並將其轉換為具體設計規格之方法。其中，於初始規劃階段決定設計需求重要性尤為關鍵，因其影響企業應如何分配開發資源並擬定後續階段的計畫。考量現今社會高度複雜性，將需求間相依性(Dependency)納入決定設計需求重要性有其必要性。

過往考量相依性的評估方法牽涉過多主觀評估抑或容易遇到評估瓶頸。有鑑於此，本研究將模糊認知圖(Fuzzy Cognitive Maps, FCMs)導入品質機能展開，作為簡易且能有效求取含相依性之設計需求重要性的工具，因其能夠以影響關係角度來直觀評估需求間複雜因果關係，並利用演算法之迭代計算來降低主觀評估影響，不僅提升評估效率，也能得出合理結果。此外，針對多數文獻訂定非顧客熟悉評估形式，本研究予以顧客選擇明確值、區間值、語意變數等熟悉方式進行評估，並為避免不同形式整合容易產生資訊流失問題，將所有評估資訊轉換為二元模糊語意形式再行整合。期望透過上述缺失的改善，建構兼具效率與效果之品質機能展開。

本研究主要為應用模糊認知圖於決定含相依性之設計需求重要性，並將決策模式分為兩大階段。第一階段為蒐集與整合顧客和專家各項評估資訊，並將不同形式資訊轉換至二元模糊語意後再行整合。第二階段為將第一階段所得到的評估值，將其投入模糊認知圖的非監督赫布學習演算法(Nonlinear Hebbian Learning Algorithms, NHL)進行迭代演算，得出含相依性之各項評估值，最後經由整合計算，得出含相依性之設計需求重要性。最後，本研究參考Efe(2019)所提出之範例進行演算分析與方法比較，以驗證模式之合理性和有效性。

Determining how to quickly develop innovative products in order to meet customer demand has become a critical issue in modern society. Quality Function Deployment (QFD) is one of common product development managerial methods by which to do this. The first phase of QFD is especially important because it influences enterprises in terms of allocating their developing resources. Considering the complexity of modern society, it’s necessary to take correlations among requirements into consideration.

In previous QFD studies, evaluation of correlations among requirements usually has involved excessive subjective judgement or has resulted in evaluative bottlenecks among experts. In order to simply and effectively determine the importance of design requirements, this research introduces Fuzzy Cognitive Maps (FCMs) into QFD. FCM can facilitate evaluation of correlations from the perspective of causality and exploit a mathematical algorithm to reduce subjective influences. Additionally, unlike many previous studies, this research allows customers to select familiar evaluation forms, including crisp values, interval values, and linguistic variables to better evaluate. To avoid information loss emerging from aggregating evaluations with different evaluation forms, all the evaluations are transformed into 2-tuple linguistic values. Through improvements in the problems mentioned above, we look forward to establishing an effective QFD model.

This study mainly applies FCM to determine the importance of design requirements (DRs) containing correlations among requirements. The proposed model consists of two phases: (1) We collect and transform customers’ and experts’ evaluations into 2-tuple linguistic values and then use an aggregation operator to obtain collective opinions. (2) We input values derived from the first phase into Nonlinear Hebbian Learning Algorithms of FCM (FCM-NHL) to calculate evaluations containing correlations among requirements. Then, we aggregate results calculated from FCM-NHL and obtain the final importance of DRs. Finally, we use a practical example from an existing study to demonstrate the rationality of the approach and analyze the superiority of the proposed model by comparing it with the method used in the study.

Akao, Y. (1990). Quality Function Deployment: Integrating Customer Requirements into product design: Productivity Press, New York.
Axelrod, R. M. (1976), Structure of Decision: The Cognitive Maps of Political Elites, Princeton University Press.
Baykasoğlu, A., & Gölcük, İ. (2015). Development of a novel multiple-attribute decision making model via fuzzy cognitive maps and hierarchical fuzzy TOPSIS. Information Sciences, 301, 75-98.
Bueno, S., & Salmeron, J. L. (2009). Benchmarking main activation functions in fuzzy cognitive maps. Expert Systems with Applications, 36(3), 5221-5229.
Büyüközkan, G., Ertay, T., Kahraman, C., & Ruan, D. (2004). Determining the importance weights for the design requirements in the house of quality using the fuzzy analytic network approach. International Journal of Intelligent Systems, 19(5), 443-461.
Büyüközkan, G., and Feyzioğlu, O. (2005). Group decision making to better respond customer needs in software development. Computers & Industrial Engineering, 48, 427-441.
Büyüközkan, G., Feyziog˘lu, O., and Ruan, D. (2007). Fuzzy group decision-making to multiple preference formats in quality function deployment. Computers in Industry, 58, 392–402.
Chan, L., Kao, H., and Wu, M. (1999). Rating the importance of customer needs in quality function deployment by fuzzy and entropy methods. International Journal of Production Research, 37 , 2499-2518.
Chan, L.-K., and Wu, M.-L. (2005). A systematic approach to quality function deployment with a full illustrative example. Omega, 33, 119-139.
Cohen, L. (1995). Quality Function Deployment: How to Make QFD Work for You: Addison-Wesley, Reading, MA.
Dat, L. Q., Phuong, T. T., Kao, H.-P., Chou, S.-Y., and Nghia, P. V. (2015). A new integrated fuzzy QFD approach for market segments evaluation and selection. Applied Mathematical Modelling, 39, 3653–3665
Efe, B. (2019). Fuzzy cognitive map based quality function deployment approach for
dishwasher machine selection. Applied Soft Computing, 83, 105660.
Feng, G., Lu, W., Pedrycz, W., Yang, J., & Liu, X. (2019). The learning of fuzzy cognitive maps with noisy data: A rapid and robust learning method with maximum entropy. IEEE Transactions on Cybernetics.
Franceschini, F., & Rossetto, S. (1998). Quality function deployment: How to improve its use. Total Quality Management, 9(6), 491-500.
Glykas, M. (Ed.). (2010). Fuzzy cognitive maps: Advances in theory, methodologies, tools and applications (Vol. 247). Springer Science & Business Media.
Glykas, M. (2013). Fuzzy cognitive strategic maps in business process performance measurement. Expert Systems with Applications, 40(1), 1-14.
Grant, D., & Osei-Bryson, K. M. (2005, January). Using fuzzy cognitive maps to assess MIS organizational change impact. In Proceedings of the 38th Annual Hawaii International Conference on System Sciences (pp. 263c-263c). IEEE.
Griffin, A., & Hauser, J.R. (1993). The voice of the customer. Marketing Science, 12, 1-27.
Groumpos, P. P., & Stylios, C. D. (2000). Modelling supervisory control systems using fuzzy cognitive maps. Chaos, Solitons & Fractals, 11(1-3), 329-336.
Hauser, J.R., & Clausing, D. (1988). The house of quality. Harvard Business Reviews 66, 63–73.
Herrera, F., Martínez, L. (2000a), “A 2-tuple fuzzy linguistic representation model for computing with words,” IEEE Transactions on Fuzzy Systems, 8 (6), 746-752.
Herrera, F., Martínez, L. (2000b), “An approach for combining linguistic and numerical information based on the 2-tuple fuzzy linguistic representation model in decision-making,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 8 (5), 539-562.
Herrera, F., Martinez, L. and Sanchez, P.J. (2005). Managing non-homogeneous information in group decision-making, Europe Journal Of Operation Research, 166, 115–132.
Ho, E.S.S.A., Lai, Y.J, and Chang, S.I., (1999). An integrated group decision-making approach to quality function deployment. IIE Transactions, 31, 553–567.
Karavas, C. S., Kyriakarakos, G., Arvanitis, K. G., & Papadakis, G. (2015). A multi-agent decentralized energy management system based on distributed intelligence for the design and control of autonomous polygeneration microgrids. Energy Conversion and Management, 103, 166-179.
Kosko, B. (1986). Fuzzy cognitive maps. International journal of man-machine studies, 24(1), 65-75.
Kosko, B. (1992). Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence (No. QA76. 76. E95 K86).
Ko, W. C. (2015). Construction of house of quality for new product planning: A 2-tuple fuzzy linguistic approach. Computers in Industry, 73, 117-127.
Liu, H. T., & Wang, C. H. (2010). An advanced quality function deployment model using fuzzy analytic network process. Applied Mathematical Modelling, 34(11), 3333-3351.
Irani, Z., Sharif, A., Kamal, M. M., & Love, P. E. (2014). Visualising a knowledge mapping of information systems investment evaluation. Expert Systems with Applications, 41(1), 105-125.
Lyman, D. (1990). Deployment normalization. Transactions from a second symposium on Quality Function Deployment, a conference co-sponsored by the Automotive Division of the American Society for Quality Control, the American Supplier Institute, Dearborn, MI, and GOALrQPC, Methuen, MA, 307–315.
Mpelogianni, V., & Groumpos, P. P. (2016, July). Towards a new approach of fuzzy cognitive maps. In 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-6). IEEE.
Natarajan, R., Subramanian, J., & Papageorgiou, E. I. (2016). Hybrid learning of fuzzy cognitive maps for sugarcane yield classification. Computers and Electronics in Agriculture, 127, 147-157.
Papageorgiou, E., Stylios, C., & Groumpos, P. (2003, December). Fuzzy cognitive map learning based on nonlinear Hebbian rule. In Australasian Joint Conference on Artificial Intelligence (pp. 256-268). Springer, Berlin, Heidelberg.
Papageorgiou, E., & Groumpos, P. (2004, June). A weight adaptation method for fuzzy cognitive maps to a process control problem. In International Conference on Computational Science (pp. 515-522). Springer, Berlin, Heidelberg.
Papageorgiou, E. I. (2011). A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques. Applied Soft Computing, 11(1), 500-513.
Papageorgiou, E. I. (2011). Learning algorithms for fuzzy cognitive maps—a review study. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(2), 150-163.
Papageorgiou, E. I., & Salmeron, J. L. (2012). A review of fuzzy cognitive maps research during the last decade. IEEE Transactions on Fuzzy Systems, 21(1), 66-79.
Salmeron, J. L. (2009). Augmented fuzzy cognitive maps for modelling LMS critical success factors. Knowledge-based systems, 22(4), 275-278.
Salmeron, J. L., Vidal, R., & Mena, A. (2012). Ranking fuzzy cognitive map based
scenarios with TOPSIS. Expert Systems with Applications, 39(3), 2443-2450.
Satty, T. L. (1996). Decision making with dependence and feedback: The analytic network process. RWS Publication.
Sivasamy, K., Arumugam, C., Devadasan, S. R., Murugesh, R., & Thilak, V. M. M. (2016). Advanced models of quality function deployment: a literature review. Quality & Quantity, 50(3), 1399-1414.
Stach, W., Kurgan, L., & Pedrycz, W. (2008, June). Data-driven nonlinear Hebbian learning method for fuzzy cognitive maps. In 2008 IEEE international conference on fuzzy systems (IEEE world congress on computational intelligence) (pp. 1975-1981). IEEE.
Stylios, C. D., & Groumpos, P. P. (2004). Modeling complex systems using fuzzy cognitive maps. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 34(1), 155-162.
Sullivan, L. P. (1986). Quality function deployment, Quality Progress, 39-50.
Tsadiras, A. K. (2008). Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps. Information Sciences, 178(20), 3880-3894.
van Vliet, M., Kok, K., & Veldkamp, T. (2010). Linking stakeholders and modellers in scenario studies: The use of Fuzzy Cognitive Maps as a communication and learning tool. Futures, 42(1), 1-14.
Vassiliki, M., & Groumpos, P. P. (2016, June). A revised approach in modeling fuzzy cognitive maps. In 2016 24th Mediterranean Conference on Control and Automation (MED) (pp. 350-354). IEEE.
Wang, Y. M., & Chin, K. S. (2011). A linear goal programming approach to determining the relative importance weights of customer requirements in quality function deployment. Information Sciences, 181(24), 5523-5533.
Yaman, D., & Polat, S. (2009). A fuzzy cognitive map approach for effect-based operations: An illustrative case. Information Sciences, 179(4), 382-403.
Yan, H.-B., and Ma, T. (2015). A group decision-making approach to uncertain quality function deployment based on fuzzy preference relation and fuzzy majority. European Journal of Operational Research, 241, 815-829
Yu, R., & Tzeng, G. H. (2006). A soft computing method for multi-criteria decision making with dependence and feedback. Applied mathematics and computation, 180(1), 63-75.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338-353.
Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information sciences, 8(3), 199-249.