The effectiveness of IF-MADM (intuitionistic-fuzzy multi-attribute decision-making) for group decisions: methods and an empirical assessment for the selection of a senior centre

    Zheng-Yun Zhuang   Affiliation
    ; Chia-Rong Su Affiliation
    ; Shu-Chin Chang Affiliation


This study determines the effectiveness of intuitionistic-fuzzy multi-attribute decision-making (IF-MADM) for making group decisions in practice. The effectiveness of the method is measured in terms of four dimensions: applicability, efficacy, efficiency and informativeness. To measure the efficacy, an IF-MADM model that has been recently proposed, AHP and the TOPSIS approach, which are compensatory models for group MADM, are used to model and solve the same collective decision. Using non-parametric statistical tests for data analytics, a ‘similarity confirmation method’ is proposed for a pair-wise test. This is to determine whether the score vectors are similar. Score vectors are used to determine the final ordinal ranks and whose scales differ greatly for different MADM methods. Since the latter two MADM models are both trustworthy with a known range of applications, any similarity in the results verifies the efficacy of IF-MADM. Using this process, the applicability of IF-MADM modelling is demonstrated. The efficiency and informativeness are also benchmarked and justified in terms of the model’s ability to produce a more informed decision. These results are of interest to practitioners for the selection and application of MADM models. Finally, the selection of a senior centre, which is a real group decision problem, is used to illustrate these. This extends the empirical application of IF-MADM, as relatively few studies practically compare issues for IF-MADM with those for other MADM models. The study also supports a rarely studied non-clinical healthcare decision that is relevant because there are many aging societies.

Keyword : group decision, multi-attribute decision-making, intuitionistic fuzzy number, multiple criteria analysis, operational research in health services, data-driven decision making

How to Cite
Zhuang, Z.-Y., Su, C.-R., & Chang, S.-C. (2019). The effectiveness of IF-MADM (intuitionistic-fuzzy multi-attribute decision-making) for group decisions: methods and an empirical assessment for the selection of a senior centre. Technological and Economic Development of Economy, 25(2), 322-364.
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Feb 27, 2019
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Akaa, O. U., Abu, A., Spearpoint, M., & Giovinazzi, S. (2016). A group-AHP decision analysis for the selection of applied fire protection to steel structures. Fire Safety Journal, 86, 95-105.

Atanassov, K. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87-96.

Atanassov, K., & Gargov, G. (1989). Interval valued intuitionistic fuzzy sets. Fuzzy sets and systems, 31(3), 343-349.

Atanassov, K., Pasi, G., & Yager, R. (2005). Intuitionistic fuzzy interpretations of multi-criteria multi-person and multi-measurement tool decision making. International Journal of Systems Science, 36(14), 859-868.

Azaiez, M. N., & Al Sharif, S. S. (2005). A 0-1 goal programming model for nurse scheduling. Computers & Operations Research, 32(3), 491-507.

Bali, O., Dagdeviren, M., & Gumus, S. (2015). An integrated dynamic intuitionistic fuzzy MADM approach for personnel promotion problem. Kybernetes, 44(10), 1422-1436.

Bao, Q., Ruan, D., Shen, Y., Hermans, E., & Janssens, D. (2012). Improved hierarchical fuzzy TOPSIS for road safety performance evaluation. Knowledge-Based Systems, 32, 84-90.

Bayrak, M. Y., Çelebi, N., & Taşkin, H. (2007). A fuzzy approach method for supplier selection. Production Planning and Control: the Management of Operations, 18(1), 54-63.

Beisgen, B., & Kraitchman, M. (2003). Senior centers: opportunities for successful aging. New York: Springer Publishing Company.

Bhattacherjee, A. (2012). Social science research: principles, methods, and practices. University of South Florida Scholar Commons, USA.

Bian, T., Hu, J., & Deng, Y. (2017). Identifying influential nodes in complex networks based on AHP. Physica A: Statistical Mechanics and its Applications, 479, 422-436.

Boran, F. E., Boran, K., & Menlik, T. (2012). The evaluation of renewable energy technologies for electricity generation in Turkey using intuitionistic fuzzy TOPSIS. Energy Sources, Part B: Economics, Planning, and Policy, 7, 81-90.

Boran, F. E., Genç, S., & Akay, D. (2011). Personnel selection based on intuitionistic fuzzy sets. Human Factors and Ergonomics in Manufacturing & Service Industries, 21(5), 493-503.

Boran, F. E., Genç, S., Kurt, M., & Akay, D. (2009). A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems with Applications, 36(8), 11363-11368.

Borji, H. S. (2016). 4 global economic issues of an aging population. In Investopedia. Retrieved from

Bryman, A. (2015). Social research methods. USA: Oxford University Press.

Bryman, A., & Bell, E. (2015). Business research methods. USA: Oxford University Press.

Chang, C.-T., Chen, H.-M., & Zhuang, Z.-Y. (2012) Revised multi-segment goal programming: percentage goal programming. Computers and Industrial Engineering, 63(4), 1235-1242.

Charnes, A., Cooper, W. W., & Ferguson, R. O. (1955). Optimal estimation of executive compensation by linear programming. Management Science, 1(2), 138-151.

Chen, S.-J., Hwang, C.-L., & Hwang F.-P. (1992) Fuzzy multiple attribute decision making: Methods and applications. Berlin Heidelberg: Springer-Verlag.

Chen, T.-Y. (2015). The inclusion-based TOPSIS method with interval-valued intuitionistic fuzzy sets for multiple criteria group decision making. Applied Soft Computing, 26, 57-73.

Chen, T.-Y., Li, Y.-J., & Wang, H.-P. (2011). A dissonance reduction method for intuitionistic fuzzy multi-criteria decision-making problems. Pan-Pacific Management Review, 14(1), 1-27.

Chen, S. M., & Tan, J. M. (1994). Handling multicriteria fuzzy decision-making problems based on vague set theory. Fuzzy Sets and Systems, 67(2), 163-172.

Chi, L.-P., Zhuang, Z.-Y., Fu, C.-H., & Huang J.-H. (2018). A Knowledge Discovery Education Framework Targeting the Effective Budget Use and Opinion Explorations in Designing Specific High Cost Product. Sustainability, 10(8), 2742.

Das, S., Kar, M. B., & Kar, S. (2013). Group multi-criteria decision making using intuitionistic multifuzzy sets. Journal of Uncertainty Analysis and Applications, 1(10), 1-16.

Dong, Q., & Cooper, O. An orders-of-magnitude AHP supply chain risk assessment framework. International Journal of Production Economics, 182, 144-156.

Dönmez, D. (2015). Social science methods for empirical data collection and analysis. Retrieved from

Dweiri, F., Kumar, S., Khan, S. A., & Jain, V. (2016). Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Systems with Applications, 62, 273-283.

Erdogan, S. A., Šaparauskas, J., & Turskis, Z. (2017). Decision making in construction management: AHP and expert choice approach. Procedia Engineering, 172, 270-276.

Fernández, J. F. G., & Márquez, A. C. (2012). Managing maintenance strategy. In Maintenance management in network utilities: framework and practical implementation. Springer Science & Business Media.

Frege, C. M. (2005). Varieties of industrial relations research: take‐over, convergence or divergence? British Journal of Industrial Relations, 43(2), 179-207.

Gong, Z., Xu, X., Yang, Y., Zhou, Y., & Zhang, H. (2016). The spherical distance for intuitionistic fuzzy sets and its application in decision analysis. Technological and Economic Development of Economy, 22(3), 393-415.

Govindan, K., & Jepsen, M. B. (2016). ELECTRE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research, 250(1), 1-29.

Govindan, K., Kaliyan, M., Kannan D., & Haq A. N. (2014). Barriers analysis for green supply chain management implementation in Indian industries using analytic hierarchy process. International Journal of Production Economics, 147, 555-568.

Gruber, J., & Wise, D. (2001). An international perspective on policies for an aging society (Working Paper No. 8103). National Bureau of Economic Research, US.

Gupta, H., & Barua, M. K. (2017). Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS. Journal of Cleaner Production, 152, 242-258.

Hanne, T. (2013). Meta decision problems in multiple criteria decision making. In T. Gal, T. Stewart, & T. Hanne (Eds.), Multicriteria Decision Making: advances in MCDM models, algorithms, theory, and applications (Vol. 21). Springer Science & Business Media.

He, Y. H., Wang, L. B., He, Z. Z., & Xie, M. (2016). A fuzzy TOPSIS and rough set based approach for mechanism analysis of product infant failure. Engineering Applications of Artificial Intelligence, 47, 25-37.

Hillerman, T., Souza, J. C. F., Reis, A. C. B., & Carvalho, R. N. (2017). Applying clustering and AHP methods for evaluating suspect healthcare claims. Journal of Computational Science, 19, 97-111.

Ho, H.-P., Chang, C.-T., & Ku, C.-Y. (2013). On the location selection problem using analytic hierarchy process and multi-choice goal programming. International Journal of Systems Science, 44(1), 94-108.

Hossain, M. F., Adnan, Z. H., & Hasin, M. (2014). Improvement in weighting assignment process in Analytic Hierarchy Process by introducing suggestion matrix and Likert scale. International Journal of Supply Chain Management, 3(4), 91-95.

Huang, J., Zhao, Y., & Li, B. (2012). The application of intuitionistic fuzzy MADM based on projection model in thread threat assessment. In Lei et al. (Eds.), International Conference on Artificial Intelligence and Computational Intelligence 2012 (pp. 500-505). Springer-Verlag Berlin Heidelberg.

Hwang, C.-L., & Yoon, K. (1981). Multiple attribute decision making: methods and applications. New York: Springer-Verlag.

Hwang, C.-L., Lai, Y.-J., & Liu, T.-Y. (1993) A new approach for multiple objective decision making. Computers and Operational Research, 20, 889-899.

IBM. (2018). IBM Watson Health: Empowering Heroes, Transforming Health. Retrieved February, 2018, from URL:

Janic, M., & Reggiani, A. (2002). An application of the multiple criteria decision making (MCDM) analysis to the selection of a new hub airport. European Journal of Transport and Infrastructure Research, 2(2), 113-142.

Kahraman, C. (Ed.). (2008). Fuzzy multi-criteria decision making: theory and applications with recent developments (Vol. 16). Springer Science & Business Media.

Kahraman, C., Onar, S. C., & Oztaysi, B. (2015). Fuzzy multicriteria decision-making: a literature review. International Journal of Computational Intelligence Systems, 8(4), 637-666.

Kang, D., Jang, W., & Park, Y. (2016). Evaluation of e-commerce websites using fuzzy hierarchical TOPSIS based on ES-QUAL. Applied Soft Computing, 42, 53-65.

Kannan, D., de Sousa Jabbour, A. B. L., & Jabbour, C. J. C. (2014). Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company. European Journal of Operational Research, 233(2), 432-447.

Kokangül, A., Polat, U., & Dağsuyu, C. (2017). A new approximation for risk assessment using the AHP and Fine Kinney methodologies. Safety Science, 91, 24-32.

Kuo, R. J., Wu, Y. H., & Hsu, T. S. (2012). Integration of fuzzy set theory and TOPSIS into HFMEA to improve outpatient service for elderly patients in Taiwan. Journal of the Chinese Medical Association, 75(7), 341-348.

Li, D. F. (2005). Multi-attribute decision making models and methods using intuitionistic fuzzy sets. Journal of Computer and System Sciences, 70, 73-85.

Li, D. F. (2010). TOPSIS-based nonlinear-programming methodology for multiattribute decision making with interval-valued intuitionistic fuzzy sets. IEEE Transactions on Fuzzy Systems, 18(2), 299-311.

Li, D. F., Chen, G. H., & Huang, Z. G. (2010). Linear programming method for multiattribute group decision making using IF sets. Information Sciences, 180, 1591-1609.

Li, D. F., & Nan, J. X. (2011). Extension of the TOPSIS for multi-attribute group decision making under Atanassov IFS environments. International Journal of Fuzzy System Applications, 1(4), 47-61.

Li, G., Kou, G., Lin, C., Xu, L., & Liao, Y. (2015). Multi-attribute decision making with generalized fuzzy numbers. Journal of the Operational Research Society, 66(11), 1793-1803.

Li, W., Yu, S., Pei, H., Zhao, C., & Tian, B. (2017). A hybrid approach based on fuzzy AHP and 2-tuple fuzzy linguistic method for evaluation in-flight service quality. Journal of Air Transport Management, 60, 49-64.

Lin, L., Yuan, X. H., & Xia, Z. Q. (2007). Multicriteria fuzzy decision-making methods based on intuitionistic fuzzy sets. Journal of Computer and System Sciences, 73, 84-88.

Liu, S., Chan, F. T., & Ran, W. (2013). Multi-attribute group decision-making with multi-granularity linguistic assessment information: An improved approach based on deviation and TOPSIS. Applied Mathematical Modelling, 37(24), 10129-10140.

Liu, C. H., Tzeng, G. H., & Lee, M. H. (2012). Improving tourism policy implementation – the use of hybrid MCDM models. Tourism Management, 33(2), 413-426.

Ma, L.-C. (2010). Visualizing preferences on spheres for group decisions based on multiplicative preference relations. European Journal of Operational Research, 203(1), 176-184.

Mahdevari, S., Shahriar, K., & Esfahanipour, A. (2014). Human health and safety risks management in underground coal mines using fuzzy TOPSIS. Science of the Total Environment, 488, 85-99.

Majumder, M. (2015). Multi criteria decision making. In: Impact of urbanization on water shortage in face of climatic aberrations (pp. 35-47). Singapore: Springer.

Mardani, A., Jusoh, A., MD Nor, K., Khalifah, Z., Zakwan, N., & Valipour, A. (2015). Multiple criteria decision-making techniques and their applications–a review of the literature from 2000 to 2014. Economic Research-Ekonomska Istraživanja, 28(1), 516-571.

Márquez, A. C. (2007). Criticality analysis for asset priority setting. In The maintenance management framework: models and methods for complex systems maintenance. Springer Science & Business Media.

Marr, B. (2016). Big data in practice. US: Wiley.

McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: the management revolution. Harvard Business Review, 90(10), 61-67.

Mir, M. A., Ghazvinei, P. T., Sulaiman, N. M. N., Basri, N. E. A., Saheri, S., Mahmood, N. Z., Jahan, A., Begum, R. A., & Aghamohammadi, N. (2016). Application of TOPSIS and VIKOR improved versions in a multi criteria decision analysis to develop an optimized municipal solid waste management model. Journal of Environmental Management, 166, 109-115.

Nikou, S., & Mezei, J. (2013) Evaluation of mobile services and substantial adoption factors with analytic hierarchy process (AHP). Telecommunications Policy, 37(10), 915-929.

Oh, S. O., & Park, J. W. (2014). A study on relative importance and priority regarding airport selection attributes utilizing AHP. International Journal of Business and Social Research, 4(10), 43-53.

Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445-455.

Opricovic, S., & Tzeng, G. H. (2007). Extended VIKOR method in comparison with outranking methods. European Journal of Operational Research, 178(2), 514-529.

Ouyang, Y., & Pedrycz, W. (2016). A new model for intuitionistic fuzzy multi-attributes decision making. European Journal of Operational Research, 249, 677-682.

Oztaysi, B., Onar, S. C., Kahraman, C., & Yavuz, M. (2017). Multi-criteria alternative-fuel technology selection using interval-valued intuitionistic fuzzy sets. Transportation Research Part D, 53, 128-148.

Pankowska, A., & Wygralak, M. (2006) General IF-sets with triangular norms and their applications to group decision-making. Information Sciences, 176(18), 2713-2754.

Park, J. H., Park, I. Y., Kwun, Y. C., & Tan, X. (2011) Extension of the TOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environment. Applied Mathematical Modelling, 35(5), 2544-2556.

PennState University Libraries. (2017). Empirical research in education and the behavioral/social sciences. Retrieved February 2018, from URL:

Ramanathan, R., Mathirajan, M., & Ravindran, A. R. (Eds.). (2017). Big data analytics using multiple criteria decision-making models. Boca Raton: CRC Press.

Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26(4), 332-344.

Ren, H., & Wang, G. (2015). An interval-valued intuitionistic fuzzy MADM method based on a new similarity measure. Information, 6(4), 880-894.

Research Center of Industry Innovation for the Senior Citizens. (2012). CGURP-UARPD-3A0101: Final Term Report. Chang Gung University. Retrieved from

Roy, B. (1991). The outranking approach and the foundations of ELECTRE methods. Theory and Decision, 31(1), 49-73.

Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15, 59-62.

Sabaei, D., Erkoyuncu, J., & Roy, R. (2015). A review of multi-criteria decision making methods for enhanced maintenance delivery. Procedia CIRP, 37, 30-35.

Sadiq, R., & Tesfamariam, S. (2009). Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process (IF-AHP). Stochastic Environmental Research and Risk Assessment, 23(1), 75-91.

Samuel, O. W., Asogbon, G. M., Sangaiah, A. K., Fang, P., & Li, G. (2017). An integrated decision support system based on ANN and Fuzzy AHP for heart failure risk prediction. Expert Systems with Applications, 68, 163-172.

Sasanka, C. T., & Ravindra, K. (2015). Implementation of VIKOR method for selection of magnesium alloy to suit automotive applications. International Journal of Advanced Science and Technology, 83, 49-58.

Sayadi, M. K., Heydari, M., & Shahanaghi, K. (2009). Extension of VIKOR method for decision making problem with interval numbers. Applied Mathematical Modelling, 33(5), 2257-2262.

Schrage, L. (2002). LINGO Release 8.0 Users Manual. Chicago: LINDO System, Inc.

Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379-423.

Singh, D., & Rao, R. (2011). A hybrid multiple attribute decision making method for solving problems of industrial environment. International Journal of Industrial Engineering Computations, 2(3), 631-644.

Simon, H. A. (1976). Administrative behavior: a study of decision-making processes in administrative organization. New York: Free Press.

Szmidt, E., & Kacprzyk, J. (1996). Intuitionistic fuzzy sets in group decision making. Notes on IFS, 2(1), 15-32.

Szulecka, J., & Zalazar, E. M. (2017). Forest plantations in Paraguay: Historical developments and a critical diagnosis in a SWOT-AHP framework. Land Use Policy, 60, 384-394.

Triantaphyllou, E. (2000). Chapter 2: Multi-criteria decision making methods. In Multi-criteria decision making methods: A comparative study (pp. 5-21). US: Springer.

Tzeng, G.-H., & Huang, J.-J. (2011). Multiple attribute decision making: methods and applications. CRC Press, Taylor & Francis Group.

Velasquez, M., & Hester, P. T. (2013). An analysis of multi-criteria decision making methods. International Journal of Operations Research, 10(2), 56-66.

Vlachos, I. K., & Sergiadis, G. D. (2007). Intuitionistic fuzzy information - Applications to pattern recognition. Pattern Recognition Letters, 28, 197-206.

Walczak, D., & Rutkowska, A. (2017). Project rankings for participatory budget based on the fuzzy TOPSIS method. European Journal of Operational Research, 260(2), 706-714.

Wang, C.-Y., & Chen, S.-M. (2017). Multiple attribute decision making based on interval-valued intuitionistic fuzzy sets, linear programming methodology, and the extended TOPSIS method. Information Sciences, 397, 155-167.

Wang, S.-P., Hsieh, Y.-K., Zhuang, Z.-Y., & Ou, N.-C. (2014). Solving an outpatient nurse scheduling problem by binary goal programming. Journal of Industrial and Production Engineering, 31(1), 41-50.

Wang, X., & Peng, B. (2015). Determining the value of the port transport waters: Based on improved TOPSIS model by multiple regression weighting. Ocean & Coastal Management, 107, 37-45.

Wei, W., Liang, J., Wang, J., & Qian, Y. (2013). Decision-relative discernibility matrices in the sense of entropies. International Journal of General Systems, 42(7), 721-738.

Xu, Z. (2007). Intuitionistic preference relations and their applications in group decision making. Information Sciences, 177(1), 2363-2379.

Xu, Z. (2011). Approaches to multiple attribute group decision making based on intuitionistic fuzzy power aggregation operators. Knowledge-Based Systems, 24, 749-760.

Xu, Z., & Liao, H. (2014). Intuitionistic fuzzy analytic hierarchy process. IEEE Transactions on Fuzzy Systems, 22(4), 749-761.

Xu, Z., & Yager, R. R. (2008). Dynamic intuitionistic fuzzy multi-attribute decision making. International Journal of Approximate Reasoning, 48(1), 246-262.

Xu, Y., Wang, Y., & Miu, X. (2012). Multi-attribute decision making method for air target threat evaluation based on intuitionistic fuzzy sets. Journal of Systems Engineering and Electronics, 23(6), 891-897.

Yang, Y., & Chiclana, F. (2009). Intuitionistic fuzzy sets: spherical representation and distances. International Journal of Intelligent Systems, 24, 399-420.

Yang, J.-B., & Madan, G. S. (1994). An evidential reasoning approach for multiple-attribute decision making with uncertainty. IEEE Transactions on Systems, Man and Cybernetics, 24(1), 1-18.

Yazdani, M., & Payam, A. F. (2015). A comparative study on material selection of microelectromechanical systems electrostatic actuators using Ashby, VIKOR and TOPSIS. Materials & Design (1980-2015), 65, 328-334.

Ye, F. (2010). An extended TOPSIS method with interval-valued intuitionistic fuzzy numbers for virtual enterprise partner selection. Expert Systems with Applications, 37(10), 7050-7055.

Ye, J. (2013). Multiple attribute group decision-making methods with unknown weights in intuitionistic fuzzy setting and interval valued intuitionistic fuzzy setting. International Journal of General Systems, 42(5), 489-502.

Yu, P. L. (1973). A class of solutions for group decision problems. Management Science, 19(8), 936-946.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.

Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and Economic Development of Economy, 20(1), 165-179.

Zeleny, M. (2011). Multiple criteria decision making (MCDM): From paradigm lost to paradigm regained? Journal of Multi-criteria Decision Analysis, 18(1-2), 77-89.

Zhang, N., & Wei, G. (2013). Extension of VIKOR method for decision making problem based on hesitant fuzzy set. Applied Mathematical Modelling, 37(7), 4938-4947.

Zhao, X. (2014). TOPSIS method for interval-valued intuitionistic fuzzy multiple attribute decision making and its application to teaching quality evaluation. Journal of Intelligent & Fuzzy Systems, 26(6), 3049-3055.

Zhou, S., Liu, W., & Chang, W. (2016). An improved TOPSIS with weighted hesitant vague information. Chaos, Solitons & Fractals, 89, 47-53.

Zhuang, Z. Y., Chiang, I. J., Su, C. R., & Chen, C. Y. (2017) Modelling the decision of paper shredder selection using analytic hierarchy process and graph theory and matrix approach. Advances in Mechanical Engineering, 9(12), 1-11.

Zhuang, Z. Y., & Hocine, A. (2018). Meta goal programing approach for solving multi-criteria de Novo programing problem. European Journal of Operational Research, 265(1), 228-238.

Zhuang, Z.-Y., Yang, L.-W., Lee, M.-H., & Wang, C.-Y. (2018). ‘MEAN+R’: implementing a web-based, multi-participant decision support system using the prevalent MEAN architecture with R based on a revised intuitionistic-fuzzy multiple attribute decision-making model. Microsystem Technologies, 24(10), 4291-4309.