Normalization of attribute values with interval information in group decision-making setting: with an application to software quality evaluation

References

• Aghajani Bazzazi, A., Osanloo, M., & Karimi, B. (2011). Deriving preference order of open pit mines equipment through MADM methods: Application of modified VIKOR method. Expert Systems with Applications, 38(3), 2550–2556.
   Web of Science ®Google Scholar
• Azar, D., Harmanani, H., & Korkmaz, R. (2009). A hybrid heuristic approach to optimize rule-based software quality estimation models. Information and Software Technology, 51(9), 1365–1376.
   Web of Science ®Google Scholar
• Blagojevic, B., Srdjevic, B., Srdjevic, Z., & Zoranovic, T. (2016). Heuristic aggregation of individual judgments in AHP group decision making using simulated annealing algorithm. Information Sciences, 330, 260–273.
   Web of Science ®Google Scholar
• Çelen, A. (2014). Comparative analysis of normalization procedures in TOPSIS method: With an application to Turkish deposit banking market. Informatica, 25(2), 185–208.
   Web of Science ®Google Scholar
• Dehghan-Manshadi, B., Mahmudi, H., Abedian, A., & Mahmudi, R. (2007). A novel method for materials selection in mechanical design: Combination of non-linear normalization and a modified digital logic method. Materials & Design, 28(1), 8–15.
   Web of Science ®Google Scholar
• Gök, S. A., Palanc, O., & Olgun, M. (2014). Cooperative interval games: Mountain situations with interval data. Journal of Computational and Applied Mathematics, 259(Part B), 622–632.
   Google Scholar
• Gou, X., & Xu, Z. (2017). Exponential operations for intuitionistic fuzzy numbers and interval numbers in multi-attribute decision making. Fuzzy Optimization and Decision Making, 16(2), 183–204.
   Web of Science ®Google Scholar
• Hafezalkotob, A., & Hafezalkotob, A. (2015). Comprehensive MULTIMOORA method with target-based attributes and integrated significant coefficients for materials selection in biomedical applications. Materials & Design, 87, 949–959.
   Web of Science ®Google Scholar
• ISO/IEC 25001:2014. (2014). ISO/IEC 25001:2007, Software engineering – software product quality requirements and evaluation (SQuaRE) – Planning and management. ISO/IEC. Retrieved from http://www.iso.org/iso/home/store/catalogue_ics/catalogue_detail_ics.htm?csnumber=64787
   Google Scholar
• Jafarian, M., & Vahdat, S. (2012). A fuzzy multi-attribute approach to select the welding process at high pressure vessel manufacturing. Journal of Manufacturing Processes, 14(3), 250–256.
   Google Scholar
• Jahan, A., & Edwards, K. L. (2015). A state-of-the-art survey on the influence of normalization techniques in ranking: Improving the materials selection process in engineering design. Materials & Design, 65, 335–342.
   Web of Science ®Google Scholar
• Jahanshahloo, G., Lotfi, F., & Izadikhah, M. (2006). An algorithmic method to extend TOPSIS for decision-making problems with interval data. Applied Mathematics and Computation, 175(2), 1375–1384.
   Web of Science ®Google Scholar
• Kumar, K., Prakash, A., & Tripathi, R. (2017). Spectrum handoff scheme with multiple attributes decision making for optimal network selection in cognitive radio networks. Digital Communications and Networks, 3, 164–175.
   Web of Science ®Google Scholar
• Li, C.-C., Dong, Y., Herrera, F., Herrera-Viedma, E., & Martnez, L. (2017). Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching. Information Fusion, 33, 29–40.
   Web of Science ®Google Scholar
• Liang, D., & Liu, D. (2014). Systematic studies on three-way decisions with interval-valued decision-theoretic rough sets. Information Sciences, 276, 186–203.
   Web of Science ®Google Scholar
• Liu, S., & Qiu, W. (1998). Studies on the basic theories for MADM. Systems Engineering Theory & Practice, 18(1), 38–43.
   Google Scholar
• Lourenzutti, R., & Krohling, R. A. (2016). A generalized TOPSIS method for group decision making with heterogeneous information in a dynamic environment. Information Sciences, 330, 1–18.
   Web of Science ®Google Scholar
• Nayak, S., Misra, B., & Behera, H. (2014). Impact of data normalization on stock index forecasting. International Journal of Computer Information Systems and Industrial Management Applications, 6, 257–269.
   Google Scholar
• Quan, Z., Qisheng, G., & Jinhua, G. (2008). New approach to multiple attribute decision making with interval numbers. Journal of Systems Engineering and Electronics, 19(2), 304–310.
   Web of Science ®Google Scholar
• Sarraf, A. Z., Mohaghar, A., & Bazargani, H. (2013). Developing TOPSIS method using statistical normalization for selecting Knowledge management strategies. Journal of Industrial Engineering and Management, 6(4), 860.
   Google Scholar
• Sengupta, A., & Pal, T. (2009). Fuzzy preference ordering of interval numbers in decision problems. In: Janusz Kacprzyk (eds), Studies in Fuzziness and Soft Computing, Volume 238, Springer-Verlag Berlin Heidelberg.
   Google Scholar
• Shih, H., Shyur, H., & Lee, E. (2007). An extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45(7), 801–813.
   Web of Science ®Google Scholar
• Turskis, Z., Zavadskas, E. K., & Peldschus, F. (2009). Multi-criteria optimization system for decision making in construction design and management. Engineering Economics, 61(1), 7–17.
   Google Scholar
• Vafaei, N., Ribeiro, R. A., & Camarinha-Matos, L. M. (2016). Normalization techniques for multi-criteria decision making: Analytical Hierarchy Process case study. In: Camarinha-Matos L.M., Falcão A.J., Vafaei N., Najdi S. (eds) Technological Innovation for Cyber-Physical Systems. DoCEIS 2016. IFIP Advances in Information and Communication Technology, vol 470, pp.261–269. Springer, Cham.
   Google Scholar
• Vafaei, N, Ribeiro, R. A, & Camarinha-Matos, Luis M. Camarinha. (2018). International Journal Of Information and Decision Sciences, 10(1), 19–38. doi:10.1504/IJIDS.2018.090667
   Google Scholar
• Xiong, G., Lan, J., Zhang, H., & Ding, T.-H. (2017). The effect of attribute normalization factors in attribute distance weighted average. Automatic Control and Computer Sciences, 51(2), 85–96.
   Web of Science ®Google Scholar
• Yazdani, M., Jahan, A., & Zavadskas, E. (2017). Analysis in material selection: Influence of normalization tools on COPRAS-G. Economic Computation & Economic Cybernetics Studies & Research, 51(1), 59–74.
   Web of Science ®Google Scholar
• Ye, J. (2017a). Bidirectional projection method for multiple attribute group decision making with neutrosophic numbers. Neural Computing & Applications, 28, 1021–1029.
   Web of Science ®Google Scholar
• Ye, J. (2017b). Projection and bidirectional projection measures of single-valued neutrosophic sets and their decision-making method for mechanical design schemes. Journal of Experimental & Theoretical Artificial Intelligence, 29(4), 731–740.
   Web of Science ®Google Scholar
• Ye, J. (2017c). Simplified neutrosophic harmonic averaging projection-based method for multiple attribute decision-making problems. International Journal of Machine Learning & Cybernetics, 8(3), 981–987.
   Web of Science ®Google Scholar
• Yu, X., Xu, Z., & Chen, Q. (2011). A method based on preference degrees for handling hybrid multiple attribute decision making problems. Expert Systems with Applications, 38(4), 3147–3154.
   Web of Science ®Google Scholar
• Yue, C. (2016). A geometric approach for ranking interval-valued intuitionistic fuzzy numbers with an application to group decision-making. Computers & Industrial Engineering, 102, 233–245.
   Web of Science ®Google Scholar
• Yue, C. (2017a). Entropy-based weights on decision makers in group decision-making setting with hybrid preference representations. Applied Soft Computing, 60, 737–749.
   Web of Science ®Google Scholar
• Yue, C. (2017b). Two normalized projection modfels and application to group decision-making. Journal of Intelligent and Fuzzy Systems, 32(6), 4389–4402.
   Web of Science ®Google Scholar
• Yue, C. (2018a). An interval-valued intuitionistic fuzzy projection-based approach and application to evaluating knowledge transfer effectiveness. Neural Computing & Applications. doi:10.1007/s00521-018-3571-5
   Google Scholar
• Yue, C. (2018b). Normalized projection approach to group decision-making with hybrid decision information. International Journal of Machine Learning and Cybernetics, 9(8), 1365–1375.
   Web of Science ®Google Scholar
• Yue, C. (2018c). A novel approach to interval comparison and application to software quality evaluation. Journal of Experimental & Theoretical Artificial Intelligence, 30(5), 583–602.
   Web of Science ®Google Scholar
• Yue, C. (2018d). A projection-based approach to software quality evaluation from the users’ perspectives. International Journal of Machine Learning and Cybernetics. doi:10.1007/s13042-018-0873-y
   Google Scholar
• Yue, C. (2018e). A symbol-based fuzzy decision-making approach to evaluate the user satisfaction on services in academic digital libraries. Iranian Journal of Fuzzy Systems. Retrieved from http://ijfs.usb.ac.ir/article_4103_b3d4be1fafe5d68f05a5396c1fb1754c.pdf
   Web of Science ®Google Scholar
• Yue, C., & Yue, Z. (2018). Measuring the satisfaction and loyalty for Chinese smartphone users: A simple symbol-based decision making method. Scientia Iranica. doi:10.24200/sci.2018.3841.0
   Web of Science ®Google Scholar
• Yue, Z. (2012a). Application of the projection method to determine weights of decision makers for group decision making. Scientia Iranica, 19(3), 872–878.
   Web of Science ®Google Scholar
• Yue, Z. (2012b). Extension of TOPSIS to determine weight of decision maker for group decision making problems with uncertain information. Expert Systems with Applications, 39(7), 6343–6350.
   Web of Science ®Google Scholar
• Yue, Z. (2014). TOPSIS-based group decision-making methodology in intuitionistic fuzzy setting. Information Sciences, 277, 141–153.
   Web of Science ®Google Scholar
• Yue, Z., & Jia, Y. (2015). A group decision making model with hybrid intuitionistic fuzzy information. Computers & Industrial Engineering, 87, 202–212.
   Web of Science ®Google Scholar
• Yue, Z., & Jia, Y. (2017a). A direct projection-based group decision-making methodology with crisp values and interval data. Soft Computing, 21(9), 2395–2405.
   Web of Science ®Google Scholar
• Yue, Z., & Jia, Y. (2017b). A projection-based approach to intuitionistic fuzzy group decision making. Scientia Iranica, 24(3), 1505–1518.
   Web of Science ®Google Scholar