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A type 2-hesitation fuzzy logic based multi-criteria group decision making system for intelligent shared environments

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Abstract

Intelligent environments aim to maximize the user comfort and safety while achieving other objectives such as energy minimization. Intelligent shared spaces (such as homes, classrooms, offices, libraries, etc.) need to consider the preferences of users from diverse backgrounds. However, there are high levels of uncertainties faced in intelligent shared spaces. Hence, there is a need to employ intelligent decision making systems which can consider the various users preferences and criteria in order to achieve the convenience of the various users while handling the faced uncertainties. Therefore, we propose a Fuzzy Logic-Multi-Criteria Group Decision Making (FL-MCGDM) system which provides a comprehensive valuation from a group of users/decision makers based on the aggregation of users’ opinions and preferences. The proposed FL-MCGDM system employs an interval type-2 fuzzy logic and hesitation index [from Intuitionistic Fuzzy Sets (IFSs)]. We have carried out experiments in the intelligent apartment (iSpace) located in the University of Essex to evaluate various approaches employing group decision making techniques for illumination selection in an intelligent shared environment. It was found that the Footprint of Uncertainty (of interval type-2 fuzzy sets) and hesitation index (of intuitionistic fuzzy sets (IFSs)) are able to provide a measure of the uncertainties present among the various decision makers. The proposed Type 2-Hesitation FL-MCGDM system better agrees with the users’ decision compared to existing fuzzy MCDM including the Fuzzy Logic based TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), type-1 FL-MCGDM and interval type-2 in FL-MCGDM.

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References

  • Atanassov K (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 110:87–96

    Article  MathSciNet  Google Scholar 

  • Atanassov K (1999) Intuitionistics fuzzy sets. Physica-Verlag, Heidelberg

    Book  Google Scholar 

  • Bilgin A, Dooley J, Whittington L, Hagras H, Henson M, Wagner C, Malibari A, Al-Ghamdi A, Al-haddad M, Al-Ghazzawi D (2012) Dynamic profile-selection for z-slices based type-2 fuzzy agents controlling multi-user ambient intelligent environments. In: Proceedings of the 2012 IEEE international conference on fuzzy systems, Brisbane

  • Castillo O, Alanis A, Garcia M, Arias H (2007) An intuitionistic fuzzy system for time series analysis in plant monitoring and diagnosis. Orig Res Article Appl Soft Comput 7(4):1227–1233

    Article  Google Scholar 

  • Chen SM, Lee LW (2010) Fuzzy multiple criteria hierarchical group decision-making based on interval type-2 fuzzy sets. IEEE Trans Syst Man Cybern Part A Syst Hum 40(5):1120–1128

    Google Scholar 

  • Chen SM, Lee LW (2010) Fuzzy multiple attributes group decision-making based on ranking values and the arithmetic operations of interval type-2 fuzzy sets. Expert Syst Appl 37(1):824–833

    Article  Google Scholar 

  • Chen SM, Lee LW (2010) Fuzzy multiple attributes group decision-making based on interval type-2 TOPSIS method. Expert Syst Appl 37(4):2790–2798

    Article  Google Scholar 

  • Chen SM, Yang MW, Lee LW, Yang SW (2012) Fuzzy multiple attributes group decision-making based on ranking interval type-2 fuzzy sets. Expert Syst Appl 39:5295–5308

    Article  Google Scholar 

  • Deschrijver G, Kerre E (2007) On the position of intuitionistic fuzzy set theory in the framework of theories modeling imprecision. Inf Sci 177:1860–1866

    Article  MATH  MathSciNet  Google Scholar 

  • Gong Z, Li L, Forrest J, Zhao Y (2011) The optimal priority models of the intuitionistic fuzzy preference relation and their application in selecting industries with higher meteorological sensitivity. Orig Res Article Expert Syst Appl 38(4):4394–4402

    Article  Google Scholar 

  • Hagras H (2004) A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE Trans Fuzzy Syst 12(4):524–539

    Article  Google Scholar 

  • Hasuike T, Ishii H (2009) A type-2 fuzzy portfolio selection problem considering possibility measure and crisp possibilistic mean value, IFSA-EUSFLAT, pp 1120–1124

  • Herrera F, Herrera-Viedma E (2000) Linguistic decision analysis: steps for solving decision problems under linguistic information. Orig Res Article Fuzzy Sets Syst 115(1):67–82

    Article  MATH  MathSciNet  Google Scholar 

  • Kahneman D, Frederick S (2005) A model of heuristic judgment. In: Holyoak KJ, Morrison RG (eds) The Cambridge handbook of thinking and reasoning. Cambridge University Press, Cambridge, pp 267–293

  • Kaur P, Chakrabortyb S (2007) A new approach to vendor selection problem with impact factor as an indirect measure of quality. J Modern Math Stat 1:1–8

    Google Scholar 

  • Liang Q, Karnik N, Mendel J (2000) Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems. IEEE Trans Syst Man Cybern Part C Appl Rev 30(3):329–339

    Article  Google Scholar 

  • Lin L, Yuan X, Xia Z (2007) Multi-criteria fuzzy decision-making methods based on intuitionistic fuzzy sets. J Comput Syst Sci 73:84–88

    Article  MATH  MathSciNet  Google Scholar 

  • Liu H, Wang G (2007) Multi-criteria decision-making methods based on intuitionistic fuzzy sets. Eur J Oper Res 179:220–233

    Article  MATH  Google Scholar 

  • Mendel J (1995) Fuzzy logic system for engineering: a tutorial. Proc IEEE 83(3):345–374

    Article  Google Scholar 

  • Mendel J, John R (2002) Type-2 fuzzy sets made simple. IEEE Trans Fuzzy Syst 10(2):117–127

    Article  Google Scholar 

  • Naim S, Hagras H (2012) A fuzzy logic based multi-criteria group decision making system for the assessment of umbilical cord acid-base balance. WCCI 2012 IEEE World Congress on computational intelligence, Brisbane, pp 2122–2129

  • Ozen T, Garibaldi J (2004) Effect of type-2 fuzzy membership function shape on modeling variation in human decision making. Proc IEEE Int Conf 2:971–976

    Google Scholar 

  • Ozen T, Garibaldi J (2003) Investigating adaptation in type-2 fuzzy logic systems applied to umbilical acid-base assessment. European symposium on intelligent technologies, hybrid systems and their implementation on smart adaptive systems, Oulu

  • Przemyslaw G, Edyta M (2005) Some notes on (Atanassov’s) intuitionistic fuzzy sets. Fuzzy Sets Syst 156:492–495

    Article  MATH  Google Scholar 

  • Rouhani S, Ghazanfari M, Jafari M (2012) Evaluation model of business intelligence for enterprise systems using fuzzy TOPSIS. Expert Syst Appl 39(3):3764–3771

    Article  Google Scholar 

  • Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper 48:9–26

    Article  MATH  Google Scholar 

  • Shim JP, Warkentin M, Courtney JF, Power DJ, Sharda R, Carlsson C (2002) Past, present, and future of decision support technology. Decis Support Syst 33:111–126

    Article  Google Scholar 

  • Su ZX, Xia GP, Chen MY, Wang L (2012) Induced generalized intuitionistic fuzzy OWA operator for multi-attribute group decision making. Expert Syst Appl 39:1902–1910

    Google Scholar 

  • Tadik D, Arsovski S, Stefanović M, Aleksić A (2010) A fuzzy AHP and TOPSIS for ELV dismantling selection. Int J Qual Res 4(2):139–144

    Google Scholar 

  • Tamalika C, Raya AK (2008) A new measure using intuitionistic fuzzy set theory and its application to edge detection. Appl Soft Comput 8:919–927

    Article  Google Scholar 

  • Uzokaa FE, Obotb O, Barkerc K, Osujid J (2011) An experimental comparison of fuzzy logic and analytic hierarchy process for medical decision support systems. Comput Methods Programs Biomed 103:10–27

    Article  Google Scholar 

  • Wang P (2009) QoS-aware web services selection with intuitionistic fuzzy set under consumer’s vague perception. Expert Syst Appl 36:4460–4466

    Article  Google Scholar 

  • Wang W, Liu X, Qin Y (2012) Multi-attribute group decision making models under interval type-2 fuzzy environment. Knowl Based Syst 30:121–128

    Article  Google Scholar 

  • Xu Z (2007) Intuitionistic preference relations and their application in group decision making. Inf Sci 177:2363–2379

    Article  MATH  Google Scholar 

  • Zadeh L (1975) The concepts of a linguistic variable and its application to approximate reasoning, part I. Inf Sci 8:199–249

    Article  MATH  MathSciNet  Google Scholar 

  • Zeleny M (1982) Multiple criteria decision making. Mac Graw Hill, pp 199–249

  • Zhang H, Yu L (2012) MADM method based on cross-entropy and extended TOPSIS with interval-valued intuitionistic fuzzy sets. Knowl Based Syst 30:115–120

    Article  Google Scholar 

Download references

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Correspondence to Syibrah Naim.

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Communicated by G. Acampora.

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Naim, S., Hagras, H. A type 2-hesitation fuzzy logic based multi-criteria group decision making system for intelligent shared environments. Soft Comput 18, 1305–1319 (2014). https://doi.org/10.1007/s00500-013-1145-0

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