January  2018, 14(1): 283-308. doi: 10.3934/jimo.2017047

## Some robust improved geometric aggregation operators under interval-valued intuitionistic fuzzy environment for multi-criteria decision-making process

 School of Mathematics, Thapar University Patiala-147004, Punjab, India

Received  July 2016 Revised  October 2016 Published  June 2017

Fund Project: The author would like to thank the Editor-in-Chief and referees for providing very helpful comments and suggestions

The objective of this manuscript is to present some new interactive geometric aggregation operators for the interval-valued intuitionistic fuzzy numbers (IVIFNs). In order to achieve it, firstly the shortcomings of the existing operators have been highlighted and then resolved it by defining new operational laws based on the pairs of hesitation degree between the membership functions. By using these improved laws, some geometric aggregation operators, namely interval-valued intuitionistic fuzzy Hamacher interactive weighted and hybrid geometric labeled as IIFHIWG and IIFHIHWG operators, respectively have been proposed. Furthermore, desirable properties corresponding to these operators have been stated. Finally, a decision-making method based on the proposed operator has been illustrated to demonstrate the approach. A computed result is compared with the existing results.

Citation: Harish Garg. Some robust improved geometric aggregation operators under interval-valued intuitionistic fuzzy environment for multi-criteria decision-making process. Journal of Industrial & Management Optimization, 2018, 14 (1) : 283-308. doi: 10.3934/jimo.2017047
##### References:
  K. Atanassov and G. Gargov, Interval-valued intuitionistic fuzzy sets, Fuzzy Sets and Systems, 31 (1989), 343-349. doi: 10.1016/0165-0114(89)90205-4. Google Scholar  K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20 (1986), 87-96. Google Scholar  W. -K. Chen and Y. -T. Chen, Fuzzy optimization in decision making of air quality management, Springer International Publishing, Cham, 2015,341-363,Google Scholar  H. Garg, Confidence levels based Pythagorean fuzzy aggregation operators and its application to decision-making process, Computational and Mathematical Organization Theory, (2017), 1-26. doi: 10.1007/s10588-017-9242-8. Google Scholar  H. Garg, Generalized intuitionistic fuzzy interactive geometric interaction operators using einstein t-norm and t-conorm and their application to decision making, Computer and Industrial Engineering, 101 (2016), 53-69. doi: 10.1016/j.cie.2016.08.017. Google Scholar  H. Garg, Generalized intuitionistic fuzzy multiplicative interactive geometric operators and their application to multiple criteria decision making, International Journal of Machine Learning and Cybernetics, 7 (2016), 1075-1092. doi: 10.1007/s13042-015-0432-8. Google Scholar  H. Garg, Generalized pythagorean fuzzy geometric aggregation operators using einstein t-norm and t-conorm for multicriteria decision-making process, International Journal of Intelligent Systems, 32 (2017), 597-630. doi: 10.1002/int.21860. Google Scholar  H. Garg, A new generalized improved score function of interval-valued intuitionistic fuzzy sets and applications in expert systems, Applied Soft Computing, 38 (2016), 988-999. doi: 10.1016/j.asoc.2015.10.040. Google Scholar  H. Garg, A new generalized Pythagorean fuzzy information aggregation using einstein operations and its application to decision making, International Journal of Intelligent Systems, 31 (2016), 886-920. doi: 10.1002/int.21809. Google Scholar  H. Garg, A novel accuracy function under interval-valued Pythagorean fuzzy environment for solving multicriteria decision making problem, Journal of Intelligent and Fuzzy Systems, 31 (2016), 529-540. doi: 10.3233/IFS-162165. Google Scholar  H. Garg, A novel approach for analyzing the reliability of series-parallel system using credibility theory and different types of intuitionistic fuzzy numbers, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 38 (2016), 1021-1035. doi: 10.1007/s40430-014-0284-2. Google Scholar  H. Garg, A novel correlation coefficients between Pythagorean fuzzy sets and its applications to decision-making processes, International Journal of Intelligent Systems, 31 (2016), 1234-1253. doi: 10.1002/int.21827. Google Scholar  H. Garg, Some series of intuitionistic fuzzy interactive averaging aggregation operators SpringerPlus 5 (2016), 999, doi: 10.1186/s40064-016-2591-9 doi: 10.1186/s40064-016-2591-9. Google Scholar  H. Garg, N. Agarwal and A. Tripathi, Entropy based multi-criteria decision making method under fuzzy environment and unknown attribute weights, Global Journal of Technology and Optimization, 6 (2015), 13-20. Google Scholar  M. Gupta, Group Decision Making in Fuzzy Environment -An Iterative Procedure Based on Group Dynamics, Springer International Publishing, Cham, 2015.Google Scholar  H. Hamacher, Uber logistic verknunpfungenn unssharfer aussagen und deren zugenhoringe bewertungsfunktione, Progress in Cybernatics and Systems Research, 3 (1978), 276-288. Google Scholar  Y. He, H. Chen, L. Zhou, B. Han, Q. Zhao and J. Liu, Generalized intuitionistic fuzzy geometric interaction operators and their application to decision making, Expert Systems with Applications, 41 (2014), 2484-2495. doi: 10.1016/j.eswa.2013.09.048. Google Scholar  K. Kumar and H. Garg, TOPSIS method based on the connection number of set pair analysis under interval-valued intuitionistic fuzzy set environment, Computational and Applied Mathematics, (2016), 1-11, doi: 10.1007/s40314-016-0402-0. Google Scholar  W. Li and C. Zhang, Decision Making-Interactive and Interactive Approaches, Springer International Publishing, Cham, 2015,Google Scholar  P. Liu, Some hamacher aggregation operators based on the interval-valued intuitionistic fuzzy numbers and their application to group decision making, IEEE Transactions on Fuzzy Systems, 22 (2013), 83-97. doi: 10.1109/TFUZZ.2013.2248736. Google Scholar  Nancy and H. Garg, An improved score function for ranking neutrosophic sets and its application to decision-making process, International Journal for Uncertainty Quantification, 6 (2016), 377-385. Google Scholar  Nancy and H. Garg, Novel single-valued neutrosophic decision making operators under frank norm operations and its application, International Journal for Uncertainty Quantification, 6 (2016), 361-375. Google Scholar  S. Singh and H. Garg, Distance measures between type-2 intuitionistic fuzzy sets and their application to multicriteria decision-making process, Applied Intelligence, 46 (2017), 788-799. doi: 10.1007/s10489-016-0869-9. Google Scholar  W. Wang and X. Liu, Some interval-valued intuitionistic fuzzy geometric aggregation operators based on einstein operations, in 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, 2012,604-608. doi: 10.1109/FSKD.2012.6234364. Google Scholar  W. Wang and X. Liu, The multi-attribute decision making method based on interval-valued intuitionistic fuzzy einstein hybrid weighted geometric operator, Computers and Mathematics with Applications, 66 (2013), 1845-1856. doi: 10.1016/j.camwa.2013.07.020. Google Scholar  G. Wei and X. Wang, Some geometric aggregation operators based on interval -valued intuitionistic fuzzy sets and their application to group decision making, in Proceedings of the IEEE international conference on computational intelligence and security, 2007,495-499. doi: 10.1109/CIS.2007.84. Google Scholar  Z. Xu and J. Chen, Approach to group decision making based on interval valued intuitionistic judgment matrices, Systems Engineering -Theory and Practice, 27 (2007), 126-133. doi: 10.1016/S1874-8651(08)60026-5. Google Scholar  Z. S. Xu, Intuitionistic fuzzy aggregation operators, IEEE Transaction of Fuzzy System, 15 (2007), 1179-1187. Google Scholar  Z. S. Xu, Intuitionistic preference relations and their application in group decision making, Information Sciences, 177 (2007), 2363-2379. doi: 10.1016/j.ins.2006.12.019. Google Scholar  Z. S. Xu, Methods for aggregating interval-valued intuitionistic fuzzy information and their application to decision making, Control and Decision, 22 (2007), 215-219. Google Scholar  Z. Xu and J. Chen, On geometric aggregation over interval-valued intuitionistic fuzzy information, Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on, 2 (2007), 466-471. doi: 10.1109/FSKD.2007.427. Google Scholar  Z. Xu and X. Gou, An overview of interval-valued intuitionistic fuzzy information aggregations and applications, Granular Computing, 2 (2017), 13-39. doi: 10.1007/s41066-016-0023-4. Google Scholar  Z. Xu and H. Wang, Managing multi-granularity linguistic information in qualitative group decision making: An overview, Granular Computing, 1 (2016), 21-35. doi: 10.1007/s41066-015-0006-x. Google Scholar  X. Zhao and G. Wei, Some intuitionistic fuzzy einstein hybrd aggregation operators and their application to multiple attribute decision making, Knowledge Based Systems, 37 (2013), 472-479. Google Scholar

show all references

##### References:
  K. Atanassov and G. Gargov, Interval-valued intuitionistic fuzzy sets, Fuzzy Sets and Systems, 31 (1989), 343-349. doi: 10.1016/0165-0114(89)90205-4. Google Scholar  K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20 (1986), 87-96. Google Scholar  W. -K. Chen and Y. -T. Chen, Fuzzy optimization in decision making of air quality management, Springer International Publishing, Cham, 2015,341-363,Google Scholar  H. Garg, Confidence levels based Pythagorean fuzzy aggregation operators and its application to decision-making process, Computational and Mathematical Organization Theory, (2017), 1-26. doi: 10.1007/s10588-017-9242-8. Google Scholar  H. Garg, Generalized intuitionistic fuzzy interactive geometric interaction operators using einstein t-norm and t-conorm and their application to decision making, Computer and Industrial Engineering, 101 (2016), 53-69. doi: 10.1016/j.cie.2016.08.017. Google Scholar  H. Garg, Generalized intuitionistic fuzzy multiplicative interactive geometric operators and their application to multiple criteria decision making, International Journal of Machine Learning and Cybernetics, 7 (2016), 1075-1092. doi: 10.1007/s13042-015-0432-8. Google Scholar  H. Garg, Generalized pythagorean fuzzy geometric aggregation operators using einstein t-norm and t-conorm for multicriteria decision-making process, International Journal of Intelligent Systems, 32 (2017), 597-630. doi: 10.1002/int.21860. Google Scholar  H. Garg, A new generalized improved score function of interval-valued intuitionistic fuzzy sets and applications in expert systems, Applied Soft Computing, 38 (2016), 988-999. doi: 10.1016/j.asoc.2015.10.040. Google Scholar  H. Garg, A new generalized Pythagorean fuzzy information aggregation using einstein operations and its application to decision making, International Journal of Intelligent Systems, 31 (2016), 886-920. doi: 10.1002/int.21809. Google Scholar  H. Garg, A novel accuracy function under interval-valued Pythagorean fuzzy environment for solving multicriteria decision making problem, Journal of Intelligent and Fuzzy Systems, 31 (2016), 529-540. doi: 10.3233/IFS-162165. Google Scholar  H. Garg, A novel approach for analyzing the reliability of series-parallel system using credibility theory and different types of intuitionistic fuzzy numbers, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 38 (2016), 1021-1035. doi: 10.1007/s40430-014-0284-2. Google Scholar  H. Garg, A novel correlation coefficients between Pythagorean fuzzy sets and its applications to decision-making processes, International Journal of Intelligent Systems, 31 (2016), 1234-1253. doi: 10.1002/int.21827. Google Scholar  H. Garg, Some series of intuitionistic fuzzy interactive averaging aggregation operators SpringerPlus 5 (2016), 999, doi: 10.1186/s40064-016-2591-9 doi: 10.1186/s40064-016-2591-9. Google Scholar  H. Garg, N. Agarwal and A. Tripathi, Entropy based multi-criteria decision making method under fuzzy environment and unknown attribute weights, Global Journal of Technology and Optimization, 6 (2015), 13-20. Google Scholar  M. Gupta, Group Decision Making in Fuzzy Environment -An Iterative Procedure Based on Group Dynamics, Springer International Publishing, Cham, 2015.Google Scholar  H. Hamacher, Uber logistic verknunpfungenn unssharfer aussagen und deren zugenhoringe bewertungsfunktione, Progress in Cybernatics and Systems Research, 3 (1978), 276-288. Google Scholar  Y. He, H. Chen, L. Zhou, B. Han, Q. Zhao and J. Liu, Generalized intuitionistic fuzzy geometric interaction operators and their application to decision making, Expert Systems with Applications, 41 (2014), 2484-2495. doi: 10.1016/j.eswa.2013.09.048. Google Scholar  K. Kumar and H. Garg, TOPSIS method based on the connection number of set pair analysis under interval-valued intuitionistic fuzzy set environment, Computational and Applied Mathematics, (2016), 1-11, doi: 10.1007/s40314-016-0402-0. Google Scholar  W. Li and C. Zhang, Decision Making-Interactive and Interactive Approaches, Springer International Publishing, Cham, 2015,Google Scholar  P. Liu, Some hamacher aggregation operators based on the interval-valued intuitionistic fuzzy numbers and their application to group decision making, IEEE Transactions on Fuzzy Systems, 22 (2013), 83-97. doi: 10.1109/TFUZZ.2013.2248736. Google Scholar  Nancy and H. Garg, An improved score function for ranking neutrosophic sets and its application to decision-making process, International Journal for Uncertainty Quantification, 6 (2016), 377-385. Google Scholar  Nancy and H. Garg, Novel single-valued neutrosophic decision making operators under frank norm operations and its application, International Journal for Uncertainty Quantification, 6 (2016), 361-375. Google Scholar  S. Singh and H. Garg, Distance measures between type-2 intuitionistic fuzzy sets and their application to multicriteria decision-making process, Applied Intelligence, 46 (2017), 788-799. doi: 10.1007/s10489-016-0869-9. Google Scholar  W. Wang and X. Liu, Some interval-valued intuitionistic fuzzy geometric aggregation operators based on einstein operations, in 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, 2012,604-608. doi: 10.1109/FSKD.2012.6234364. Google Scholar  W. Wang and X. Liu, The multi-attribute decision making method based on interval-valued intuitionistic fuzzy einstein hybrid weighted geometric operator, Computers and Mathematics with Applications, 66 (2013), 1845-1856. doi: 10.1016/j.camwa.2013.07.020. Google Scholar  G. Wei and X. Wang, Some geometric aggregation operators based on interval -valued intuitionistic fuzzy sets and their application to group decision making, in Proceedings of the IEEE international conference on computational intelligence and security, 2007,495-499. doi: 10.1109/CIS.2007.84. Google Scholar  Z. Xu and J. Chen, Approach to group decision making based on interval valued intuitionistic judgment matrices, Systems Engineering -Theory and Practice, 27 (2007), 126-133. doi: 10.1016/S1874-8651(08)60026-5. Google Scholar  Z. S. Xu, Intuitionistic fuzzy aggregation operators, IEEE Transaction of Fuzzy System, 15 (2007), 1179-1187. Google Scholar  Z. S. Xu, Intuitionistic preference relations and their application in group decision making, Information Sciences, 177 (2007), 2363-2379. doi: 10.1016/j.ins.2006.12.019. Google Scholar  Z. S. Xu, Methods for aggregating interval-valued intuitionistic fuzzy information and their application to decision making, Control and Decision, 22 (2007), 215-219. Google Scholar  Z. Xu and J. Chen, On geometric aggregation over interval-valued intuitionistic fuzzy information, Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on, 2 (2007), 466-471. doi: 10.1109/FSKD.2007.427. Google Scholar  Z. Xu and X. Gou, An overview of interval-valued intuitionistic fuzzy information aggregations and applications, Granular Computing, 2 (2017), 13-39. doi: 10.1007/s41066-016-0023-4. Google Scholar  Z. Xu and H. Wang, Managing multi-granularity linguistic information in qualitative group decision making: An overview, Granular Computing, 1 (2016), 21-35. doi: 10.1007/s41066-015-0006-x. Google Scholar  X. Zhao and G. Wei, Some intuitionistic fuzzy einstein hybrd aggregation operators and their application to multiple attribute decision making, Knowledge Based Systems, 37 (2013), 472-479. Google Scholar
Information about each alternative in the form of the IVIFNs
 $C_1$ $C_2$ $C_3$ $C_4$ $C_5$ $C_6$ $X_1$ $\langle[0.2, 0.3], [0.4, 0.5]\rangle$ $\langle[0.6, 0.7], [0.2, 0.3]\rangle$ $\langle[0.4, 0.5], [0.2, 0.4]\rangle$ $\langle[0.7, 0.8], [0.1, 0.2]\rangle$ $\langle[0.1, 0.3], [0.5, 0.6]\rangle$ $\langle[0.5, 0.7], [0.2, 0.3]\rangle$ $X_2$ $\langle[0.6, 0.7], [0.2, 0.3]\rangle$ $\langle[0.5, 0.6], [0.1, 0.3]\rangle$ $\langle[0.6, 0.7], [0.2, 0.3]\rangle$ $\langle[0.6, 0.7], [0.1, 0.2]\rangle$ $\langle[0.3, 0.4], [0.5, 0.6]\rangle$ $\langle[0.4, 0.7], [0.1, 0.2]\rangle$ $X_3$ $\langle[0.4, 0.5], [0.3, 0.4]\rangle$ $\langle[0.7, 0.8], [0.1, 0.2]\rangle$ $\langle[0.5, 0.6], [0.3, 0.4]\rangle$ $\langle[0.6, 0.7], [0.1, 0.3]\rangle$ $\langle[0.4, 0.5], [0.3, 0.4]\rangle$ $\langle[0.3, 0.5], [0.1, 0.3]\rangle$ $X_4$ $\langle[0.6, 0.7], [0.2, 0.3]\rangle$ $\langle[0.5, 0.6], [0.1, 0.3]\rangle$ $\langle[0.7, 0.8], [0.1, 0.2]\rangle$ $\langle[0.3, 0.4], [0.1, 0.2]\rangle$ $\langle[0.5, 0.6], [0.1, 0.3]\rangle$ $\langle[0.7, 0.8], [0.1, 0.2]\rangle$ $X_5$ $\langle[0.5, 0.6], [0.3, 0.4]\rangle$ $\langle[0.3, 0.4], [0.3, 0.5]\rangle$ $\langle[0.6, 0.7], [0.1, 0.3]\rangle$ $\langle[0.6, 0.8], [0.1, 0.2]\rangle$ $\langle[0.6, 0.7], [0.2, 0.3]\rangle$ $\langle[0.5, 0.6], [0.2, 0.4]\rangle$
 $C_1$ $C_2$ $C_3$ $C_4$ $C_5$ $C_6$ $X_1$ $\langle[0.2, 0.3], [0.4, 0.5]\rangle$ $\langle[0.6, 0.7], [0.2, 0.3]\rangle$ $\langle[0.4, 0.5], [0.2, 0.4]\rangle$ $\langle[0.7, 0.8], [0.1, 0.2]\rangle$ $\langle[0.1, 0.3], [0.5, 0.6]\rangle$ $\langle[0.5, 0.7], [0.2, 0.3]\rangle$ $X_2$ $\langle[0.6, 0.7], [0.2, 0.3]\rangle$ $\langle[0.5, 0.6], [0.1, 0.3]\rangle$ $\langle[0.6, 0.7], [0.2, 0.3]\rangle$ $\langle[0.6, 0.7], [0.1, 0.2]\rangle$ $\langle[0.3, 0.4], [0.5, 0.6]\rangle$ $\langle[0.4, 0.7], [0.1, 0.2]\rangle$ $X_3$ $\langle[0.4, 0.5], [0.3, 0.4]\rangle$ $\langle[0.7, 0.8], [0.1, 0.2]\rangle$ $\langle[0.5, 0.6], [0.3, 0.4]\rangle$ $\langle[0.6, 0.7], [0.1, 0.3]\rangle$ $\langle[0.4, 0.5], [0.3, 0.4]\rangle$ $\langle[0.3, 0.5], [0.1, 0.3]\rangle$ $X_4$ $\langle[0.6, 0.7], [0.2, 0.3]\rangle$ $\langle[0.5, 0.6], [0.1, 0.3]\rangle$ $\langle[0.7, 0.8], [0.1, 0.2]\rangle$ $\langle[0.3, 0.4], [0.1, 0.2]\rangle$ $\langle[0.5, 0.6], [0.1, 0.3]\rangle$ $\langle[0.7, 0.8], [0.1, 0.2]\rangle$ $X_5$ $\langle[0.5, 0.6], [0.3, 0.4]\rangle$ $\langle[0.3, 0.4], [0.3, 0.5]\rangle$ $\langle[0.6, 0.7], [0.1, 0.3]\rangle$ $\langle[0.6, 0.8], [0.1, 0.2]\rangle$ $\langle[0.6, 0.7], [0.2, 0.3]\rangle$ $\langle[0.5, 0.6], [0.2, 0.4]\rangle$
Effect of the parameter $\gamma$ on the ranking of the alternatives by IIFHIWG and the existing operators
 $\gamma=1$ $\gamma=2$ $\gamma=3$ Wei and Wang  Proposed Wang and Liu  Proposed Liu  Proposed Score value Score value Score value $X_1$ 0.0548 0.1346 0.0727 0.1454 0.0822 0.1517 $X_2$ 0.2874 0.3174 0.2998 0.3310 0.3065 0.3388 $X_3$ 0.2139 0.2713 0.2205 0.2760 0.2245 0.2793 $X_4$ 0.4463 0.4997 0.4535 0.5013 0.4576 0.5024 $X_5$ 0.2985 0.3119 0.3047 0.3166 0.3083 0.3197 ranking $X_4 \succ X_5 \succ X_2 \succ X_3 \succ X_1$ $X_4 \succ X_2 \succ X_5 \succ X_3 \succ X_1$ $X_4 \succ X_5 \succ X_2 \succ X_3 \succ X_1$ $X_4 \succ X_2 \succ X_5 \succ X_3 \succ X_1$ $X_4 \succ X_5 \succ X_2 \succ X_3 \succ X_1$ $X_4 \succ X_2 \succ X_5 \succ X_3 \succ X_1$
 $\gamma=1$ $\gamma=2$ $\gamma=3$ Wei and Wang  Proposed Wang and Liu  Proposed Liu  Proposed Score value Score value Score value $X_1$ 0.0548 0.1346 0.0727 0.1454 0.0822 0.1517 $X_2$ 0.2874 0.3174 0.2998 0.3310 0.3065 0.3388 $X_3$ 0.2139 0.2713 0.2205 0.2760 0.2245 0.2793 $X_4$ 0.4463 0.4997 0.4535 0.5013 0.4576 0.5024 $X_5$ 0.2985 0.3119 0.3047 0.3166 0.3083 0.3197 ranking $X_4 \succ X_5 \succ X_2 \succ X_3 \succ X_1$ $X_4 \succ X_2 \succ X_5 \succ X_3 \succ X_1$ $X_4 \succ X_5 \succ X_2 \succ X_3 \succ X_1$ $X_4 \succ X_2 \succ X_5 \succ X_3 \succ X_1$ $X_4 \succ X_5 \succ X_2 \succ X_3 \succ X_1$ $X_4 \succ X_2 \succ X_5 \succ X_3 \succ X_1$
Effect of the parameter $\gamma$ on the ranking of the alternatives by using IIFHIHWG and the existing operators
 $\gamma=1$ $\gamma=2$ $\gamma=3$ Wei and Wang  Proposed Wang and Liu  Proposed Liu  Proposed Score value Score value Score value $X_1$ 0.1221 0.2080 0.1434 0.2163 0.1558 0.2151 $X_2$ 0.3304 0.3674 0.3443 0.3734 0.3522 0.3795 $X_3$ 0.2535 0.3019 0.2630 0.3068 0.2692 0.3113 $X_4$ 0.3705 0.4853 0.3815 0.4815 0.3880 0.4828 $X_5$ 0.3141 0.3414 0.3164 0.3510 0.3203 0.3500 ranking $X_4 \succ X_2 \succ X_5 \succ X_3 \succ X_1$ $X_4 \succ X_2 \succ X_5 \succ X_3 \succ X_1$ $X_4 \succ X_2 \succ X_5 \succ X_3 \succ X_1$ $X_4 \succ X_2 \succ X_5 \succ X_3 \succ X_1$ $X_4 \succ X_2 \succ X_5 \succ X_3 \succ X_1$ $X_4 \succ X_2 \succ X_5 \succ X_3 \succ X_1$
 $\gamma=1$ $\gamma=2$ $\gamma=3$ Wei and Wang  Proposed Wang and Liu  Proposed Liu  Proposed Score value Score value Score value $X_1$ 0.1221 0.2080 0.1434 0.2163 0.1558 0.2151 $X_2$ 0.3304 0.3674 0.3443 0.3734 0.3522 0.3795 $X_3$ 0.2535 0.3019 0.2630 0.3068 0.2692 0.3113 $X_4$ 0.3705 0.4853 0.3815 0.4815 0.3880 0.4828 $X_5$ 0.3141 0.3414 0.3164 0.3510 0.3203 0.3500 ranking $X_4 \succ X_2 \succ X_5 \succ X_3 \succ X_1$ $X_4 \succ X_2 \succ X_5 \succ X_3 \succ X_1$ $X_4 \succ X_2 \succ X_5 \succ X_3 \succ X_1$ $X_4 \succ X_2 \succ X_5 \succ X_3 \succ X_1$ $X_4 \succ X_2 \succ X_5 \succ X_3 \succ X_1$ $X_4 \succ X_2 \succ X_5 \succ X_3 \succ X_1$
Ordering of the attributes for different $\gamma$
 $\gamma$ By IIFHIWG By IIFHIHWG Aggregated IVIFN Score values Aggregated IVIFN Score values 0.1 $X_1$ $\big\langle[0.3771, 0.5753], [0.2996, 0.4247]\big\rangle$ 0.1140 $\big\langle[0.4562, 0.6029], [0.2818, 0.3971]\big\rangle$ 0.1901 $X_2$ $\big\langle[0.5042, 0.6545], [0.2324, 0.3455]\big\rangle$ 0.2904 $\big\langle[0.5016, 0.6889], [0.1971, 0.3111]\big\rangle$ 0.3411 $X_3$ $\big\langle[0.4719, 0.6441], [0.2310, 0.3559]\big\rangle$ 0.2646 $\big\langle[0.4808, 0.6605], [0.2187, 0.3395]\big\rangle$ 0.2916 $X_4$ $\big\langle[0.6130, 0.7519], [0.1218, 0.2481]\big\rangle$ 0.4975 $\big\langle[0.5404, 0.7670], [0.1097, 0.2330]\big\rangle$ 0.4824 $X_5$ $\big\langle[0.5306, 0.6405], [0.2027, 0.3595]\big\rangle$ 0.3045 $\big\langle[0.5338, 0.6603], [0.1876, 0.3397]\big\rangle$ 0.3334 Ranking $X_4\succ X_5 \succ X_2 \succ X_3 \succ X_1$ $X_4\succ X_2\succ X_5 \succ X_3 \succ X_1$ 0.5 $X_1$ $\big\langle[0.3805, 0.5819], [0.2933, 0.4181]\big\rangle$ 0.1255 $\big\langle[0.4597 0.6086], [0.2764 0.3914]\big\rangle$ 0.2003 $X_2$ $\big\langle[0.5092, 0.6634], [0.2249, 0.3366]\big\rangle$ 0.3056 $\big\langle[0.5062 0.6977], [0.1897 0.3023]\big\rangle$ 0.3560 $X_3$ $\big\langle[0.4734, 0.6455], [0.2285, 0.3545]\big\rangle$ 0.2679 $\big\langle[0.4826 0.6633], [0.2157 0.3367]\big\rangle$ 0.2967 $X_4$ $\big\langle[0.6133, 0.7526], [0.1214, 0.2474]\big\rangle$ 0.4986 $\big\langle[0.5406 0.7681], [0.1093 0.2319]\big\rangle$ 0.4838 $X_5$ $\big\langle[0.5318, 0.6429], [0.2009, 0.3571]\big\rangle$ 0.3084 $\big\langle[0.5352 0.6639], [0.1855 0.3361]\big\rangle$ 0.3388 Ranking $X_4\succ X_5 \succ X_2 \succ X_3 \succ X_1$ $X_4\succ X_2\succ X_5 \succ X_3 \succ X_1$ 1 $X_1$ $\big\langle[0.3834, 0.5868], [0.2878, 0.4132]\big\rangle$ 0.1346 $\big\langle[0.4626, 0.6125], [0.2717, 0.3875]\big\rangle$ 0.2080 $X_2$ $\big\langle[0.5134, 0.6699], [0.2185, 0.3301]\big\rangle$ 0.3174 $\big\langle[0.5100, 0.7041], [0.1835, 0.2959]\big\rangle$ 0.3674 $X_3$ $\big\langle[0.4750, 0.6467], [0.2260, 0.3533]\big\rangle$ 0.2713 $\big\langle[0.4845, 0.6659], [0.2126, 0.3341]\big\rangle$ 0.3019 $X_4$ $\big\langle[0.6136, 0.7533], [0.1210, 0.2467]\big\rangle$ 0.4997 $\big\langle[0.5409, 0.7693], [0.1089, 0.2307]\big\rangle$ 0.4853 $X_5$ $\big\langle[0.5330, 0.6449], [0.1991, 0.3551]\big\rangle$ 0.3119 $\big\langle[0.5141, 0.6751], [0.1813, 0.3249]\big\rangle$ 0.3414 Ranking $X_4\succ X_2 \succ X_5 \succ X_3 \succ X_1$ $X_4\succ X_2\succ X_5 \succ X_3 \succ X_1$ 2 $X_1$ $\big\langle[0.3872, 0.5923], [0.2809, 0.4077]\big\rangle$ 0.1454 $\big\langle[0.4663, 0.6161], [0.2659, 0.3839]\big\rangle$ 0.2163 $X_2$ $\big\langle[0.5186, 0.6770], [0.2106, 0.3230]\big\rangle$ 0.3310 $\big\langle[0.5268, 0.7023], [0.1847, 0.2977]\big\rangle$ 0.3734 $X_3$ $\big\langle[0.4774, 0.6484], [0.2221, 0.3516]\big\rangle$ 0.2760 $\big\langle[0.4890, 0.6646], [0.2047, 0.3354]\big\rangle$ 0.3068 $X_4$ $\big\langle[0.6141, 0.7543], [0.1202, 0.2457]\big\rangle$ 0.5013 $\big\langle[0.5556, 0.7623], [0.1172, 0.2377]\big\rangle$ 0.4815 $X_5$ $\big\langle[0.5348, 0.6473], [0.1963, 0.3527]\big\rangle$ 0.3166 $\big\langle[0.5166, 0.6813], [0.1772, 0.3187]\big\rangle$ 0.3510 Ranking $X_4\succ X_2 \succ X_5 \succ X_3 \succ X_1$ $X_4\succ X_2\succ X_5 \succ X_3 \succ X_1$ 5 $X_1$ $\big\langle[0.3922, 0.5987], [0.2716 0.4013]\big\rangle$ 0.1590 $\big\langle[0.4324, 0.6341], [0.2537, 0.3659]\big\rangle$ 0.2235 $X_2$ $\big\langle[0.5256, 0.6849], [0.1999 0.3151]\big\rangle$ 0.3478 $\big\langle[0.5195, 0.7130], [0.1776, 0.2870]\big\rangle$ 0.3840 $X_3$ $\big\langle[0.4815, 0.6506], [0.2153 0.3494]\big\rangle$ 0.2837 $\big\langle[0.4939, 0.6691], [0.1969, 0.3309]\big\rangle$ 0.3176 $X_4$ $\big\langle[0.6150, 0.7558], [0.1189 0.2442]\big\rangle$ 0.5039 $\big\langle[0.5565, 0.7642], [0.1158, 0.2358]\big\rangle$ 0.4846 $X_5$ $\big\langle[0.5379, 0.6504], [0.1917 0.3496]\big\rangle$ 0.3235 $\big\langle[0.5521, 0.6726], [0.1906, 0.3274]\big\rangle$ 0.3534 Ranking $X_4\succ X_2\succ X_5 \succ X_3 \succ X_1$ $X_4\succ X_2\succ X_5 \succ X_3 \succ X_1$ 10 $X_1$ $\big\langle[0.3952, 0.6020], [0.2659, 0.3980]\big\rangle$ 0.1667 $\big\langle[0.4360, 0.6387], [0.2474, 0.3613]\big\rangle$ 0.2330 $X_2$ $\big\langle[0.5300, 0.6889], [0.1932, 0.3111]\big\rangle$ 0.3573 $\big\langle[0.5220, 0.7368], [0.1572, 0.2632]\big\rangle$ 0.4192 $X_3$ $\big\langle[0.4847, 0.6519], [0.2102, 0.3481]\big\rangle$ 0.2891 $\big\langle[0.4767, 0.6968], [0.1744, 0.3032]\big\rangle$ 0.3479 $X_4$ $\big\langle[0.6158, 0.7568], [0.1178, 0.2432]\big\rangle$ 0.5058 $\big\langle[0.5573, 0.7657], [0.1147, 0.2343]\big\rangle$ 0.4870 $X_5$ $\big\langle[0.5403, 0.6522], [0.1882, 0.3478]\big\rangle$ 0.3282 $\big\langle[0.5608, 0.6594], [0.1933, 0.3406]\big\rangle$ 0.3432 Ranking $X_4\succ X_2\succ X_5 \succ X_3 \succ X_1$ $X_4\succ X_2\succ X_3 \succ X_5 \succ X_1$ 25 $X_1$ $\big\langle[0.3979, 0.6046], [0.2610, 0.3954]\big\rangle$ 0.1730 $\big\langle[0.4217, 0.6566], [0.2234, 0.3434]\big\rangle$ 0.2558 $X_2$ $\big\langle[0.5338, 0.6920], [0.1874, 0.3080]\big\rangle$ 0.3652 $\big\langle[0.5570, 0.7047], [0.1689, 0.2953]\big\rangle$ 0.3988 $X_3$ $\big\langle[0.4878, 0.6530], [0.2051, 0.3470]\big\rangle$ 0.2943 $\big\langle[0.5176, 0.6687], [0.1871, 0.3313]\big\rangle$ 0.3339 $X_4$ $\big\langle[0.6165, 0.7576], [0.1167, 0.2424]\big\rangle$ 0.5075 $\big\langle[0.6057, 0.7394], [0.1297, 0.2606]\big\rangle$ 0.4774 $X_5$ $\big\langle[0.5426, 0.6536], [0.1847, 0.3464]\big\rangle$ 0.3325 $\big\langle[0.5600, 0.6525], [0.1944, 0.3475]\big\rangle$ 0.3353 Ranking $X_4\succ X_2\succ X_5 \succ X_3 \succ X_1$ $X_4\succ X_2\succ X_5 \succ X_3 \succ X_1$
 $\gamma$ By IIFHIWG By IIFHIHWG Aggregated IVIFN Score values Aggregated IVIFN Score values 0.1 $X_1$ $\big\langle[0.3771, 0.5753], [0.2996, 0.4247]\big\rangle$ 0.1140 $\big\langle[0.4562, 0.6029], [0.2818, 0.3971]\big\rangle$ 0.1901 $X_2$ $\big\langle[0.5042, 0.6545], [0.2324, 0.3455]\big\rangle$ 0.2904 $\big\langle[0.5016, 0.6889], [0.1971, 0.3111]\big\rangle$ 0.3411 $X_3$ $\big\langle[0.4719, 0.6441], [0.2310, 0.3559]\big\rangle$ 0.2646 $\big\langle[0.4808, 0.6605], [0.2187, 0.3395]\big\rangle$ 0.2916 $X_4$ $\big\langle[0.6130, 0.7519], [0.1218, 0.2481]\big\rangle$ 0.4975 $\big\langle[0.5404, 0.7670], [0.1097, 0.2330]\big\rangle$ 0.4824 $X_5$ $\big\langle[0.5306, 0.6405], [0.2027, 0.3595]\big\rangle$ 0.3045 $\big\langle[0.5338, 0.6603], [0.1876, 0.3397]\big\rangle$ 0.3334 Ranking $X_4\succ X_5 \succ X_2 \succ X_3 \succ X_1$ $X_4\succ X_2\succ X_5 \succ X_3 \succ X_1$ 0.5 $X_1$ $\big\langle[0.3805, 0.5819], [0.2933, 0.4181]\big\rangle$ 0.1255 $\big\langle[0.4597 0.6086], [0.2764 0.3914]\big\rangle$ 0.2003 $X_2$ $\big\langle[0.5092, 0.6634], [0.2249, 0.3366]\big\rangle$ 0.3056 $\big\langle[0.5062 0.6977], [0.1897 0.3023]\big\rangle$ 0.3560 $X_3$ $\big\langle[0.4734, 0.6455], [0.2285, 0.3545]\big\rangle$ 0.2679 $\big\langle[0.4826 0.6633], [0.2157 0.3367]\big\rangle$ 0.2967 $X_4$ $\big\langle[0.6133, 0.7526], [0.1214, 0.2474]\big\rangle$ 0.4986 $\big\langle[0.5406 0.7681], [0.1093 0.2319]\big\rangle$ 0.4838 $X_5$ $\big\langle[0.5318, 0.6429], [0.2009, 0.3571]\big\rangle$ 0.3084 $\big\langle[0.5352 0.6639], [0.1855 0.3361]\big\rangle$ 0.3388 Ranking $X_4\succ X_5 \succ X_2 \succ X_3 \succ X_1$ $X_4\succ X_2\succ X_5 \succ X_3 \succ X_1$ 1 $X_1$ $\big\langle[0.3834, 0.5868], [0.2878, 0.4132]\big\rangle$ 0.1346 $\big\langle[0.4626, 0.6125], [0.2717, 0.3875]\big\rangle$ 0.2080 $X_2$ $\big\langle[0.5134, 0.6699], [0.2185, 0.3301]\big\rangle$ 0.3174 $\big\langle[0.5100, 0.7041], [0.1835, 0.2959]\big\rangle$ 0.3674 $X_3$ $\big\langle[0.4750, 0.6467], [0.2260, 0.3533]\big\rangle$ 0.2713 $\big\langle[0.4845, 0.6659], [0.2126, 0.3341]\big\rangle$ 0.3019 $X_4$ $\big\langle[0.6136, 0.7533], [0.1210, 0.2467]\big\rangle$ 0.4997 $\big\langle[0.5409, 0.7693], [0.1089, 0.2307]\big\rangle$ 0.4853 $X_5$ $\big\langle[0.5330, 0.6449], [0.1991, 0.3551]\big\rangle$ 0.3119 $\big\langle[0.5141, 0.6751], [0.1813, 0.3249]\big\rangle$ 0.3414 Ranking $X_4\succ X_2 \succ X_5 \succ X_3 \succ X_1$ $X_4\succ X_2\succ X_5 \succ X_3 \succ X_1$ 2 $X_1$ $\big\langle[0.3872, 0.5923], [0.2809, 0.4077]\big\rangle$ 0.1454 $\big\langle[0.4663, 0.6161], [0.2659, 0.3839]\big\rangle$ 0.2163 $X_2$ $\big\langle[0.5186, 0.6770], [0.2106, 0.3230]\big\rangle$ 0.3310 $\big\langle[0.5268, 0.7023], [0.1847, 0.2977]\big\rangle$ 0.3734 $X_3$ $\big\langle[0.4774, 0.6484], [0.2221, 0.3516]\big\rangle$ 0.2760 $\big\langle[0.4890, 0.6646], [0.2047, 0.3354]\big\rangle$ 0.3068 $X_4$ $\big\langle[0.6141, 0.7543], [0.1202, 0.2457]\big\rangle$ 0.5013 $\big\langle[0.5556, 0.7623], [0.1172, 0.2377]\big\rangle$ 0.4815 $X_5$ $\big\langle[0.5348, 0.6473], [0.1963, 0.3527]\big\rangle$ 0.3166 $\big\langle[0.5166, 0.6813], [0.1772, 0.3187]\big\rangle$ 0.3510 Ranking $X_4\succ X_2 \succ X_5 \succ X_3 \succ X_1$ $X_4\succ X_2\succ X_5 \succ X_3 \succ X_1$ 5 $X_1$ $\big\langle[0.3922, 0.5987], [0.2716 0.4013]\big\rangle$ 0.1590 $\big\langle[0.4324, 0.6341], [0.2537, 0.3659]\big\rangle$ 0.2235 $X_2$ $\big\langle[0.5256, 0.6849], [0.1999 0.3151]\big\rangle$ 0.3478 $\big\langle[0.5195, 0.7130], [0.1776, 0.2870]\big\rangle$ 0.3840 $X_3$ $\big\langle[0.4815, 0.6506], [0.2153 0.3494]\big\rangle$ 0.2837 $\big\langle[0.4939, 0.6691], [0.1969, 0.3309]\big\rangle$ 0.3176 $X_4$ $\big\langle[0.6150, 0.7558], [0.1189 0.2442]\big\rangle$ 0.5039 $\big\langle[0.5565, 0.7642], [0.1158, 0.2358]\big\rangle$ 0.4846 $X_5$ $\big\langle[0.5379, 0.6504], [0.1917 0.3496]\big\rangle$ 0.3235 $\big\langle[0.5521, 0.6726], [0.1906, 0.3274]\big\rangle$ 0.3534 Ranking $X_4\succ X_2\succ X_5 \succ X_3 \succ X_1$ $X_4\succ X_2\succ X_5 \succ X_3 \succ X_1$ 10 $X_1$ $\big\langle[0.3952, 0.6020], [0.2659, 0.3980]\big\rangle$ 0.1667 $\big\langle[0.4360, 0.6387], [0.2474, 0.3613]\big\rangle$ 0.2330 $X_2$ $\big\langle[0.5300, 0.6889], [0.1932, 0.3111]\big\rangle$ 0.3573 $\big\langle[0.5220, 0.7368], [0.1572, 0.2632]\big\rangle$ 0.4192 $X_3$ $\big\langle[0.4847, 0.6519], [0.2102, 0.3481]\big\rangle$ 0.2891 $\big\langle[0.4767, 0.6968], [0.1744, 0.3032]\big\rangle$ 0.3479 $X_4$ $\big\langle[0.6158, 0.7568], [0.1178, 0.2432]\big\rangle$ 0.5058 $\big\langle[0.5573, 0.7657], [0.1147, 0.2343]\big\rangle$ 0.4870 $X_5$ $\big\langle[0.5403, 0.6522], [0.1882, 0.3478]\big\rangle$ 0.3282 $\big\langle[0.5608, 0.6594], [0.1933, 0.3406]\big\rangle$ 0.3432 Ranking $X_4\succ X_2\succ X_5 \succ X_3 \succ X_1$ $X_4\succ X_2\succ X_3 \succ X_5 \succ X_1$ 25 $X_1$ $\big\langle[0.3979, 0.6046], [0.2610, 0.3954]\big\rangle$ 0.1730 $\big\langle[0.4217, 0.6566], [0.2234, 0.3434]\big\rangle$ 0.2558 $X_2$ $\big\langle[0.5338, 0.6920], [0.1874, 0.3080]\big\rangle$ 0.3652 $\big\langle[0.5570, 0.7047], [0.1689, 0.2953]\big\rangle$ 0.3988 $X_3$ $\big\langle[0.4878, 0.6530], [0.2051, 0.3470]\big\rangle$ 0.2943 $\big\langle[0.5176, 0.6687], [0.1871, 0.3313]\big\rangle$ 0.3339 $X_4$ $\big\langle[0.6165, 0.7576], [0.1167, 0.2424]\big\rangle$ 0.5075 $\big\langle[0.6057, 0.7394], [0.1297, 0.2606]\big\rangle$ 0.4774 $X_5$ $\big\langle[0.5426, 0.6536], [0.1847, 0.3464]\big\rangle$ 0.3325 $\big\langle[0.5600, 0.6525], [0.1944, 0.3475]\big\rangle$ 0.3353 Ranking $X_4\succ X_2\succ X_5 \succ X_3 \succ X_1$ $X_4\succ X_2\succ X_5 \succ X_3 \succ X_1$
  Harish Garg, Kamal Kumar. Group decision making approach based on possibility degree measure under linguistic interval-valued intuitionistic fuzzy set environment. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-23. doi: 10.3934/jimo.2018162  Muhammad Qiyas, Saleem Abdullah, Shahzaib Ashraf, Saifullah Khan, Aziz Khan. Triangular picture fuzzy linguistic induced ordered weighted aggregation operators and its application on decision making problems. Mathematical Foundations of Computing, 2019, 2 (3) : 183-201. doi: 10.3934/mfc.2019013  Zengjing Chen, Yuting Lan, Gaofeng Zong. Strong law of large numbers for upper set-valued and fuzzy-set valued probability. Mathematical Control & Related Fields, 2015, 5 (3) : 435-452. doi: 10.3934/mcrf.2015.5.435  Andrea Bonito, Roland Glowinski. On the nodal set of the eigenfunctions of the Laplace-Beltrami operator for bounded surfaces in $R^3$: A computational approach. Communications on Pure & Applied Analysis, 2014, 13 (5) : 2115-2126. doi: 10.3934/cpaa.2014.13.2115  Gregorio Díaz, Jesús Ildefonso Díaz. On the free boundary associated with the stationary Monge--Ampère operator on the set of non strictly convex functions. Discrete & Continuous Dynamical Systems - A, 2015, 35 (4) : 1447-1468. doi: 10.3934/dcds.2015.35.1447  Leonid A. Bunimovich. Dynamical systems and operations research: A basic model. Discrete & Continuous Dynamical Systems - B, 2001, 1 (2) : 209-218. doi: 10.3934/dcdsb.2001.1.209  Xiaodong Liu, Wanquan Liu. The framework of axiomatics fuzzy sets based fuzzy classifiers. Journal of Industrial & Management Optimization, 2008, 4 (3) : 581-609. doi: 10.3934/jimo.2008.4.581  Jiaquan Zhan, Fanyong Meng. Cores and optimal fuzzy communication structures of fuzzy games. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1187-1198. doi: 10.3934/dcdss.2019082  Juan J. Nieto, M. Victoria Otero-Espinar, Rosana Rodríguez-López. Dynamics of the fuzzy logistic family. Discrete & Continuous Dynamical Systems - B, 2010, 14 (2) : 699-717. doi: 10.3934/dcdsb.2010.14.699  Natalia Skripnik. Averaging of fuzzy integral equations. Discrete & Continuous Dynamical Systems - B, 2017, 22 (5) : 1999-2010. doi: 10.3934/dcdsb.2017118  Purnima Pandit. Fuzzy system of linear equations. Conference Publications, 2013, 2013 (special) : 619-627. doi: 10.3934/proc.2013.2013.619  Lotfi Tadj, Zhe George Zhang, Chakib Tadj. A queueing analysis of multi-purpose production facility's operations. Journal of Industrial & Management Optimization, 2011, 7 (1) : 19-30. doi: 10.3934/jimo.2011.7.19  Masayuki Sato, Naoki Fujita, A. J. Sievers. Logic operations demonstrated with localized vibrations in a micromechanical cantilever array. Discrete & Continuous Dynamical Systems - S, 2011, 4 (5) : 1287-1298. doi: 10.3934/dcdss.2011.4.1287  Ondrej Budáč, Michael Herrmann, Barbara Niethammer, Andrej Spielmann. On a model for mass aggregation with maximal size. Kinetic & Related Models, 2011, 4 (2) : 427-439. doi: 10.3934/krm.2011.4.427  Dong Li, Xiaoyi Zhang. On a nonlocal aggregation model with nonlinear diffusion. Discrete & Continuous Dynamical Systems - A, 2010, 27 (1) : 301-323. doi: 10.3934/dcds.2010.27.301  Yanghong Huang, Andrea Bertozzi. Asymptotics of blowup solutions for the aggregation equation. Discrete & Continuous Dynamical Systems - B, 2012, 17 (4) : 1309-1331. doi: 10.3934/dcdsb.2012.17.1309  Erik Kropat, Gerhard Wilhelm Weber. Fuzzy target-environment networks and fuzzy-regression approaches. Numerical Algebra, Control & Optimization, 2018, 8 (2) : 135-155. doi: 10.3934/naco.2018008  Andrej V. Plotnikov, Tatyana A. Komleva, Liliya I. Plotnikova. The averaging of fuzzy hyperbolic differential inclusions. Discrete & Continuous Dynamical Systems - B, 2017, 22 (5) : 1987-1998. doi: 10.3934/dcdsb.2017117  Wei Wang, Xiao-Long Xin. On fuzzy filters of Heyting-algebras. Discrete & Continuous Dynamical Systems - S, 2011, 4 (6) : 1611-1619. doi: 10.3934/dcdss.2011.4.1611  Cuilian You, Yangyang Hao. Stability in mean for fuzzy differential equation. Journal of Industrial & Management Optimization, 2019, 15 (3) : 1375-1385. doi: 10.3934/jimo.2018099

2018 Impact Factor: 1.025
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