Dr. Harish  Garg

Dr. Harish Garg

Assistant Professor
Thapar Institute of Engineering & Technology, Deemed University, Patiala, Punjab,India


Highest Degree
Ph.D. in Mathematics from Indian Institute of Technology Roorkee, India

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Biography

Dr. Harish Garg is working with the School of Mathematics at Thapar Institute of Engineering & Technology (Deemed University) Patiala, Punjab, India.

He has authored 250+ papers published in refereed International Journals including Information Sciences, IEEE TRANSACTIONS ON FUZZY SYSTEMS, International Journal of Intelligent Systems, IEEE/CAA JOURNAL OF AUTOMATIC SINICA, IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, Cognitive Computation, Artificial Intelligence Review, Applied Soft Computing, Experts Systems with Applications, IEEE ACCESS, Journal of Intelligent and Fuzzy Systems, Expert Systems, Journal of Manufacturing Systems, Applied Mathematics & Computations, ISA Transactions, Applied Intelligence, Computer and Industrial Engineering, Soft Computing, Computer and Operations Research, Journal of Experimental & Theoretical Artificial Intelligence, International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, Journal of Industrial and Management Optimization, International Journal of Uncertainty Quantification, and many more. His research interests are in the fields of Computational Intelligence, Multi-criteria decision making, fuzzy decision making, Pythagorean fuzzy sets, Computing with words and Soft Computing.

Dr. Garg is the Editor-in-Chief of Annals of Optimization Theory & Practice. He is also Associate Editors for the Journal of Intelligent & Fuzzy Systems, International Journal of Computational Intelligence Systems, Kybernetes, Technological and Economic Development of Economy. Mathematical Problems in Engineering, Journal of Industrial & Management Optimization, Complexity, Complex and Intelligent Systems, CAAI Transactions on Intelligence Technology and so on. His Google citations are over 8830+. For more details, visit http://sites.google.com/site/harishg58iitr/

Area of Interest:

Mathematics
100%
Mathematics and Statistics
62%
Operations Research
90%
Applied Mathematics
75%
Fuzzy Logic
55%

Selected Publications

  1. Yang, Z., H. Garg, J. Li, G. Srivastava and Z. Cao, 2020. Investigation of multiple heterogeneous relationships using a q-rung orthopair fuzzy multi-criteria decision algorithm. Neural Comput. Appl., 10.1007/s00521-020-05003-5.
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  2. Xue, Y., Y. Deng and H. Garg, 2020. Uncertain database retrieval with measure – Based belief function attribute values under intuitionistic fuzzy set. Inf. Sci., 546: 436-447.
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  3. Wang, L., H. Garg and N. Li, 2020. Pythagorean fuzzy interactive Hamacher power aggregation operators for assessment of express service quality with entropy weight. Soft Comput., 10.1007/s00500-020-05193-z.
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  4. Niroomand, S., H. Garg and A. Mahmoodirad, 2020. An intuitionistic fuzzy two stage supply chain network design problem with multi-mode demand and multi-mode transportation. ISA Trans., 10.1016/j.isatra.2020.07.033.
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  5. Krishankumar, R., K.S. Ravichandran, V. Shyam, S.V. Sneha, S. Kar and H. Garg, 2020. Multi-attribute group decision-making using double hierarchy hesitant fuzzy linguistic preference information. Neural Comput. Appl., 32: 14031-14045.
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  6. Jin, F., H. Garg, L. Pei, J. Liu and H. Chen, 2020. Multiplicative consistency adjustment model and data envelopment analysis-driven decision-making process with probabilistic hesitant fuzzy preference relations. Int. J. Fuzzy Syst., 22: 2319-2332.
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  7. Garg, H., 2020. Linguistic interval-valued pythagorean fuzzy sets and their application to multiple attribute group decision-making process. Cogn. Comput., 10.1007/s12559-020-09750-4.
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  8. Garg, H., 2020. A novel trigonometric operation-based q-rung orthopair fuzzy aggregation operator and its fundamental properties. Neural Comput. Appl., 32: 15077-15099.
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  9. Garg, H. and D. Rani, 2020. Multi-criteria decision making method based on Bonferroni mean aggregation operators of complex intuitionistic fuzzy numbers. Am. Inst. Math. Sci., 10.3934/jimo.2020069.
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  10. Garg H., Z. Ali and T. Mahmood, 2020. Algorithms for complex interval‐valued q‐rung orthopair fuzzy sets in decision making based on aggregation operators, ahp, and topsis. Expert Systems 10.1111/exsy.12609 http://dx.doi.org/10.1111/exsy.12609.
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  11. Garai, T., S. Dalapati, H. Garg and T.K. Roy, 2020. Possibility mean, variance and standard deviation of single-valued neutrosophic numbers and its applications to multi-attribute decision-making problems. Soft Comput., 10.1007/s00500-020-05112-2.
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  12. Garai, T., H. Garg and T.K. Roy, 2020. A ranking method based on possibility mean for multi-attribute decision making with single valued neutrosophic numbers. J. Ambient Intell. Human Comput., 10.1007/s12652-020-01853-y.
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  13. Wang, L., H. Garg and N. Li, 2019. Interval-valued q-rung orthopair 2-tuple linguistic aggregation operators and their applications to decision making process. IEEE Access, 7: 131962-131977.
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  14. Rani, D. and H. Garg, 2019. Some modified results of the subtraction and division operations on interval neutrosophic sets. J. Exp. Theor. Artif. Intell., 31: 677-698.
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  15. Peng, X. and H. Garg, 2019. Multiparametric similarity measures on pythagorean fuzzy sets with applications to pattern recognition. Applied Intell., (In Press). 10.1007/s10489-019-01445-0.
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  16. Nguyen, X.T., V.D. Nguyen, V.H. Nguyen and H. Garg, 2019. Exponential similarity measures for Pythagorean fuzzy sets and their applications to pattern recognition and decision-making process. Complex Intell. Syst., 5: 217-228.
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  17. Nancy and H. Garg, 2019. A novel divergence measure and its based TOPSIS method for multi criteria decision-making under single-valued neutrosophic environment. J. Intell. Fuzzy Syst., 36: 101-115.
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  18. Kifayat Ullah, H. Garg, T. Mahmood, N. Jan and Z. Ali, 2019. Correlation coefficients for T-spherical fuzzy sets and their applications in clustering and multi-attribute decision making. Soft Comput., (In Press). 10.1007/s00500-019-03993-6.
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  19. Kaur, G. and H. Garg, 2019. Generalized cubic intuitionistic fuzzy aggregation operators using t-norm operations and their applications to group decision-making process. Arabian J. Sci. Eng., 44: 2775-2794.
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  20. Jan, N., K. Ullah, T. Mahmood, H. Garg, B. Davvaz, A.B. Saeid and S. Broumi, 2019. Some root level modifications in interval valued fuzzy graphs and their generalizations including neutrosophic graphs. Mathematics, Vol. 7, No. 1. 10.3390/math7010072.
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  21. Garg, H., 2019. Novel neutrality operation-based Pythagorean fuzzy geometric aggregation operators for multiple attribute group decision analysis. Int. J. Intell. Syst., 34: 2459-2489.
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  22. Garg, H., 2019. New logarithmic operational laws and their aggregation operators for Pythagorean fuzzy set and their applications. Int. J. Intell. Syst., 34: 82-106.
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  23. Garg, H., 2019. Neutrality operations-based Pythagorean fuzzy aggregation operators and its applications to multiple attribute group decision-making process. J. Ambient Intell. Humanized Comput., (In Press). 10.1007/s12652-019-01448-2.
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  24. Garg, H., 2019. Intuitionistic fuzzy hamacher aggregation operators with entropy weight and their applications to multi-criteria decision-making problems. Iran. J. Sci. Technol. Trans. Electr. Eng., 43: 597-613.
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  25. Garg, H., 2019. Hesitant pythagorean fuzzy maclaurin symmetric mean operators and its applications to multiattribute decision-making process. Int. J. Intell. Syst., 34: 601-626.
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  26. Garg, H., 2019. Generalized intuitionistic fuzzy entropy-based approach for solving multi-attribute decision-making problems with unknown attribute weights. Proc. Natl. Acad. Sci. India Sect. A: Phys. Sci., 89: 129-139.
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  27. Garg, H., 2019. A hybrid GSA-GA algorithm for constrained optimization problems. Inform. Sci., 478: 499-523.
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  28. Garg, H. and R. Arora, 2019. Generalized intuitionistic fuzzy soft power aggregation operator based on t‐norm and their application in multicriteria decision‐making. Int. J. Intell. Syst., 34: 215-246.
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  29. Garg, H. and R. Arora, 2019. Maclaurin symmetric mean aggregation operators based on t-norm operations for the dual hesitant fuzzy soft set. J. Ambient Intell. Humanized Comput., (In Press). 10.1007/s12652-019-01238-w.
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  30. Garg, H. and Nancy, 2019. Multiple criteria decision making based on frank Choquet Heronian mean operator for single-valued neutrosophic sets. Applied Comput. Math., 18: 163-188.
  31. Garg, H. and Nancy, 2019. Linguistic single-valued neutrosophic power aggregation operators and their applications to group decision-making problems. IEEE/CAA J. Automatica Sin., (In Press). 10.1109/JAS.2019.1911522.
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  32. Garg, H. and Nancy, 2019. Algorithms for possibility linguistic single-valued neutrosophic decision-making based on COPRAS and aggregation operators with new information measures. Measurement, 138: 278-290.
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  33. Garg, H. and K. Kumar, 2019. Prioritized aggregation operators based on linguistic connection number for multiple attribute group decision-making under linguistic intuitionistic fuzzy environment. ICSES Trans. Neural Fuzzy Comput. .
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  34. Garg, H. and K. Kumar, 2019. Power geometric aggregation operators based on connection number of set pair analysis under intuitionistic fuzzy environment. Arabian J. Sci. Eng., (In Press). 10.1007/s13369-019-03961-0.
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  35. Garg, H. and K. Kumar, 2019. Multiattribute decision making based on power operators for linguistic intuitionistic fuzzy set using set pair analysis. Expert Syst., Vol. 36. 10.1111/exsy.12428.
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  36. Garg, H. and K. Kumar, 2019. Linguistic interval-valued Atanassov intuitionistic fuzzy sets and their applications to group decision-making problems. IEEE Trans. Fuzzy Syst., (In Press). 10.1109/TFUZZ.2019.2897961.
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  37. Garg, H. and K. Kumar, 2019. Improved possibility degree method for ranking intuitionistic fuzzy numbers and their application in multiattribute decision-making. Granular Comput., 4: 237-247.
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  38. Garg, H. and K. Kumar, 2019. An extended technique for order preference by similarity to ideal solution group decision-making method with linguistic interval-valued intuitionistic fuzzy information. J. Multi-Criteria Decis. Anal., 26: 16-26.
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  39. Garg, H. and K. Kumar, 2019. An advanced study on operations of connection number based on set pair analysis. Natl. Acad. Sci. Lett., 42: 351-354.
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  40. Garg, H. and K. Kumar, 2019. A novel possibility measure to interval-valued intuitionistic fuzzy set using connection number of set pair analysis and its applications. Neural Comput. Applic., (In Press). 10.1007/s00521-019-04291-w.
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  41. Garg, H. and G. Kaur, 2019. TOPSIS based on nonlinear-programming methodology for solving decision-making problems under cubic intuitionistic fuzzy set environment. Comput. Applied Math., Vol. 38. 10.1007/s40314-019-0869-6.
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  42. Garg, H. and G. Kaur, 2019. A robust correlation coefficient for probabilistic dual hesitant fuzzy sets and its applications. Neural Comput. Applic., (In Press). 10.1007/s00521-019-04362-y.
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  43. Garg, H. and D. Rani, 2019. Some results on information measures for complex intuitionistic fuzzy sets. Int. J. Intell. Syst., 34: 2319-2363.
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  44. Garg, H. and D. Rani, 2019. Some generalized complex intuitionistic fuzzy aggregation operators and their application to multicriteria decision-making process. Arabian J. Sci. Eng., 44: 2679-2698.
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  45. Garg, H. and D. Rani, 2019. Robust averaging-geometric aggregation operators for complex intuitionistic fuzzy sets and their applications to MCDM process. Arabian J. Sci. Eng., (In Press). 10.1007/s13369-019-03925-4.
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  46. Garg, H. and D. Rani, 2019. New generalised Bonferroni mean aggregation operators of complex intuitionistic fuzzy information based on Archimedean t-norm and t-conorm. J. Exp. Theor. Artif. Intell., (In Press). 10.1080/0952813X.2019.1620871.
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  47. Garg, H. and D. Rani, 2019. Exponential, logarithmic and compensative generalized aggregation operators under complex intuitionistic fuzzy environment. Group Decision Negotiation, 28: 991-1050.
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  48. Garg, H. and D. Rani, 2019. Complex interval-valued intuitionistic fuzzy sets and their aggregation operators. Fundamenta Informaticae, 164: 61-101.
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  49. Garg, H. and D. Rani, 2019. A robust correlation coefficient measure of complex intuitionistic fuzzy sets and their applications in decision-making. Applied Intell., 49: 496-512.
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  50. Garg H. and D. Rani, 2019. Novel exponential divergence measure of complex intuitionistic fuzzy sets with an application to decision-making process. Scientia Iranica 10.24200/SCI.2019.53194.3103 http://dx.doi.org/10.24200/sci.2019.53194.3103.
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  51. Garai, T. and H. Garg, 2019. Multi-objective linear fractional inventory model with possibility and necessity constraints under generalised intuitionistic fuzzy set environment. CAAI Trans. Intell. Technol., (In Press). 10.1049/trit.2019.0030.
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  52. Ebrahimnejad, A., J.L. Verdegay and H. Garg, 2019. Signed distance ranking based approach for solving bounded interval‐valued fuzzy numbers linear programming problems. Int. J. Intell. Syst., 34: 2055-2076.
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  53. Deepak, D., B. Mathew, S.J. John and H. Garg, 2019. A topological structure involving Hesitant fuzzy sets. J. Intell. Fuzzy Syst., 36: 6401-6412.
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  54. Athira, T.M., S.J. John and H. Garg, 2019. Entropy and distance measures of Pythagorean fuzzy soft sets and their applications. J. Intell. Fuzzy Syst., 36: 6401-6412.
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  55. Arora, R. and H. Garg, 2019. Group decision-making method based on prioritized linguistic intuitionistic fuzzy aggregation operators and its fundamental properties. Comput. Applied Math., Vol. 38, No. 2. 10.1007/s40314-019-0764-1.
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  56. Akram, M., F. Illyas and H. Garg, 2019. Multi-criteria group decision making based on ELECTRE I method in pythagorean fuzzy information. Soft Comput., (In Press). 10.1007/s00500-019-04105-0.
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  57. Wei, G., H. Garg, H. Gao and C. Wei, 2018. Interval-valued Pythagorean fuzzy Maclaurin symmetric mean operators in multiple attribute decision making. IEEE Access, 6: 67866-67884.
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  58. Singh, S. and H. Garg, 2018. Symmetric triangular interval type-2 intuitionistic fuzzy sets with their applications in multi criteria decision making. Symmetry, Vol. 10, No. 9. 10.3390/sym10090401.
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  59. Shah, H., N. Tairan, H. Garg and R. Ghazali, 2018. Global Gbest guided-artificial bee colony algorithm for numerical function optimization. Computers, Vol. 7, No. 4. 10.3390/computers7040069.
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  60. Shah, H., N. Tairan, H. Garg and R. Ghazali, 2018. A quick gbest guided artificial bee colony algorithm for stock market prices prediction. Symmetry, Vol. 10, No. 7. 10.3390/sym10070292.
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  61. Selvachandran, G., H. Garg, M.H. Alaroud and A.R. Salleh, 2018. Similarity measure of complex vague soft sets and its application to pattern recognition. Int. J. Fuzzy Syst., 20: 1901-1914.
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  62. Selvachandran, G., H. Garg and S. Quek, 2018. Vague entropy measure for complex vague soft sets. Entropy, Vol. 20, No. 6. 10.3390/e20060403.
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  63. Rani, D. and H. Garg, 2018. Complex intuitionistic fuzzy power aggregation operators and their applications in multicriteria decision-making. Expert Syst., Vol. 35, No. 6. 10.1111/exsy.12325.
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  64. Peng, X., J. Dai and H. Garg, 2018. Exponential operation and aggregation operator for q-rung orthopair fuzzy set and their decision-making method with a new score function. Int. J. Intell. Syst., 33: 2255-2282.
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  65. Peng, X. and H. Garg, 2018. Algorithms for interval-valued fuzzy soft sets in emergency decision making based on WDBA and CODAS with new information measure. Comput. Ind. Eng., 119: 439-452.
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  66. Patwal, R.S., N. Narang and H. Garg, 2018. A novel TVAC-PSO based mutation strategies algorithm for generation scheduling of pumped storage hydrothermal system incorporating solar units. Energy, 142: 822-837.
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  67. Niwas, R. and H. Garg, 2018. An approach for analyzing the reliability and profit of an industrial system based on the cost free warranty policy. J. Braz. Soc. Mech. Sci. Eng., Vol. 40, No. 5. 10.1007/s40430-018-1167-8.
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  68. Kumar, K. and H. Garg, 2018. TOPSIS method based on the connection number of set pair analysis under interval-valued intuitionistic fuzzy set environment. Comput. Applied Math., 37: 1319-1329.
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  69. Kumar, K. and H. Garg, 2018. Prioritized linguistic interval-valued aggregation operators and their applications in group decision-making problems. Mathematics, Vol. 6, No. 10. 10.3390/math6100209.
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  70. Kumar, K. and H. Garg, 2018. Connection number of set pair analysis based TOPSIS method on intuitionistic fuzzy sets and their application to decision making. Applied Intell., 48: 2112-2119.
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  71. Kaur, G. and H. Garg, 2018. Multi-attribute decision-making based on bonferroni mean operators under cubic intuitionistic fuzzy set environment. Entropy, Vol. 20. 10.3390/e20010065.
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  72. Kaur, G. and H. Garg, 2018. Cubic intuitionistic fuzzy aggregation operators. Int. J. Uncertainty Quantif., 8: 405-427.
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  73. Jamkhaneh, E.B. and H. Garg, 2018. Some new operations over the generalized intuitionistic fuzzy sets and their application to decision-making process. Granular Comput., 3: 111-122.
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  74. Garg, H., M. Munir, K. Ullah, T. Mahmood and N. Jan, 2018. Algorithm for T-spherical fuzzy multi-attribute decision making based on improved interactive aggregation operators. Symmetry, Vol. 10, No. 12. 10.3390/sym10120670.
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  75. Garg, H., 2018. Some robust improved geometric aggregation operators under interval-valued intuitionistic fuzzy environment for multi-criteria decision-making process. J. Ind. Manage. Optimiz., 14: 283-308.
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  76. Garg, H., 2018. Some methods for strategic decision‐making problems with immediate probabilities in Pythagorean fuzzy environment. Int. J. Intelli. Syst., 33: 687-712.
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  77. Garg, H., 2018. Some hybrid weighted aggregation operators under neutrosophic set environment and their applications to multicriteria decision-making. Applied Intell., 48: 4871-4888.
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  78. Garg, H., 2018. Some arithmetic operations on the generalized sigmoidal fuzzy numbers and its application. Granular Comput., 3: 9-25.
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  79. Garg, H., 2018. Novel correlation coefficients under the intuitionistic multiplicative environment and their applications to decision-making process. J. Ind. Manage. Optimiz., 14: 1501-1519.
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  80. Garg, H., 2018. Non-linear programming method for multi-criteria decision making problems under interval neutrosophic set environment. Applied Intell., 48: 2199-2213.
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  81. Garg, H., 2018. New exponential operational laws and their aggregation operators for interval-valued Pythagorean fuzzy multicriteria decision-making. Int. J. Intell. Syst., 33: 653-683.
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  82. Garg, H., 2018. Multi-objective non-linear programming problem for reliability optimization in intuitionistic fuzzy environment. Math. Applied Inform. Syst., 2: 197-229.
  83. Garg, H., 2018. Linguistic Pythagorean fuzzy sets and its applications in multiattribute decision-making process. Int. J. Intell. Syst., 33: 1234-1263.
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  84. Garg, H., 2018. Hesitant Pythagorean fuzzy sets and their aggregation operators in multiple attribute decision-making. Int. J. Uncertainty Quantif., 8: 267-289.
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  85. Garg, H., 2018. Generalized interaction aggregation operators in intuitionistic fuzzy multiplicative preference environment and their application to multicriteria decision-making. Appl. Intell., 48: 2120-2136.
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  86. Garg, H., 2018. Generalised Pythagorean fuzzy geometric interactive aggregation operators using Einstein operations and their application to decision making. J. Exp. Theoret. Artif. Intell., 30: 763-794.
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  87. Garg, H., 2018. Arithmetic operations on generalized parabolic fuzzy numbers and its application. Proc. Natl. Acad. Sci. India Sect. A: Phys. Sci., 88: 15-26.
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  88. Garg, H., 2018. Analysis of an industrial system under uncertain environment by using different types of fuzzy numbers. Int. J. Syst. Assur. Eng. Manage., 9: 525-538.
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  89. Garg, H., 2018. An improved cosine similarity measure for intuitionistic fuzzy sets and their applications to decision-making process. Hacettepe J. Math. Statist., 47: 1578-1594.
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  90. Garg, H., 2018. A linear programming method based on an improved score function for interval-valued Pythagorean fuzzy numbers and its application to decision-making. Int. J. Uncert. Fuzziness Knowledge-Based Syst., 26: 67-80.
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  91. Garg, H. and S. Singh, 2018. A novel triangular interval type-2 intuitionistic fuzzy sets and their aggregation operators. Iran. J. Fuzzy Syst., 15: 69-93.
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  92. Garg, H. and R. Arora, 2018. Novel scaled prioritized intuitionistic fuzzy soft interaction averaging aggregation operators and their application to multi criteria decision making. Eng. Applic. Artif. Intell., 71: 100-112.
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  93. Garg, H. and R. Arora, 2018. Generalized and group-based generalized intuitionistic fuzzy soft sets with applications in decision-making. Applied Intelli., 48: 343-356.
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  94. Garg, H. and R. Arora, 2018. Dual hesitant fuzzy soft aggregation operators and their application in decision-making. Cognit. Comput., 10: 769-789.
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  95. Garg, H. and R. Arora, 2018. Bonferroni mean aggregation operators under intuitionistic fuzzy soft set environment and their applications to decision-making. J. Operat. Res. Soc., 69: 1711-1724.
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  96. Garg, H. and R. Arora, 2018. A nonlinear-programming methodology for multi-attribute decision-making problem with interval-valued intuitionistic fuzzy soft sets information. Applied Intell., 48: 2031-2046.
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  97. Garg, H. and Nancy, 2018. New logarithmic operational laws and their applications to multiattribute decision making for single-valued neutrosophic numbers. Cognit. Syst. Res., 52: 931-946.
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  98. Garg, H. and Nancy, 2018. Multi-criteria decision-making method based on prioritized Muirhead mean aggregation operator under neutrosophic set environment. Symmetry, Vol. 10, No. 7. 10.3390/sym10070280.
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  99. Garg, H. and Nancy, 2018. Linguistic single-valued neutrosophic prioritized aggregation operators and their applications to multiple-attribute group decision-making. J. Ambient Intell. Humanized Comput., 9: 1975-1997.
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  100. Garg, H. and K. Kumar, 2018. Some aggregation operators for linguistic intuitionistic fuzzy set and its application to group decision-making process using the set pair analysis. Arabian J. Sci. Eng., 43: 3213-3227.
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  101. Garg, H. and K. Kumar, 2018. Group decision making approach based on possibility degree measures and the linguistic intuitionistic fuzzy aggregation operators using Einstein norm operations. J. Multiple-Valued Logic Soft Comput., 31: 175-209.
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  102. Garg, H. and K. Kumar, 2018. Group decision making approach based on possibility degree measure under linguistic interval-valued intuitionistic fuzzy set environment. J. Ind. Manage. Optimiz. 10.3934/jimo.2018162.
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  103. Garg, H. and K. Kumar, 2018. Distance measures for connection number sets based on set pair analysis and its applications to decision-making process. Applied Intell., 48: 3346-3359.
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  104. Garg, H. and K. Kumar, 2018. An advanced study on the similarity measures of intuitionistic fuzzy sets based on the set pair analysis theory and their application in decision making. Soft Comput., 22: 4959-4970.
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  105. Garg, H. and K. Kumar, 2018. A novel exponential distance and its based TOPSIS method for interval-valued intuitionistic fuzzy sets using connection number of SPA theory. Artif. Intell. Rev. 10.1007/s10462-018-9668-5.
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  106. Garg, H. and K. Kumar, 2018. A novel correlation coefficient of intuitionistic fuzzy sets based on the connection number of set pair analysis and its application. Sci. Iran. Trans. E: Ind. Eng., 25: 2373-2388.
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  107. Garg, H. and J. Kaur, 2018. A novel (R,S)-norm entropy measure of intuitionistic fuzzy sets and its applications in multi-attribute decision-making. Mathematics, Vol. 6, No. 6. 10.3390/math6060092.
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  108. Garg, H. and G. Kaur, 2018. Novel distance measures for cubic intuitionistic fuzzy sets and their applications to pattern recognitions and medical diagnosis. Granular Comput. 10.1007/s41066-018-0140-3.
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  109. Garg, H. and G. Kaur, 2018. Extended TOPSIS method for multi-criteria group decision-making problems under cubic intuitionistic fuzzy environment. Sci. Iranica. 10.24200/SCI.2018.5307.1194.
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  110. Garg, H. and G. Kaur, 2018. Algorithm for probabilistic dual hesitant fuzzy multi-criteria decision-making based on aggregation operators with new distance measures. Mathematics, Vol. 6, No. 12. 10.3390/math6120280.
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  111. Arora, R. and H. Garg, 2018. Prioritized averaging/geometric aggregation operators under the intuitionistic fuzzy soft set environment. Scient. Iran. Trans. E, Indust. Eng., 25: 466-482.
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  112. Arora, R. and H. Garg, 2018. A robust correlation coefficient measure of dual hesitant fuzzy soft sets and their application in decision making. Eng. Applic. Artif. Intell., 72: 80-92.
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  113. Arora, R. and H. Garg, 2018. A robust aggregation operators for multi-criteria decision-making with intuitionistic fuzzy soft set environment. Sci. Iran. Trans. E: Ind. Eng., 25: 931-942.
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  114. Singh, S. and H. Garg, 2017. Distance measures between type-2 intuitionistic fuzzy sets and their application to multicriteria decision-making process. Applied Intelli., 46: 788-799.
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  115. Rani, D. and H. Garg, 2017. Distance measures between the complex intuitionistic fuzzy sets and their applications to the decision-making process. Int. J. Uncert. Quantif., 7: 423-439.
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  116. Puri, J., S.P. Yadav and H. Garg, 2017. A new multi-component DEA approach using common set of weights methodology and imprecise data: An application to public sector banks in India with undesirable and shared resources. Ann. Operat. Res., 259: 351-388.
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  117. Garg, H., N. Agarwal and A. Tripathi, 2017. Some improved interactive aggregation operators under interval-valued intuitionistic fuzzy environment and their application to decision making process. Scient. Iran. Trans. E, Indust. Eng., 24: 2581-2604.
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  118. Garg, H., N. Agarwal and A. Tripathi, 2017. Generalized intuitionistic fuzzy entropy measure of order α and degree β and its applications to multi-criteria decision making problem. Int. J. Fuzzy Syst. Applic., 6: 86-107.
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  119. Garg, H., N. Agarwal and A. Tripathi, 2017. Choquet integral-based information aggregation operators under the interval-valued intuitionistic fuzzy set and its applications to decision-making process. Int. J. Uncert. Quantif., 7: 249-269.
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  120. Garg, H., N. Agarwal and A. Tripathi, 2017. A new generalized directed divergence measure for Intuitionistic fuzzy sets and applications in expert systems. Ann. Fuzzy Math. Inform., 13: 699-723.
  121. Garg, H., 2017. Some picture fuzzy aggregation operators and their applications to multicriteria decision-making. Arabian J. Sci. Eng., 42: 5275-5290.
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  122. Garg, H., 2017. Some new biparametric distance measures on single-valued neutrosophic sets with applications to pattern recognition and medical diagnosis. Information, Vol. 8. 10.3390/info8040162.
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  123. Garg, H., 2017. Performance analysis of an industrial system using soft computing based hybridized technique. J. Brazil. Soc. Mech. Sci. Eng., 39: 1441-1451.
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  124. Garg, H., 2017. Novel intuitionistic fuzzy decision making method based on an improved operation laws and its application. Eng. Applic. Artif. Intelli., 60: 164-174.
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  125. Garg, H., 2017. Generalized Pythagorean fuzzy geometric aggregation operators using Einstein t‐norm and t‐conorm for multicriteria decision‐making process. Int. J. Intelli. Syst., 32: 597-630.
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  126. Garg, H., 2017. Distance and similarity measures for intuitionistic multiplicative preference relation and its applications. Int. J. Uncert. Quantif., 7: 117-133.
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  127. Garg, H., 2017. Confidence levels based Pythagorean fuzzy aggregation operators and its application to decision-making process. Comput. Math. Org. Theory, 23: 546-571.
  128. Garg, H., 2017. A robust ranking method for intuitionistic multiplicative sets under crisp, interval environments and its applications. IEEE Trans. Emerg. Topics Comput. Intelli., 1: 366-374.
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  129. Garg, H., 2017. A novel improved accuracy function for interval valued Pythagorean fuzzy sets and its applications in the decision‐making process. Int. J. Intelli. Syst., 32: 1247-1260.
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  130. Garg, H., 2017. A new improved score function of an interval-valued Pythagorean fuzzy set based TOPSIS method. Int. J. Uncert. Quantif., 7: 463-474.
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  131. Garg, H. and R. Arora, 2017. Distance and similarity measures for dual hesitant fuzzy soft sets and their applications in multicriteria decision making problem. Int. J. Uncert. Quantif., 7: 229-248.
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  132. Rani, D., T.R. Gulati and H. Garg, 2016. Multi-objective non-linear programming problem in intuitionistic fuzzy environment: Optimistic and pessimistic view point. Exp. Syst. Applic., 64: 228-238.
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  133. Nancy and H. Garg, 2016. Novel single-valued neutrosophic decision making operators under Frank norm operations and its application. Int. J. Uncertainty Quantif., 6: 361-375.
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  134. Nancy and H. Garg, 2016. An improved score function for ranking neutrosophic sets and its application to decision-making process. Int. J. Uncertainty Quantif., 6: 377-385.
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  135. Garg, H., N. Agarwal and A. Tripathi, 2016. Fuzzy number intuitionistic fuzzy soft sets and its properties. J. Fuzzy Set Valued Anal., 3: 196-213.
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  136. Garg, H., 2016. Some series of intuitionistic fuzzy interactive averaging aggregation operators. SpringerPlus, Vol. 5. 10.1186/s40064-016-2591-9.
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  137. Garg, H., 2016. Generalized intuitionistic fuzzy multiplicative interactive geometric operators and their application to multiple criteria decision making. Int. J. Mach. Learn. Cybernet., 7: 1075-1092.
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  138. Garg, H., 2016. Generalized intuitionistic fuzzy interactive geometric interaction operators using Einstein t-norm and t-conorm and their application to decision making. Comput. Indust. Eng., 10: 53-69.
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  139. Garg, H., 2016. Bi-criteria Optimization for Finding the Optimal Replacement Interval for Maintaining the Performance of the Process Industries. In: Modern Optimization Algorithms and Applications in Engineering and Economics, Vasant, P., W. Weber and V.N. Dieu (Eds.)., Chapter 25, IGI Global Publisher, USA., ISBN-13: 9781466696440, pp: 643-675.
  140. Garg, H., 2016. An Integrated Framework to Analyze the Performance of Process Industrial Systems Using a Fuzzy and Evolutionary Algorithm. In: Intelligent Decision Making in Quality Management, Kahraman, C. and S. Yanik (Eds.). Springer, Cham, ISBN: 978-3-319-24497-6, pp: 141-177.
  141. Garg, H., 2016. A novel correlation coefficients between Pythagorean fuzzy sets and its applications to decision‐making processes. Int. J. Intelli. Syst., 31: 1234-1252.
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  142. Garg, H., 2016. A novel approach for analyzing the reliability of series-parallel system using credibility theory and different types of intuitionistic fuzzy numbers. J. Brazil. Soc. Mech. Sci. Eng., 38: 1021-1035.
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  143. Garg, H., 2016. A novel accuracy function under interval-valued Pythagorean fuzzy environment for solving multicriteria decision making problem. J. Int. Fuzzy Syst., 31: 529-540.
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  144. Garg, H., 2016. A new generalized improved score function of interval-valued intuitionistic fuzzy sets and applications in expert systems. Applied Soft Comput., 38: 988-999.
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  145. Garg, H., 2016. A new generalized Pythagorean fuzzy information aggregation using Einstein operations and its application to decision making. Int. J. Intelli. Syst., 31: 886-920.
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  146. Garg, H., 2016. A new approach for solving fuzzy differential equations using Runga-Kutta and biogeography-based optimization. J. Intelli. Fuzzy Syst., 30: 2417-2429.
  147. Garg, H., 2016. A hybrid PSO-GA algorithm for constrained optimization problems. Applied Math. Comput., 274: 292-305.
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  148. Garg, H. and N. Garg, 2016. On single-valued neutrosophic entropy of order α. Neutrosophic Sets Syst., 14: 21-28.
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  149. Garg, H., N. Agarwal and A. Tripathi, 2015. Entropy based multi-criteria decision making method under fuzzy environment and unknown attribute weights. Global J. Technol. Optimiz., Vol. 6. 10.4172/2229-8711.1000182.
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  150. Garg, H., 2015. Predicting uncertain behavior in critical engineering systems under vague environment. J. Multiple-Valued Logic Soft Comput., 25: 1-21.
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  151. Garg, H., 2015. Multi-objective optimization problem of system reliability under intuitionistic fuzzy set environment using Cuckoo search algorithm. J. Intelli. Fuzzy Syst., 29: 1653-1669.
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  152. Garg, H., 2015. Fuzzy Inventory Models for Deteriorating Items under Different Types of Lead-Time Distributions. In: Intelligent Techniques in Engineering Management, Kahraman, C. and S.C. Onar (Eds.)., Springer, Cham., ISBN: 978-3-319-17905-6, pp: 247-274.
  153. Garg, H., 2015. An efficient biogeography based optimization algorithm for solving reliability optimization problems. Swarm Evol. Comput., 24: 1-10.
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  154. Garg, H., 2015. An approach for solving constrained reliability-redundancy allocation problems using cuckoo search algorithm. Beni-Suef Univ. J. Basic Applied Sci., 4: 14-25.
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  155. Garg, H., 2015. An approach for analyzing the reliability of industrial system using fuzzy Kolmogorov's differential equations. Arabian J. Sci. Eng., 40: 975-987.
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  156. Rani, M., H. Garg and S.P. Sharma, 2014. Cost minimization of butter-oil processing plant using artificial bee colony technique. Math. Comput. Simul., 97: 94-107.
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  157. Garg, H., M. Rani, S.P. Sharma and Y. Vishwakarma, 2014. Intuitionistic fuzzy optimization technique for solving multi-objective reliability optimization problems in interval environment. Expert Syst. Applic., 41: 3157-3167.
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  158. Garg, H., M. Rani, S.P. Sharma and Y. Vishwakarma, 2014. Bi-objective optimization of the reliability-redundancy allocation problem for series-parallel system. J. Manufacturing Syst., 33: 335-347.
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  159. Garg, H., M. Rani and S.P. Sharma, 2014. Performance analysis of repairable industrial systems using artificial bee colony and fuzzy methodology. Int. J. Artif. Intell. Tools, Vol. 23. 10.1142/S0218213014500080.
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  160. Garg, H., M. Rani and S.P. Sharma, 2014. An approach for analyzing the reliability of industrial systems using soft-computing based technique. Expert Syst. Applic., 41: 489-501.
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  161. Garg, H., 2014. Solving structural engineering design optimization problems using an artificial bee colony algorithm. J. Ind. Manage. Optim., 10: 777-794.
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  162. Garg, H., 2014. Reliability, availability and maintainability analysis of industrial systems using PSO and fuzzy methodology. MAPAN-J. Metrol. Soc. India, 29: 115-129.
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  163. Garg, H., 2014. Performance and behavior analysis of repairable industrial systems using Vague Lambda-Tau methodology. Applied Soft Comput., 22: 323-338.
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  164. Garg, H., 2014. Modeling and Analyzing System Failure Behavior for Reliab Ility Analysis using Soft Computing Based Techniques. In: Quality and Reliability Management and Its Applications, Pham H. (Ed.). Springer, Germany.
  165. Garg, H., 2014. Integrated Framew ork to Analyze the Performance of Process Industrial Systems using Fuzzy and Evolutionary Algorithm. In: Intelligent Decision Making in Quality Management, Kahraman C. and S. Ugurlu (Eds.). Springer, Germany.
  166. Garg, H., 2014. Analyzing the behavior of an industrial system using fuzzy confidence interval based methodology. Natl. Acad. Sci. Lett., 37: 359-370.
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  167. Garg, H., 2014. A novel approach for analyzing the behavior of industrial systems using weakest t-norm and intuitionistic fuzzy set theory. ISA Trans., 53: 1199-1208.
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  168. Garg, H., 2014. A Hybrid GA-GSA Algorithm for Optimizing the Performance of an Industrial System by Utilizing Uncertain Data. In: Handbook of Research on Artif icial Intelligence Techniques and Algorithms, Vasant, P. (Ed.). IGI Global, USA., pp: 625-659.
  169. Rani, M., S.P. Sharma and H. Garg, 2013. A novel approach for analyzing the behavior of repairable systems by utilizing uncertain data. Int. J. Performability Eng., 9: 201-210.
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  170. Garg, H., S.P. Sharma and M. Rani, 2013. Weibull fuzzy probability distribution for analysing the behaviour of pulping unit in a paper industry. Int. J. Ind. Syst. Eng., 14: 395-413.
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  171. Garg, H., M. Rani and S.P. Sharma, 2013. Reliability analysis of the engineering systems using Intuitionistic fuzzy set theory. J. Qual. Reliab. Eng. 10.1155/2013/943972.
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  172. Garg, H., M. Rani and S.P. Sharma, 2013. Preventive maintenance scheduling of the pulping unit in a paper plant. Jpn. J. Ind. Applied Math., 30: 397-414.
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  173. Garg, H., M. Rani and S.P. Sharma, 2013. Predicting uncertain behavior of press unit in a paper industry using artificial bee colony and fuzzy Lambda-Tau. Applied Soft Comput., 13: 1869-1881.
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  174. Garg, H., M. Rani and S.P. Sharma, 2013. Predicting Uncertain Behavior and Performance Analysis of the Pulping System in a Paper Industry using PSO and Fuzzy Methodology. In: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications, Vasant, P.M. (Ed.). IGI Global, USA., ISBN-13: 9781466644502, pp: 414-449.
  175. Garg, H., M. Rani and S.P. Sharma, 2013. An efficient two phase approach for solving reliability-redundancy allocation problem using artificial bee colony technique. Comput. Oper. Res., 40: 2961-2969.
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  176. Garg, H., 2013. Reliability analysis of repairable systems using Petri nets and vague Lambda-Tau methodology. ISA Trans., 52: 6-18.
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  177. Garg, H., 2013. Performance analysis of complex repairable industrial systems using PSO and fuzzy confidence interval based methodology. ISA Trans., 52: 171-183.
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  178. Garg, H., 2013. Fuzzy multiobjective reliability optimization problem of industrial systems using particle swarm optimization. J. Ind. Math. 10.1155/2013/872450.
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  179. Garg, H., 2013. Fuzzy Inventory Model for Deteriorating Items using Different Types of Lead-Time Distributions. In: Intelligent Techniques in Engineering Management, Kahraman, C. and S. Cevik (Eds.). Springer, Germany.
  180. Garg, H., 2013. An approach for analyzing fuzzy system reliability using particle swarm optimization and intuitionistic fuzzy set theory. Multiple-Valued Logic Soft Comput., 21: 335-354.
    Direct Link  |  
  181. Garg, H. and S.P. Sharma, 2013. Reliability-redundancy allocation problem of pharmaceutical plant. J. Eng. Sci. Technol., 8: 190-198.
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  182. Garg, H. and S.P. Sharma, 2013. Predicting uncertain behavior of the press unit in a paper mill using PSOBLT technique. J. Intell. Fuzzy Syst., 25: 231-242.
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  183. Garg, H. and S.P. Sharma, 2013. Multi-objective reliability-redundancy allocation problem using particle swarm optimization. Comput. Ind. Eng., 64: 247-255.
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  184. Garg, H. and S.P. Sharma, 2013. Fuzzy multi-objective optimization problem of a synthesis unit utilizing uncertain data. J. Uncertain Syst., 7: 13-21.
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  185. Garg, H. and M. Rani, 2013. An approach for reliability analysis of industrial systems using PSO and IFS technique. ISA Trans., 52: 701-710.
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  186. Garg, H., S.P. Sharma and M. Rani, 2012. Stochastic behavior analysis of an industrial systems using PSOBLT technique. Int. J. Uncertainty Fuzziness Knowl.-Based 20: 741-761.
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  187. Garg, H., S.P. Sharma and M. Rani, 2012. Cost minimization of washing unit in a paper mill using artificial bee colony technique. Int. J. Syst. Assurance Eng. Manage., 3: 371-381.
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  188. Garg, H., S.P. Sharma and M. Rani, 2012. Behavior analysis of pulping unit in a paper mill with Weibull fuzzy distribution function using ABCBLT. Int. J. Applied Math. Mech., 8: 86-96.
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  189. Garg, H., M. Rani and S.P. Sharma, 2012. Fuzzy RAM analysis of the screening unit in a paper industry by utilizing uncertain data. Int. J. Qual. Stat. Reliab. 10.1155/2012/203842.
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  190. Garg, H. and S.P. Sharma, 2012. Stochastic behavior analysis of complex repairable industrial systems utilizing uncertain data. ISA Trans., 51: 752-762.
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  191. Garg, H. and S.P. Sharma, 2012. Behavior analysis of synthesis unit in fertilizer plant. Int. J. Qual. Reliab. Manage., 29: 217-232.
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  192. Garg, H. and S.P. Sharma, 2012. A two-phase approach for reliability and maintainability analysis of an industrial system. Int. J. Reliab. Qual. Saf. Eng., Vol. 19. 10.1142/S0218539312500131.
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  193. Sharma, S.P. and H. Garg, 2011. Behavioural analysis of urea decomposition system in a fertiliser plant. Int. J. Ind. Syst. Eng., 3: 271-297.
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  194. Rani, M., S.P. Sharma and H. Garg, 2011. Availability redundancy allocation of washing unit in a paper mill utilizing uncertain data. Elixir Mech. Eng., 39: 4627-4631.
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  195. Garg, H. and S.P. Sharma, 2011. Multi-objective optimization of crystallization unit in a fertilizer plant using particle swarm optimization. Int. J. Applied Sci. Eng., 9: 261-276.
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  196. Khalil, A.M., S.G. Li, H. Garg, H.X. Li and S.Q. Ma, 2009. New operations on interval-valued picture fuzzy set, interval-valued picture fuzzy soft set and their applications. IEEE Access, 7: 51236-51253.
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