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An Improved Approach to Group Decision-Making Using Intuitionistic Fuzzy Soft Set

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Advances in Distributed Computing and Machine Learning

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 127))

Abstract

One of the latest models to handle uncertainty is soft set. It associates each parameter in its set of parameters to a subset of elements in the universe of discourse. In its introductory article itself, Molodtsov has shown the applications of this new tool in many different areas. But, over the years, soft sets have been used by many researchers for decision-making applications over uncertainty-based datasets. It has been observed that appropriate hybridization of models is always better than the route ones. Following this, many hybrid models of soft set have already been added to the literature by different researchers. So far, multiple attempts have been made to use intuitionistic fuzzy soft set (IFSS) for decision-making. This paper introduces the concept of interval-valued fuzzy (IVF) priority for decision-making using IFSS. It also provides an improved approach to deal with group decision-making (GDM) using IFSS and an application of IFSS in GDM.

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References

  1. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  Google Scholar 

  2. Atanassov K (1986) Intuitionistic fuzzy sets. Fuzzy Set Syst 20:87–96

    Article  Google Scholar 

  3. Maji PK, Biswas R, Roy AR (2001) Fuzzy soft sets. J Fuzzy Math 9(3):589–602

    MathSciNet  MATH  Google Scholar 

  4. Maji PK, Biswas R, Roy AR (2002) An application of soft sets in a decision making problem. Comput Math Appl 44:1007–1083

    Article  MathSciNet  Google Scholar 

  5. Maji PK, Biswas R, Roy AR (2003) Soft set theory. Comput Math Appl 45:555–562

    Article  MathSciNet  Google Scholar 

  6. Tripathy BK, Arun KR (2015) A new approach to soft sets, soft multisets and their properties. Int J Reasoning-Based Intell Syst 7(3/4):244–253

    MathSciNet  Google Scholar 

  7. Xu YJ, Sun YK, Li DF (2010) Intuitionistic fuzzy soft set. In: Proceedings of 2nd international workshop on intelligent systems and applications, IEEE

    Google Scholar 

  8. Tripathy BK, Mohanty RK, Sooraj TR (2016) Application of uncertainty models in bioinformatics. In: Handbook of research on computational intelligence applications in bioinformatics, pp 169–182

    Google Scholar 

  9. Tripathy BK, Mohanty RK, Sooraj TR (2016) On intuitionistic fuzzy soft set and its application in group decision making. In: Proceedings of 1st international conference on emerging trends in engineering, technology and science, ICETETS

    Google Scholar 

  10. Tripathy BK, Mohanty RK, Sooraj TR (2016) On intuitionistic fuzzy soft sets and their application in decision-making. Lect Notes Electr Eng 396:67–73

    Article  Google Scholar 

  11. Tripathy BK, Mohanty RK, Sooraj TR, Tripathy A (2016) A modified representation of IFSS and its usage in GDM. Smart Innov Syst Technol 50:365–375

    Article  Google Scholar 

  12. Tripathy BK, Mohanty RK, Sooraj TR, Arun KR (2016) A new approach to intuitionistic fuzzy soft sets and its application in decision-making. Adv Intell Syst Comput 439:93–100

    Google Scholar 

  13. Tripathy BK, Sooraj TR, Mohanty RK (2017) A new approach to interval-valued fuzzy soft sets and its application in decision-making. Adv Intell Syst Comput 509:3–10

    Article  Google Scholar 

  14. Tripathy BK, Sooraj TR, Mohanty RK (2016) Advances decision making usisng hybrid soft set models. Int J Pharm Technol 8(3):17694–17721

    Google Scholar 

  15. Tripathy BK, Sooraj TR, Mohanty RK (2016) A new approach to fuzzy soft set theory and its application in decision making. Adv Intell Syst Comput 411:305–313

    Google Scholar 

  16. Tripathy BK, Sooraj TR, Mohanty RK, Arun KR (2016) Parameter reduction in soft set models and application in decision making. In: Handbook of research on fuzzy and rough set theory in organizational decision making, pp 331–354

    Google Scholar 

  17. Tripathy BK, Sooraj TR, Mohanty RK, Panigrahi A (2018) Group decision making through interval valued intuitionistic fuzzy soft sets. Int J Fuzzy Syst Appl 7(3):99–117

    Article  Google Scholar 

  18. Mohanty RK, Tripathy BK (2017) Intuitionistic hesitant fuzzy soft set and its application in decision making. Adv Intell Syst Comput 517:221–233

    Google Scholar 

  19. Mohanty RK, Sooraj TR, Tripathy BK (2017) IVIFS and decision-making. Adv Intell Syst Comput 468:319–330

    Google Scholar 

  20. Sooraj TR, Mohanty RK, Tripathy BK (2018) A new approach to interval-valued intuitionistic hesitant fuzzy soft sets and their application in decision making. Smart Innov Syst Technol 77:243–253

    Article  Google Scholar 

  21. Sooraj TR, Mohanty RK, Tripathy BK (2018) Improved decision making through IFSS. Smart Innov Syst Technol 77:213–219

    Article  Google Scholar 

  22. Sooraj TR, Mohanty RK, Tripathy BK (2017) Hesitant fuzzy soft set theory and its application in decision making. Adv Intell Syst Comput 517:315–322

    Google Scholar 

  23. Sooraj TR, Mohanty RK, Tripathy BK (2016) Fuzzy soft set theory and its application in group decision making. Adv Intell Syst Comput 452:171–178

    Google Scholar 

  24. Molodtsov D (1999) Soft set theory—first results. Comput Math Appl 37:19–31

    Article  MathSciNet  Google Scholar 

  25. Atanassov K, Gargov G (1989) Interval valued intuitionistic fuzzy sets. Fuzzy Sets Syst 31(3):343–349

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. VIT, Vellore, Tamil Nadu, India

    R. K. Mohanty & B. K. Tripathy

Authors
  1. R. K. Mohanty
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  2. B. K. Tripathy
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Corresponding author

Correspondence to B. K. Tripathy .

Editor information

Editors and Affiliations

  1. Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Asis Kumar Tripathy

  2. San Diego State University, San Diego, CA, USA

    Mahasweta Sarkar

  3. Institute of Technical Education and Research (ITER), Siksha ‘O’ Anusandhan (SOA) Deemed to be University, Bhubaneswar, Odisha, India

    Jyoti Prakash Sahoo

  4. Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. National Institute of Technology, Rourkela, Rourkela, Odisha, India

    Suchismita Chinara

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Mohanty, R.K., Tripathy, B.K. (2021). An Improved Approach to Group Decision-Making Using Intuitionistic Fuzzy Soft Set. In: Tripathy, A., Sarkar, M., Sahoo, J., Li, KC., Chinara, S. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 127. Springer, Singapore. https://doi.org/10.1007/978-981-15-4218-3_28

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