Skip to main content

Advertisement

SpringerLink
Log in

On some new aggregation operators for T-spherical fuzzy hypersoft sets with application in renewable energy sources

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

The process of decision-making in any knowledge-based system inherits a lot of uncertainty in the form of imprecise, incomplete, and inexact information with partial contents where the systematic aggregation of the information plays a vital role. In the present communication, a new kind of fuzzy hypersoft set termed as T-spherical fuzzy hypersoft set has been proposed with various aggregation operators for the decision-making process of renewable energy source selection. Two different types of aggregation operators (arithmetic/geometric) for the T-spherical fuzzy hypersoft sets (TSFHSSs) have been studied along with various properties and important results. Further, an algorithm utilizing the proposed operators for solving the decision-making selection problem has been provided for obtaining the best solution for the renewable energy source selection problem. Some advantageous remarks have also been listed for better understanding and readability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Data availability

Enquiries about data availability should be directed to the authors.

References

  1. Wang Y, Xu L, Solangi YA (2020) Strategic renewable energy resources selection for Pakistan: based on SWOT-fuzzy AHP approach. Sustain Cities Soc 52:101861

    Article  Google Scholar 

  2. Kong Y, Feng C, Yang J (2020) How does China manage its energy market? A perspective of policy evolution. Energy Policy 147:111898

    Article  Google Scholar 

  3. Dhumras H, Bajaj RK (2023) On assembly robotic design evaluation problem using enhanced quality function deployment with \(q\)-rung orthopair fuzzy set theoretic environment. J Inf Sci Eng 39(3):623–636

    Google Scholar 

  4. Molodtsov D (1999) Soft set theory first results. Comput Math Appl 37(4–5):19–31

    MathSciNet  MATH  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  6. Maji PK, Biswas R, Roy AR (2001) Intuitionistic fuzzy soft sets. J Fuzzy Math 9(3):677–692

    MathSciNet  MATH  Google Scholar 

  7. Garg H, Arora R (2020) TOPSIS method based on correlation coefficient for solving decision-making problems with intuitionistic fuzzy soft set information. AIMS Math 5:2944–2966

    Article  MathSciNet  MATH  Google Scholar 

  8. Peng X, Yang Y, Song J (2015) Pythagorean fuzzy soft set and its application. Comput Eng 41:224–229

    Google Scholar 

  9. Zulqarnain RM, Xin XL, Garg H, Khan WA (2021) Aggregation operators of Pythagorean fuzzy soft sets with their application for green supplier chain management. J Intell Fuzzy Syst 40:5545–5563

    Article  Google Scholar 

  10. Riaz M, Salabun W, Athar Farid HM, Ali N, Watrobski J (2020) A robust q-rung orthopair fuzzy information aggregation using Einstein operations with application to sustainable energy planning decision management. Energies 13(9):2155

    Article  Google Scholar 

  11. Ohlan A (2022) Multiple attribute decision-making based on distance measure under Pythagorean fuzzy environment. Int J Inf Technol 14:2205–2217

    Google Scholar 

  12. Sharma R, Singh U (2021) Fuzzy based energy efficient clustering for designing WSN-based smart parking systems. Int J Inf Technol 13:2381–2387

    Google Scholar 

  13. Lekhraj Kumar A, Kumar A (2022) An approach based on modified multiple attribute decision making for optimal node deployment in wireless sensor networks. Int J Inf Technol 14:1805–1814

    Google Scholar 

  14. Dani D, Agrawal G (2021) Evaluating the quality of Indian school education board’s websites using multi-criteria decision-making models. Int J Inf Technol 13:1–9

    Google Scholar 

  15. Wang L, Li N (2019) Continuous interval-valued Pythagorean fuzzy aggregation operators for multiple attribute group decision making. J Intell Fuzzy Syst 36:6245–6263

    Article  Google Scholar 

  16. Garg H (2017) Some picture fuzzy aggregation operators and their applications to multicriteria decision-making. Arab J Sci Eng 42:5275–5290

    Article  MathSciNet  MATH  Google Scholar 

  17. Dhumras H, Bajaj RK (2022) On prioritization of hydrogen fuel cell technology utilizing bi-parametric picture fuzzy information measures in VIKOR and TOPSIS decision-making approaches. Int J Hydrog Energy. https://doi.org/10.1016/j.ijhydene.2022.09.093

    Article  Google Scholar 

  18. Rani P, Mishra AR, Pardasani KR, Mardani A, Liao H, Streimikiene D (2019) A novel VIKOR approach based on entropy and divergence measures of Pythagorean fuzzy sets to evaluate renewable energy technologies in India. J Clean Prod 238:117936

    Article  Google Scholar 

  19. Wu Y, Xu C, Ke Y, Tao Y, Li X (2019) Portfolio optimization of renewable energy projects under the type-2 fuzzy environment with a sustainability perspective. Comput Ind Eng 133:69–82

    Article  Google Scholar 

  20. Yuan J, Li C, Li W, Liu D, Li X (2018) Linguistic hesitant fuzzy multi-criterion decision-making for renewable energy: a case study in Jilin. J Clean Prod 172:3201–3214

    Article  Google Scholar 

  21. Dincer H, Yuksel S (2019) Multidimensional evaluation of global investments on renewable energy with the integrated fuzzy decision-making model under the hesitancy. Int J Energy Res 43(5):1775–1784

    Article  Google Scholar 

  22. Dhumras H, Bajaj RK (2022) On renewable energy source selection methodologies utilizing picture fuzzy hypersoft information with choice and value matrices. Scientia Iran. https://doi.org/10.24200/SCI.2022.60529.6847

    Article  Google Scholar 

  23. Ashraf S, Abdullah S, Mahmood T, Ghani F, Mahmood T (2019) Spherical fuzzy sets and their applications in multi-attribute decision-making problems. J Intell Fuzzy Syst 36:2829–2844

    Article  Google Scholar 

  24. Smarandache F (2018) Extension of soft set to hypersoft set, and then to plithogenic hypersoft set. Neutrosophic Sets Syst 22(1):168–170

    Google Scholar 

  25. Yolcu A, Smarandache F, Ozturk TY (2021) Intuitionistic fuzzy hypersoft sets. Commun Fac Sci Univ Ank Ser A1 Math Stat 70(1):443–455

    Article  MathSciNet  Google Scholar 

  26. Zulqarnain RM, Xin XL, Saeed MA (2021) A development of Pythagorean fuzzy hypersoft set with basic operations and decision-making approach based on the correlation coefficient. Theory and application of hypersoft set, vol 5. Pons Publishing House Brussels, Brussels, pp 85–106

    Google Scholar 

  27. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  MATH  Google Scholar 

  28. Coung B (2014) Picture fuzzy sets. J Comput Sci Cybern 30(4):409–420

    Google Scholar 

Download references

Acknowledgements

We are very much thankful to the Editorial Office and anonymous reviewers for suggesting the points/mistakes which have been well implemented/corrected for the necessary improvement of the manuscript. We sincerely acknowledge our deep sense of gratitude to the Editorial office and reviewers for giving their valuable time to the manuscript.

Funding

The authors declare that the research carried out in this article has no source of funding.

Author information

Author notes

  1. Monika, Rakesh K Bajaj and Aman Sharma equally contributed to the design and implementation of the research, to the analysis of the results, and to the writing of the manuscript.

Authors and Affiliations

  1. Department of Mathematics, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173234, India

    Monika & Rakesh K. Bajaj

  2. Department of CSE & IT, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173234, India

    Aman Sharma

Authors
  1. Monika
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rakesh K. Bajaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aman Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Monika, RKB and AS equally contributed to the design and implementation of the research, to the analysis of the results, and to the writing of the manuscript.

Corresponding author

Correspondence to Rakesh K. Bajaj.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

This article does not contain any studies with humans.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Monika, Bajaj, R.K. & Sharma, A. On some new aggregation operators for T-spherical fuzzy hypersoft sets with application in renewable energy sources. Int. j. inf. tecnol. 15, 2457–2467 (2023). https://doi.org/10.1007/s41870-023-01258-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-023-01258-y

Keywords

Search

Navigation