Skip to main content

Advertisement

Log in

An Enhanced Technique for Order Preference by Similarity to Ideal Solutions and its Application to Renewable Energy Resources Selection Problem

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

Selecting right renewable energy resources (RESs) is emerging as a solution to alleviate energy crisis and environmental pollution. To better address the RESs selection problems, this paper proposes an enhanced Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS). Specifically, the decision information is characterized by linguistic terms and then encoded by interval type-2 fuzzy sets (IT2FSs). The current IT2FSs preprocessing models recklessly transform IT2FSs into crisp numbers, which may discount the superiority of applying fuzzy sets. Hence, we define a novel interval type-2 fuzzy projection model to measure the IT2FSs and some related theorems about the projection model are explored mathematically. Moreover, based on this new interval type-2 fuzzy projection model, an enhanced TOPSIS is proposed to calculate the closeness coefficients of alternatives. Of note, to keep information as much as possible, the obtained closeness coefficients are still IT2FSs. Finally, the Karnik–Mendel (KM) algorithms are employed to compare and rank those closeness coefficients. The effectiveness of the proposed method is demonstrated by a RESs selection case. Comparisons are also conducted to illustrate its advantages.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Cayir Ervural, B., Zaim, S., Demirel, O.F., Aydin, Z., Delen, D.: An ANP and fuzzy TOPSIS-based SWOT analysis for Turkey’s energy planning. Renew. Sustain. Energy Rev. 82, 1538–1550 (2018)

    Google Scholar 

  2. Büyüközkan, G., Güleryüz, S.: Evaluation of renewable energy resources in turkey using an integrated mcdm approach with linguistic interval fuzzy preference relations. Energy 123, 149–163 (2017)

    Google Scholar 

  3. Çolak, M., Kaya, İ.: Prioritization of renewable energy alternatives by using an integrated fuzzy MCDM model: a real case application for Turkey. Renew. Sustain. Energy Rev. 80, 840–853 (2017)

    Google Scholar 

  4. Liu, P., Liu, J., Merigó, J.M.: Partitioned Heronian means based on linguistic intuitionistic fuzzy numbers for dealing with multi-attribute group decision making. Appl. Soft Comput. 65, 395–422 (2018)

    Google Scholar 

  5. Li, C., Dong, Y., Herrera, F.: A consensus model for large-scale linguistic group decision making with a feedback recommendation based on clustered personalized individual semantics and opposing consensus groups. IEEE Trans. Fuzzy Syst. 27, 221–233 (2019)

    Google Scholar 

  6. Meng, F., Tang, J., Fujita, H.: Linguistic intuitionistic fuzzy preference relations and their application to multi-criteria decision making. Inf. Fusion 46, 77–90 (2019)

    Google Scholar 

  7. Wang, L., Wang, H., Xu, Z., Ren, Z.: A bi-projection model based on linguistic terms with weakened hedges and its application in risk allocation. Appl Soft Comput 87, 105996 (2020)

    Google Scholar 

  8. Wang, H., Pan, X., Yan, J., Yao, J., He, S.: A projection-based regret theory method for multi-attribute decision making under interval type-2 fuzzy sets environment. Inf. Sci. 512, 108–122 (2020)

    MathSciNet  MATH  Google Scholar 

  9. Chen, T.: Likelihoods of interval type-2 trapezoidal fuzzy preference relations and their application to multiple criteria decision analysis. Inf. Sci. 295, 303–322 (2015)

    MathSciNet  MATH  Google Scholar 

  10. Mendel, J.M., John, R.I., Liu, F.: Interval type-2 fuzzy logic systems made simple. IEEE Trans. Fuzzy Syst. 14, 808–821 (2006)

    Google Scholar 

  11. Boukezzoula, R., Coquin, D.: A decision-making computational methodology for a class of type-2 fuzzy intervals: an interval-based approach. Inf. Sci. 510, 256–282 (2020)

    MATH  Google Scholar 

  12. Zhou, X., Wang, Y., Chai, J., Wang, L., Wang, S., Lev, B.: Sustainable supply chain evaluation: a dynamic double frontier network DEA model with interval type-2 fuzzy data. Inf. Sci. 504, 394–421 (2019)

    Google Scholar 

  13. Tolga, A.C., Parlak, I.B., Castillo, O.: Finite interval valued type-2 Gaussian fuzzy numbers applied to fuzzy TODIM in a healthcare problem. Eng Appl Artif Intell 87, 103352 (2020)

    Google Scholar 

  14. Deveci, M., Canıtez, F., Gökaşar, I.: WASPAS and TOPSIS based interval type-2 fuzzy MCDM method for a selection of a car sharing station. Sustain. Cities Soc. 41, 777–791 (2018)

    Google Scholar 

  15. Wu, T., Liu, X., Liu, F.: An interval type-2 fuzzy TOPSIS model for large scale group decision making problems with social network information. Inf. Sci. 432, 392–410 (2018)

    MathSciNet  MATH  Google Scholar 

  16. Yao, L., Xu, Z., Lv, C., Hashim, M.: Incomplete interval type-2 fuzzy preference relations based on a multi-criteria group decision-making model for the evaluation of wastewater treatment technologies. Measurement 151, 107137 (2020)

    Google Scholar 

  17. Wu, D., Mendel, J.M., Coupland, S.: Enhanced interval approach for encoding words into interval type-2 fuzzy sets and its convergence analysis. IEEE Trans. Fuzzy Syst. 20, 499–513 (2012)

    Google Scholar 

  18. Ching-Lai, H., Kwangsun, Y.: Multiple attribute decision making: methods and applications. Springer, Berlin (1981)

    MATH  Google Scholar 

  19. Jahan, A., Ismail, M.Y., Sapuan, S.M., Mustapha, F.: Material screening and choosing methods—a review. Mater. Des. 31, 696–705 (2010)

    Google Scholar 

  20. Bhattacharjee, P., Debnath, A., Chakraborty, S., Mandal, U.K.: Selection of optimal aluminum alloy using TOPSIS method under fuzzy environment. J. Intell. Fuzzy Syst. 32, 871–876 (2017)

    Google Scholar 

  21. Baykasoğlu, A., Gölcük, İ.: Development of an interval type-2 fuzzy sets based hierarchical MADM model by combining DEMATEL and TOPSIS. Expert Syst. Appl. 70, 37–51 (2017)

    Google Scholar 

  22. Fu, Z., Liao, H.: Unbalanced double hierarchy linguistic term set: the TOPSIS method for multi-expert qualitative decision making involving green mine selection. Inf. Fusion 51, 271–286 (2019)

    Google Scholar 

  23. Wang, Z., Hao, H., Gao, F., Zhang, Q., Zhang, J., Zhou, Y.: Multi-attribute decision making on reverse logistics based on DEA-TOPSIS: a study of the Shanghai End-of-life vehicles industry. J. Clean. Prod. 214, 730–737 (2019)

    Google Scholar 

  24. Liao, H., Gou, X., Xu, Z., Zeng, X., Herrera, F.: Hesitancy degree-based correlation measures for hesitant fuzzy linguistic term sets and their applications in multiple criteria decision making. Inf. Sci. 508, 275–292 (2020)

    MathSciNet  Google Scholar 

  25. Wang, Z., Wang, Y.: Prospect theory-based group decision-making with stochastic uncertainty and 2-tuple aspirations under linguistic assessments. Inf. Fusion 56, 81–92 (2020)

    Google Scholar 

  26. Yue, C.: Projection-based approach to group decision-making with hybrid information representations and application to software quality evaluation. Comput. Ind. Eng. 132, 98–113 (2019)

    Google Scholar 

  27. Mendel, J.M., Liu, F.: Super-exponential convergence of the Karnik–Mendel algorithms for computing the centroid of an interval type-2 fuzzy set. IEEE Trans. Fuzzy Syst. 15, 309–320 (2007)

    Google Scholar 

  28. Wu, D., Mendel, J.M.: Enhanced Karnik–Mendel algorithms. IEEE Trans. Fuzzy Syst. 17, 923–934 (2009)

    Google Scholar 

  29. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reason I. Inf. Sci. 8, 199–249 (1975)

    MATH  Google Scholar 

  30. Qin, J., Liu, X., Pedrycz, W.: An extended VIKOR method based on prospect theory for multiple attribute decision making under interval type-2 fuzzy environment. Knowl. Based Syst. 86, 116–130 (2015)

    Google Scholar 

  31. Wu, T., Liu, X.-W.: An interval type-2 fuzzy clustering solution for large-scale multiple-criteria group decision-making problems. Knowl. Based Syst. 114, 118–127 (2016)

    Google Scholar 

  32. Yue, Z.: Extension of TOPSIS to determine weight of decision maker for group decision making problems with uncertain information. Expert Syst. Appl. 39, 6343–6350 (2012)

    Google Scholar 

  33. Cables, E., García-Cascales, M.S., Lamata, M.T.: The LTOPSIS: an alternative to TOPSIS decision-making approach for linguistic variables. Expert Syst. Appl. 39, 2119–2126 (2012)

    Google Scholar 

  34. Wang, L., Wang, Y., Martínez, L.: Fuzzy TODIM method based on alpha-level sets. Expert Syst. Appl. 140, 112899 (2020)

    Google Scholar 

  35. Yue, Z.: TOPSIS-based group decision-making methodology in intuitionistic fuzzy setting. Inf. Sci. 277, 141–153 (2014)

    MathSciNet  MATH  Google Scholar 

  36. Hajek, P., Froelich, W.: Integrating TOPSIS with interval-valued intuitionistic fuzzy cognitive maps for effective group decision making. Inf. Sci. 485, 394–412 (2019)

    Google Scholar 

  37. Liang, D., Xu, Z., Liu, D., Wu, Y.: Method for three-way decisions using ideal TOPSIS solutions at Pythagorean fuzzy information. Inf. Sci. 435, 282–295 (2018)

    MathSciNet  MATH  Google Scholar 

  38. Liang, D., Xu, Z.: The new extension of TOPSIS method for multiple criteria decision making with hesitant Pythagorean fuzzy sets. Appl. Soft Comput. 60, 167–179 (2017)

    Google Scholar 

  39. Xu, Z., Zhang, X.: Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information. Knowl. Based Syst. 52, 53–64 (2013)

    Google Scholar 

  40. Wu, Z., Xu, J., Jiang, X., Zhong, L.: Two MAGDM models based on hesitant fuzzy linguistic term sets with possibility distributions: VIKOR and TOPSIS. Inf. Sci. 473, 101–120 (2019)

    MathSciNet  MATH  Google Scholar 

  41. Liu, P., Teng, F.: Probabilistic linguistic TODIM method for selecting products through online product reviews. Inf. Sci. 485, 441–455 (2019)

    Google Scholar 

  42. Zhang, Z., Zhang, S.: A novel approach to multi attribute group decision making based on trapezoidal interval type-2 fuzzy soft sets. Appl. Math. Model. 37, 4948–4971 (2013)

    MathSciNet  MATH  Google Scholar 

  43. Yue, C.: Picture fuzzy normalized projection and extended VIKOR approach to software reliability assessment. Appl. Soft Comput. 88, 106056 (2020)

    Google Scholar 

  44. Wu, Y., Xu, C., Zhang, T.: Evaluation of renewable power sources using a fuzzy MCDM based on cumulative prospect theory. Energy 147, 1227–1239 (2018)

    Google Scholar 

  45. Ezbakhe, F., Pérez-Foguet, A.: Decision analysis for sustainable development: The case of renewable energy planning under uncertainty. Eur. J. Oper. Res. 34, 563–577 (2020)

    MATH  Google Scholar 

  46. Ghenai, C., Albawab, M., Bettayeb, M.: Sustainability indicators for renewable energy systems using multi-criteria decision-making model and extended SWARA/ARAS hybrid method. Renew. Energy 146, 580–597 (2020)

    Google Scholar 

  47. Wu, Y., Wang, J., Hu, Y., Ke, Y., Li, L.: An extended TODIM-PROMETHEE method for waste-to-energy plant site selection based on sustainability perspective. Energy 156, 1–16 (2018)

    Google Scholar 

  48. Wu, Y., Wang, J., Ji, S., Song, Z.: Renewable energy investment risk assessment for nations along China’s Belt & Road Initiative: an ANP-cloud model method. Energy 190, 116381 (2020)

    Google Scholar 

Download references

Acknowledgements

This study was funded by National Natural Science Foundation of China (Grant No. 61773123).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yingming Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pan, X., Wang, Y. An Enhanced Technique for Order Preference by Similarity to Ideal Solutions and its Application to Renewable Energy Resources Selection Problem. Int. J. Fuzzy Syst. 23, 1087–1101 (2021). https://doi.org/10.1007/s40815-020-00914-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-020-00914-w

Keywords

Navigation