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Development of Reliable TOPSIS Method Using Intuitionistic Z-Numbers

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12th World Conference “Intelligent System for Industrial Automation” (WCIS-2022) (WCIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 718))

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Abstract

Technique for order of preference by similarity to ideal solution (TOPSIS) is a multi-criteria decision-making (MCDM) method which is developed based on the distance measure from the positive and negative ideal solutions. This paper extends the TOPSIS for handling data in form of intuitionistic Z-numbers (IZN). IZN consists of restriction and reliability components which are characterized by the intuitionistic fuzzy numbers. The distance measure between IZN is proposed using the convex compound of the distances for the restriction and reliability parts. The supplier selection problem in an automobile manufacturing company is adopted to illustrate the proposed model. Sensitivity analysis is performed for the validation of the proposed model and its result shows that the proposed model gives a consistent ranking of alternatives. The strength of the proposed model is the preservation of decision information in form of IZN which does not possess the conversion into regular fuzzy number to avoid the loss of information.

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References

  1. Ahmadov, S.A.: Z+ - number based alternatives selection in investment problem. In: Aliev, R.A., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M., Sadikoglu, F.M. (eds.) ICSCCW 2021. LNNS, vol. 362, pp. 43–50. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-92127-9_10

    Chapter  Google Scholar 

  2. Zadeh, L.A.: Fuzzy sets. Inf. Control. 8, 338–353 (1965)

    Article  Google Scholar 

  3. Bellman, R.E., Zadeh, L.A.: Decision-making in a fuzzy environment. Manage. Sci. 17, B-141–B-164 (1970)

    Google Scholar 

  4. Zadeh, L.A.: A note on Z-numbers. Inf. Sci. (Ny) 181, 2923–2932 (2011)

    Article  Google Scholar 

  5. Alizadeh, A.V.: On a view of decision making under Z-information. In: Aliev, R.A., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M., Sadikoglu, F.M. (eds.) ICSCCW 2021. LNNS, vol. 362, pp. 636–644. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-92127-9_85

    Chapter  Google Scholar 

  6. Abdullahi, M., Ahmad, T., Ramachandran, V.: A review on some arithmetic concepts of Z-number and its application to real-world problems. Int. J. Inf. Technol. Decis. Mak. 19, 1091–1122 (2020)

    Article  Google Scholar 

  7. Kang, B., Wei, D., Li, Y., Deng, Y.: A method of converting Z-number to classical fuzzy number. J. Inf. Comput. Sci. 9, 703–709 (2012)

    Google Scholar 

  8. Hwang, C.-L., Yoon, K.: Methods for multiple attribute decision making. In: Multiple Attribute Decision Making, pp. 58–191 (1981)

    Google Scholar 

  9. Tugrul, F.: An evaluation of supermarkets from the lens of multiple criteria: the intuitionistic fuzzy TOPSIS method. Black Sea J. Eng. Sci. 5, 146–150 (2022)

    Google Scholar 

  10. Sari, S.S., Aka, S.: Comparison of financial payment systems with the fuzzy TOPSIS method. Turkish Stud. 17, 815–834 (2022)

    Google Scholar 

  11. Nada, O.: Fuzzy TOPSIS for ranking wave energy converters. ERJ. Eng. Res. J. 45, 531–539 (2022)

    Article  Google Scholar 

  12. Yaakob, A.M., Gegov, A.: Interactive TOPSIS based group decision making methodology using Z-numbers. Int. J. Comput. Intell. Syst. 9, 311–324 (2016)

    Article  Google Scholar 

  13. Forghani, A., Sadjadi, S.J., Moghadam, B.F.: A supplier selection model in pharmaceutical supply chain using PCA, Z-TOPSIS and MILP: a case study. PLoS ONE 13, 1–17 (2018)

    Article  Google Scholar 

  14. Krohling, R.A., Pacheco, A.G.C., dos Santos, G.A.: TODIM and TOPSIS with Z-numbers. Front. Inf. Technol. Electron. Eng. 20, 283–291 (2019)

    Article  Google Scholar 

  15. Ahmad, N., Yaakob, A.M., Gegov, A., Kasim, M.M.: Integrating fuzzy AHP and Z-TOPSIS for supplier selection in an automotive manufacturing company. AIP Conf. Proc. 2138 (2019)

    Google Scholar 

  16. Sari, I.U., Kahraman, C.: Intuitionistic fuzzy Z-numbers. In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I.U., Cebi, S., Tolga, A.C. (eds.) INFUS 2020. AISC, vol. 1197, pp. 1316–1324. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-51156-2_154

    Chapter  Google Scholar 

  17. Alam, N.M.F.H.N.B., Khalif, K.M.N.K., Jaini, N.I.: Application of intuitionistic Z-numbers in supplier selection. Intell. Autom. Soft Comput. 35, 47–61 (2023)

    Google Scholar 

  18. Alam, N.M.F.H.N.B., Khalif, K.M.N.K., Jaini, N.I., Abu Bakar, A.S., Abdullah, L.: Defuzzification of intuitionistic Z-numbers for fuzzy multi criteria decision making. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A.C., Sari, I.U. (eds.) INFUS 2021. LNNS, vol. 308, pp. 879–887. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-85577-2_101

    Chapter  Google Scholar 

  19. Wang, F., Mao, J.: Approach to multicriteria group decision making with Z-numbers based on TOPSIS and power aggregation operators. Math. Probl. Eng. 2019, 1–18 (2019)

    Google Scholar 

  20. Alam, N.M.F.H.N.B., Khalif, K.M.N.K., Jaini, N.I.: Arithmetic operations of intuitionistic Z-numbers using horizontal membership functions. In: Ghazali, R., Nawi, N.M., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds.) Recent Advances in Soft Computing and Data Mining: Proceedings of the Fifth International Conference on Soft Computing and Data Mining (SCDM 2022), May 30-31, 2022, pp. 25–34. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-00828-3_3

    Chapter  Google Scholar 

  21. Li, Y., Rao, C., Goh, M., Xiao, X.: Novel multi-attribute decision-making method based on Z-number grey relational degree. Soft Comput. 26, 13333–13347 (2022)

    Article  Google Scholar 

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Acknowledgement

The authors would like to thank the Ministry of Higher Education for providing financial support under Fundamental Research Grant Scheme (FRGS) No. FRGS/1/2019/STG06/UMP/02/9 (University reference RDU1901178) and Universiti Malaysia Pahang for laboratory facilities as well as additional financial support under UMP Postgraduate Research Grants Scheme (PGRS) No. PGRS220301.

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Authors and Affiliations

  1. Centre for Mathematical Sciences, Universiti Malaysia Pahang, Gambang, Kuantan, Pahang, Malaysia

    Nik Muhammad Farhan Hakim Nik Badrul Alam, Ku Muhammad Naim Ku Khalif & Nor Izzati Jaini

  2. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Pahang, Bandar Tun Abdul Razak Jengka, Pahang, Malaysia

    Nik Muhammad Farhan Hakim Nik Badrul Alam

  3. Centre of Excellence for Artificial Intelligence and Data Science, Universiti Malaysia Pahang, Gambang, Kuantan, Pahang, Malaysia

    Ku Muhammad Naim Ku Khalif

Authors
  1. Nik Muhammad Farhan Hakim Nik Badrul Alam
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  2. Ku Muhammad Naim Ku Khalif
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  3. Nor Izzati Jaini
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Corresponding author

Correspondence to Ku Muhammad Naim Ku Khalif .

Editor information

Editors and Affiliations

  1. Joint MBA Program, Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    R. A. Aliev

  2. Department Automation of Production Processes, Tashkent State Technical University, Tashkent, Uzbekistan

    Nodirbek Rustambekovich Yusupbekov

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  4. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    M. B. Babanli

  6. Department of Computer Engineering, Near East University, Nicosia, Türkiye

    Fahreddin M. Sadikoglu

  7. Tashkent State Technical University, Tashkent, Uzbekistan

    S. M. Turabdjanov

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Alam, N.M.F.H.N.B., Khalif, K.M.N.K., Jaini, N.I. (2024). Development of Reliable TOPSIS Method Using Intuitionistic Z-Numbers. In: Aliev, R.A., et al. 12th World Conference “Intelligent System for Industrial Automation” (WCIS-2022). WCIS 2022. Lecture Notes in Networks and Systems, vol 718. Springer, Cham. https://doi.org/10.1007/978-3-031-51521-7_11

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