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An approach for combining MULTIMOORA method and Muirhead mean operators based on the complex Pythagorean fuzzy uncertain linguistic representation model

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Abstract

The MULTIMOORA is a relatively new multi-attribute decision-making (MADM) technique containing three major parts. It has partial and complete aggregation information. The MULTIMOORA procedure contains three major parts: the full multiplicative shape, ratio system, and reference point. Furthermore, the Muirhead mean (MM) operator can easily capture the interaction between any number of preferences. The main feature of this model is to evaluate the novel theory of complex Pythagorean fuzzy uncertain linguistic (CPFUL) settings and utilize their useful properties (“algebraic laws, score value, and accuracy value”). Additionally, we combined the main theory of MM operators with information taken from the theory of CPFUL to diagnose the major idea of the CPFUL Muirhead mean (CPFULMM), CPFUL weighted Muirhead mean (CPFULWMM), CPFUL dual Muirhead mean (CPFULDMM), and CPFUL dual weighted Muirhead mean (CPFULDWMM) operators. We then evaluated their valuable properties (“idempotency, Boundedness, and monotonicity”). Moreover, to enhance the worth and feasibility of the diagnosed approach, we elaborated the extension of the MULTIMOORA method under CPFUL information. We then diagnosed a procedure of the MADM scenario whilst considering the evaluated operators using the CPFUL set theory. Lastly, to compare the diagnosed information with some old operators, we used numerical examples to illustrate the flexibility and feasibility of the presented information.

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References

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

    Article  MATH  Google Scholar 

  2. Atanassov K (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96

    Article  MathSciNet  MATH  Google Scholar 

  3. Bal M, Ahmad KD, Hajjari AA, Ali R (2022) A short note on the kernel subgroup of intuitionistic fuzzy groups. J Neutroso Fuzzy Syst (JNFS) 2(1):14–20

    Article  Google Scholar 

  4. Gohain B, Chutia R, Dutta P (2022) Distance measure on intuitionistic fuzzy sets and its application in decision-making, pattern recognition, and clustering problems. Int J Intell Syst 37(3):2458–2501

    Article  Google Scholar 

  5. Jebadass JR, Balasubramaniam P (2022) Low light enhancement algorithm for color images using intuitionistic fuzzy sets with histogram equalization. Multimed Tools Appl 81(6):8093–8106

    Article  Google Scholar 

  6. Garg H (2016) A new generalized improved score function of interval-valued intuitionistic fuzzy sets and applications in expert systems. Appl Soft Comput 38:988–999

    Article  Google Scholar 

  7. Yager RR (2013) Pythagorean fuzzy subsets. In 2013 joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS) (pp. 57-61). IEEE

  8. Khan MJ, Ali MI, Kumam P, Kumam W, Aslam M, Alcantud JCR (2022) Improved generalized dissimilarity measure-based VIKOR method for Pythagorean fuzzy sets. Int J Intell Syst 37(3):1807–1845

    Article  Google Scholar 

  9. Sarkar B, Biswas A (2022) A multi-criteria decision making approach for strategy formulation using Pythagorean fuzzy logic. Expert Syst 39(1):e12802

    Article  Google Scholar 

  10. Bhunia S, Ghorai G, Xin Q, Gulzar M (2022) On the algebraic attributes of (α, β)-Pythagorean fuzzy subrings and (α, β)-Pythagorean fuzzy ideals of rings. IEEE Access 10:11048–11056

    Article  Google Scholar 

  11. Garg H (2018) Some methods for strategic decision-making problems with immediate probabilities in Pythagorean fuzzy environment. Int J Intell Syst 33(4):687–712

    Article  Google Scholar 

  12. Ramot D, Milo R, Friedman M, Kandel A (2002) Complex fuzzy sets. IEEE Trans Fuzzy Syst 10(2):171–186

    Article  Google Scholar 

  13. Alkouri, A. M. D. J. S ,& Salleh, A. R. (2012) Complex intuitionistic fuzzy sets. In AIP conference proceedings (Vol. 1482, no. 1, pp. 464-470). American Institute of Physics

  14. Garg H, Rani D (2019) Complex interval-valued intuitionistic fuzzy sets and their aggregation operators. Fundamenta Informa 164(1):61–101

    Article  MathSciNet  MATH  Google Scholar 

  15. Rani D, Garg H (2017) Distance measures between the complex intuitionistic fuzzy sets and their applications to the decision-making process. Int J Uncertain Quantif 7(5):423–439

    Article  MathSciNet  MATH  Google Scholar 

  16. Garg H, Rani D (2019) A robust correlation coefficient measure of complex intuitionistic fuzzy sets and their applications in decision-making. Appl Intell 49(2):496–512

    Article  Google Scholar 

  17. Garg H, Rani D (2019) Novel aggregation operators and ranking method for complex intuitionistic fuzzy sets and their applications to decision-making process. Artif Intell Rev:1–26

  18. Ullah K, Mahmood T, Ali Z, Jan N (2020) On some distance measures of complex Pythagorean fuzzy sets and their applications in pattern recognition. Comp Intel Syst 6(1):15–27

    Article  Google Scholar 

  19. Akram M, Naz S (2019) A novel decision-making approach under complex Pythagorean fuzzy environment. Mathema Compu App 24(3):73

    MathSciNet  Google Scholar 

  20. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning—I. Inf Sci 8(3):199–249

    Article  MathSciNet  MATH  Google Scholar 

  21. Herrera F, Martínez L (2000) A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans Fuzzy Syst 8(6):746–752

    Article  Google Scholar 

  22. Baležentis A, Baležentis T, Valkauskas R (2010) Evaluating situation of Lithuania in the European Union: structural indicators and MULTIMOORA method. Technol Econ Dev Econ 16(4):578–602

    Article  Google Scholar 

  23. Hafezalkotob A, Hafezalkotob A (2016) Extended MULTIMOORA method based on Shannon entropy weight for materials selection. J Indust Engin Intern 12(1):1–13

    Article  MATH  Google Scholar 

  24. Liu HC, Fan XJ, Li P, Chen YZ (2014) Evaluating the risk of failure modes with extended MULTIMOORA method under fuzzy environment. Eng Appl Artif Intell 34:168–177

    Article  Google Scholar 

  25. Baležentis A, Baležentis T, Brauers WK (2012) Personnel selection based on computing with words and fuzzy MULTIMOORA. Expert Syst Appl 39(9):7961–7967

    Article  MATH  Google Scholar 

  26. Liu Z, Liu P (2017) Intuitionistic uncertain linguistic partitioned Bonferroni means and their application to multiple attribute decision-making. Int J Syst Sci 48(5):1092–1105

    Article  MathSciNet  MATH  Google Scholar 

  27. Liu P, Liu Z, Zhang X (2014) Some intuitionistic uncertain linguistic Heronian mean operators and their application to group decision making. Appl Math Comput 230:570–586

    MathSciNet  MATH  Google Scholar 

  28. Liu PD, Zhang X (2012) Intuitionistic uncertain linguistic aggregation operators and their application to group decision making. Syst Engin-Theory Prac 32(12):2704–2711

    Google Scholar 

  29. Lu M, Wei GW (2017) Pythagorean uncertain linguistic aggregation operators for multiple attribute decision making. Int J Knowl-based Intel Engin Syst 21(3):165–179

    Google Scholar 

  30. Liu Z, Liu P, Liu W, Pang J (2017) Pythagorean uncertain linguistic partitioned Bonferroni mean operators and their application in multi-attribute decision making. J Intell Fuzzy Syst 32(3):2779–2790

    Article  MathSciNet  MATH  Google Scholar 

  31. Garg H, Rani D (2019) Generalized geometric aggregation operators based on t-norm operations for complex intuitionistic fuzzy sets and their application to decision-making. Cognitive Computation, 1–20

  32. Garg H, Rani D (2020) Robust averaging–geometric aggregation operators for complex intuitionistic fuzzy sets and their applications to MCDM process. Arab J Sci Eng 45(3):2017–2033

    Article  Google Scholar 

  33. Alkan Ö, Albayrak ÖK (2020) Ranking of renewable energy sources for regions in Turkey by fuzzy entropy based fuzzy COPRAS and fuzzy MULTIMOORA. Renew Energy 162:712–726

    Article  Google Scholar 

  34. Fattahi R, Khalilzadeh M (2018) Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment. Saf Sci 102:290–300

    Article  Google Scholar 

  35. Zhang C, Chen C, Streimikiene D, Balezentis T (2019) Intuitionistic fuzzy MULTIMOORA approach for multi-criteria assessment of the energy storage technologies. Appl Soft Comput 79:410–423

    Article  Google Scholar 

  36. Zavadskas EK, Antucheviciene J, Razavi Hajiagha SH, Hashemi SS (2015). The interval-valued intuitionistic fuzzy MULTIMOORA method for group decision making in engineering Mathematical Problems in Engineering, 2015

  37. Zhao H, You JX, Liu HC (2017) Failure mode and effect analysis using MULTIMOORA method with continuous weighted entropy under interval-valued intuitionistic fuzzy environment. Soft Comput 21(18):5355–5367

    Article  Google Scholar 

  38. Lv L, Li H, Wang L, Xia Q, Ji L (2019) Failure mode and effect analysis (FMEA) with extended MULTIMOORA method based on interval-valued intuitionistic fuzzy set: application in operational risk evaluation for infrastructure. Information 10(10):313

    Article  Google Scholar 

  39. Huang C, Lin M, Xu Z (2020) Pythagorean fuzzy MULTIMOORA method based on distance measure and score function: its application in multicriteria decision making process. Knowl Inf Syst 62(11):4373–4406

    Article  Google Scholar 

  40. Li XH, Huang L, Li Q, Liu HC (2020) Passenger satisfaction evaluation of public transportation using pythagorean fuzzy MULTIMOORA method under large group environment. Sustainability 12(12):4996

    Article  Google Scholar 

  41. Liang D, Darko AP, Zeng J (2020) Interval-valued pythagorean fuzzy power average-based MULTIMOORA method for multi-criteria decision-making. J Exper Theore Artificial Intel 32(5):845–874

    Article  Google Scholar 

  42. Liu P, Li D (2017) Some Muirhead mean operators for intuitionistic fuzzy numbers and their applications to group decision making. PLoS One 12(1):e0168767

    Article  Google Scholar 

  43. Qin Y, Cui X, Huang M, Zhong Y, Tang Z, Shi P (2020) Linguistic interval-valued intuitionistic fuzzy archimedean power muirhead mean operators for multiattribute group decision-making Complexity, 2020

  44. Xu W, Shang X, Wang J, Li W (2019) A novel approach to multi-attribute group decision-making based on interval-valued intuitionistic fuzzy power Muirhead mean. Symmetry 11(3):441

    Article  MATH  Google Scholar 

  45. Liu Y, Liu J, Qin Y (2020) Pythagorean fuzzy linguistic Muirhead mean operators and their applications to multiattribute decision-making. Int J Intell Syst 35(2):300–332

    Article  Google Scholar 

  46. Tang X, Wei G, Gao H (2019) Models for multiple attribute decision making with interval-valued pythagorean fuzzy muirhead mean operators and their application to green suppliers selection. Informatica 30(1):153–186

    Article  MATH  Google Scholar 

  47. Li L, Zhang R, Wang J, Zhu X, Xing Y (2018) Pythagorean fuzzy power Muirhead mean operators with their application to multi-attribute decision making. J Intell Fuzzy Syst 35(2):2035–2050

    Article  Google Scholar 

  48. Tang X, Wei G, Gao H (2019) Pythagorean fuzzy Muirhead mean operators in multiple attribute decision making for evaluating of emerging technology commercialization. Eco research-Ekonomska istraživanja 32(1):1667–1696

    Article  Google Scholar 

  49. Zhu J, Li Y (2018) Pythagorean fuzzy Muirhead mean operators and their application in multiple-criteria group decision-making. Information 9(6):142

    Article  Google Scholar 

  50. Cao Y, Li H, Su L (2020) Decision-making for project delivery system with related-indicators based on pythagorean fuzzy weighted muirhead mean operator. Information 11(9):451

    Article  Google Scholar 

  51. Xu Y, Shang X, Wang J (2018) Pythagorean fuzzy interaction Muirhead means with their application to multi-attribute group decision-making. Information 9(7):157

    Article  Google Scholar 

  52. Ali Z, Mahmood T (2020) Maclaurin symmetric mean operators and their applications in the environment of complex q-rung orthopair fuzzy sets. Comput Appl Math 39:1–27

    Article  MathSciNet  MATH  Google Scholar 

  53. Mahmood T, Ali Z (2021) Entropy measure and TOPSIS method based on correlation coefficient using complex q-rung orthopair fuzzy information and its application to multi-attribute decision making. Soft Comput 25(2):1249–1275

    Article  MATH  Google Scholar 

  54. Ali Z, Mahmood T, Yang MS (2020) Complex T-spherical fuzzy aggregation operators with application to multi-attribute decision making. Symmetry 12(8):1311

    Article  Google Scholar 

  55. Ali Z, Mahmood T, Yang MS (2020) TOPSIS method based on complex spherical fuzzy sets with Bonferroni mean operators. Mathematics 8(10):1739

    Article  Google Scholar 

  56. Mahmood T, Ullah K, Khan Q, Jan N (2019) An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets. Neural Comput & Applic 31(11):7041–7053

    Article  Google Scholar 

  57. Umetani, S., & Murakami, S. (2022). Coordinate descent heuristics for the irregular strip packing problem of rasterized shapes. European journal of operational research. 303(3), 1009-1026, 2022

  58. Juang CF, Chang CW, Hung TH (2021) Hand palm tracking in monocular images by fuzzy rule-based fusion of explainable fuzzy features with robot imitation application. IEEE Trans Fuzzy Syst 29(12):3594–3606

    Article  Google Scholar 

  59. Dong Y, Li X, Dezert J, Zhou R, Zhu C, Wei L, Ge SS (2021) Evidential reasoning with hesitant fuzzy belief structures for human activity recognition. IEEE Trans Fuzzy Syst 29(12):3607–3619

    Article  Google Scholar 

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Qi, X., Ali, Z., Mahmood, T. et al. An approach for combining MULTIMOORA method and Muirhead mean operators based on the complex Pythagorean fuzzy uncertain linguistic representation model. Appl Intell 53, 17561–17592 (2023). https://doi.org/10.1007/s10489-022-04408-0

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