Neutrosophic Z-number analytic hierarchy process: A framework for enhanced group decision-making under uncertainty

Authors

  • Phi-Hung Nguyen * Research Center of Applied Sciences, Faculty of Business, FPT University, Hanoi, 100000, Vietnam. https://orcid.org/0000-0003-3372-8287
  • Lan-Anh Thi Nguyen Research Center of Applied Sciences, Faculty of Business, FPT University, Hanoi, 100000, Vietnam.
  • Tra-Giang Vu Research Center of Applied Sciences, Faculty of Business, FPT University, Hanoi, 100000, Vietnam.
  • Minh-Thang Phan Research Center of Applied Sciences, Faculty of Business, FPT University, Hanoi, 100000, Vietnam.
  • Van-Hien Ngo Research Center of Applied Sciences, Faculty of Business, FPT University, Hanoi, 100000, Vietnam.
  • Dieu-Linh Do Research Center of Applied Sciences, Faculty of Business, FPT University, Hanoi, 100000, Vietnam.

https://doi.org/10.48313/uda.v2i1.68

Abstract

Multi-Criteria Decision-Making (MCDM) often faces challenges in handling uncertainty, imprecision, and unreliable expert judgments, limiting the effectiveness of traditional methods like the Analytic Hierarchy Process (AHP). This study proposes the Neutrosophic Z-Number AHP (NZN-AHP) method to enhance decision-making by addressing these complexities. The NZN-AHP method integrates Neutrosophic Z-Numbers (NZNs), which model truth, indeterminacy, falsity, and reliability, with AHP’s structured pairwise comparison framework. Linguistic scales and advanced aggregation operators, such as Dombi and Aczel–Alsina, are employed to process expert evaluations, ensuring robust handling of uncertain data. The NZN-AHP method achieves consistent outcomes (CR < 0.1), outperforming traditional and fuzzy AHP by incorporating reliability and indeterminacy, thus providing more accurate prioritization of criteria in complex decision-making scenarios. NZN-AHP offers a versatile and precise framework for MCDM, effectively capturing multifaceted uncertainties and enhancing decision-making across domains like logistics, finance, and strategic planning. It sets a foundation for future research into integrating NZNs with other MCDM methods, advancing the field of decision sciences.

Keywords:

Neutrosophic Z-number sets, AHP method, Neutrosophic sets, Z-numbers, Expert consensus, Uncertainty

References

  1. [1] Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

  2. [2] Atanassov, K. T., & Stoeva, S. (1986). Intuitionistic fuzzy sets. Fuzzy sets and systems, 20(1), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3

  3. [3] Büyüközkan, G., & Göçer, F. (2017). Application of a new combined intuitionistic fuzzy MCDM approach based on axiomatic design methodology for the supplier selection problem. Applied soft computing journal, 52, 1222–1238. https://doi.org/10.1016/j.asoc.2016.08.051

  4. [4] Malik, M. G. A., Bashir, Z., Rashid, T., & Ali, J. (2018). Probabilistic hesitant intuitionistic linguistic term sets in multi-attribute group decision making. Symmetry, 10(9), 392. https://doi.org/10.3390/sym10090392

  5. [5] Yager, R. R. (2013). Pythagorean fuzzy subsets. 2013 joint IFSA world congress and nafips annual meeting (IFSA/NAFIPS) (pp. 57–61). IEEE. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608375

  6. [6] Ullah, K., Mahmood, T., Ali, Z., & Jan, N. (2020). On some distance measures of complex Pythagorean fuzzy sets and their applications in pattern recognition. Complex & intelligent systems, 6, 15–27. https://doi.org/10.1007/s40747-019-0103-6

  7. [7] Mahanta, J., & Panda, S. (2021). Distance measure for Pythagorean fuzzy sets with varied applications. Neural computing and applications, 33, 17161–17171. https://doi.org/10.1007/s00521-021-06308-9

  8. [8] Yager, R. R. (2016). Generalized orthopair fuzzy sets. IEEE transactions on fuzzy systems, 25(5), 1222–1230. https://doi.org/10.1109/TFUZZ.2016.2604005

  9. [9] Garg, H., Ali, Z., & Mahmood, T. (2021). Generalized dice similarity measures for complex q-Rung Orthopair fuzzy sets and its application. Complex and intelligent systems, 7(2), 667–686. https://doi.org/10.1007/s40747-020-00203-x

  10. [10] Tang, G., Chiclana, F., & Liu, P. (2020). A decision-theoretic rough set model with q-rung orthopair fuzzy information and its application in stock investment evaluation. Applied soft computing journal, 91, 106212. https://doi.org/10.1016/j.asoc.2020.106212

  11. [11] Cường, B. C. (2015). Picture fuzzy sets. Journal of computer science and cybernetics, 30(4), 409. https://doi.org/10.15625/1813-9663/30/4/5032

  12. [12] Göçer, F. (2021). A Novel interval value extension of picture fuzzy sets into group decision making: An approach to support supply chain sustainability in catastrophic disruptions. IEEE access, 9, 117080–117096. https://doi.org/10.1109/ACCESS.2021.3105734

  13. [13] Simić, V., Soušek, R., & Jovčić, S. (2020). Picture fuzzy MCDM approach for risk assessment of railway infrastructure. Mathematics, 8(12), 1–29. https://doi.org/10.3390/math8122259

  14. [14] Kutlu Gündoğdu, F., & Kahraman, C. (2020). A novel spherical fuzzy analytic hierarchy process and its renewable energy application. Soft computing, 24(6), 4607–4621. https://doi.org/10.1007/s00500-019-04222-w

  15. [15] Nguyen, P. H. (2022). Spherical fuzzy decision-making approach integrating Delphi and TOPSIS for package tour provider selection. Mathematical problems in engineering, 2022(1), 4249079. https://doi.org/10.1155/2022/4249079

  16. [16] Nguyen, P. H. (2023). A fully completed spherical fuzzy data-driven model for analyzing employee satisfaction in logistics service industry. Mathematics, 11(10), 2235. https://doi.org/10.3390/math11102235

  17. [17] Nguyen, P. H., Tsai, J. F., Dang, T. T., Lin, M. H., Pham, H. A., & Nguyen, K. A. (2021). A hybrid spherical fuzzy MCDM approach to prioritize governmental intervention strategies against the COVID-19 pandemic: A case study from Vietnam. Mathematics, 9(20), 2626. https://doi.org/10.3390/math9202626

  18. [18] Ashraf, S., & Attaullah. (2023). Decision analysis framework based on information measures of T-spherical fuzzy sets. In Fuzzy optimization, decision-making and operations research: theory and applications (pp. 435–471). Springer. https://doi.org/10.1007/978-3-031-35668-1_20

  19. [19] Al-Binali, T., Aysan, A. F., Dinçer, H., Unal, I. M., & Yüksel, S. (2023). New horizons in bank mergers: A quantum spherical fuzzy decision-making framework for analyzing islamic and conventional bank mergers and enhancing resilience. Sustainability, 15(10), 7822. https://doi.org/10.3390/su15107822

  20. [20] Smarandache, F. (2019). Refined neutrosophy and lattices vs. pair structures and Yin Yang bipolar fuzzy set. Mathematics, 7(4), 353. https://doi.org/10.3390/math7040353

  21. [21] Abdel-Basset, M., Manogaran, G., Gamal, A., & Smarandache, F. (2018). A hybrid approach of Neutrosophic sets and DEMATEL method for developing supplier selection criteria. Design automation for embedded systems, 22(3), 257–278. https://doi.org/10.1007/s10617-018-9203-6

  22. [22] Wang, H., Smarandache, F., Zhang, Y., & Sunderraman, R. (2012). Single valued Neutrosophic sets. Technical sciences and applied mathematics, 10. https://www.researchgate.net/publication/262047656_Single_valued_Neutrosophic_sets

  23. [23] Deli, I., & Şubaş, Y. (2017). A ranking method of single valued Neutrosophic numbers and its applications to multi-attribute decision making problems. International journal of machine learning and cybernetics, 8(4), 1309–1322. https://doi.org/10.1007/s13042-016-0505-3

  24. [24] Deli, I., & Subas, Y. (2014). Single valued Neutrosophic numbers and their applications to multicriteria decision making problem. Neutrosophic sets and systems, 2(1), 1–13. https://fs.unm.edu/SN/Neutro-SingleValuedNeutroNumbers.pdf

  25. [25] Lu, Z., & Ye, J. (2017). Exponential operations and an aggregation method for single-valued Neutrosophic numbers in decision making. Information, 8(2), 62. https://doi.org/10.3390/info8020062

  26. [26] Ullah, K., Garg, H., Mahmood, T., Jan, N., & Ali, Z. (2020). Correlation coefficients for T-spherical fuzzy sets and their applications in clustering and multi-attribute decision making. Soft computing, 24, 1647–1659. https://doi.org/10.1007/s00500-019-03993-6

  27. [27] Tsai, J. F., Tran, D. H., Nguyen, P. H., & Lin, M. H. (2023). Interval-valued hesitant fuzzy DEMATEL-based blockchain technology adoption barriers evaluation methodology in agricultural supply chain management. Sustainability, 15(5), 4686. https://doi.org/10.3390/su15054686

  28. [28] Nguyen, P. H., Thi Nguyen, L. A., Le, H. Q., & Tran, L. C. (2024). Navigating critical barriers for green bond markets using A fuzzy multi-criteria decision-making model: Case study in Vietnam. Heliyon, 10(13). https://www.cell.com/heliyon/fulltext/S2405-8440(24)09524-0

  29. [29] Nguyen, P. H., Thi Nguyen, L. A., Thi Nguyen, T. H., & Vu, T. G. (2024). Exploring complexities of innovation capability in Vietnam’s IT firms: Insights from an integrated MCDM model-based grey theory. Journal of open innovation: technology, market, and complexity, 10(3), 100328. https://doi.org/10.1016/j.joitmc.2024.100328

  30. [30] Nguyen, P. H., Nguyen, L. A. T., Pham, T. V., Nguyen, K. A., Nguyen, M. A. N., Nguyen, L. D. T., & Nguyen, L. T. (2024). Z-number based fuzzy MCDM models for analyzing non-traditional security threats to finance supply chains: A case study from Vietnam. Heliyon, 10(11). https://doi.org/10.1016/j.heliyon.2024.e31615

  31. [31] Zadeh, L. A. (2011). A note on Z-numbers. Information sciences, 181(14), 2923–2932. https://doi.org/10.1016/j.ins.2011.02.022

  32. [32] Yel, İ., Baysal, M. E., & Sarucan, A. (2023). A new approach to developing software projects by assigning teams to projects with interval-valued Neutrosophic Z numbers. Engineering applications of artificial intelligence, 126, 106984. https://doi.org/10.1016/j.engappai.2023.106984

  33. [33] Ye, J., Du, S., & Yong, R. (2022). Dombi weighted aggregation operators of Neutrosophic Z-numbers for multiple attribute decision making in equipment supplier selection. Intelligent decision technologies, 16(1), 9–21. https://doi.org/10.3233/IDT-200191

  34. [34] Ye, J., Du, S., & Yong, R. (2022). Aczel–alsina weighted aggregation operators of Neutrosophic Z-numbers and their multiple attribute decision-making method. International journal of fuzzy systems, 24(5), 2397–2410. https://doi.org/10.1007/s40815-022-01289-w

  35. [35] Yong, R., Ye, J., & Du, S. (2021). Multicriteria decision-making method and application in the setting of trapezoidal Neutrosophic Z-numbers. Journal of mathematics, 2021(1), 6664330. https://doi.org/10.1155/2021/6664330

  36. [36] Yel, İ., Sarucan, A., & Baysal, M. E. (2022). Determination of competencies with fuzzy multi-criteria decision making methods for determining the development program for analyst position in a participation bank [presentation]. Lecture notes in networks and systems (Vol. 504 LNNS, pp. 425–432). https://doi.org/10.1007/978-3-031-09173-5_51

  37. [37] Nguyen, P. H., Nguyen, L. A. T., Pham, T. V., Vu, T. G., Vu, D. M., Nguyen, T. H. T., … & Le, H. G. H. (2025). Mapping barriers to sustainable fashion consumption: Insights from Neutrosophic-Z number and Delphi-DEMATEL integration. Neutrosophic sets and systems, 80, 749–788. https://doi.org/10.5281/zenodo.14788319

  38. [38] Nguyen, P. H., Nguyen, L. A. T., Pham, T. V., Le, H. Q., Nguyen, T. H. T., Vu, T. G., & Le, H. G. H. (2025). Optimizing horizontal collaboration in logistics with Neutrosophic Z-number decision models. Neutrosophic sets and systems, 81, 306–342. file:///C:/Users/Administrator/Desktop/19OptimizingHorizontal.pdf

  39. [39] Karahan, M., & Yüzbaşıoğlu, M. A. (2021). Estimating re-evaluation of the risk report obtained using the altman Z-score model in mergers with Neutrosophic numbers. Neutrosophic sets and systems, 43, 54–60. https://digitalrepository.unm.edu/nss_journal/vol43/iss1/5/

  40. [40] Kamran, M., Salamat, N., Hameed, M. S., Khan, S. K., & Broumi, S. (2024). Sine trigonometric aggregation operators with single-valued Neutrosophic Z-numbers: Application in business site selection. Neutrosophic sets and systems, 63(1), 285–321. https://doi.org/10.5281/zenodo.10531836

  41. [41] Abdullah, S. I., De, K., Kalampakas, A., Samanta, S., & Allahviranloo, T. (2024). Social networks based on linguistic Z numbers and comparisons of centrality measures. In Management of uncertainty using linguistic z-numbers: applications for decision-making, granular computing and social networks (pp. 241–264). Springer. https://doi.org/10.1007/978-3-031-65854-9_14

  42. [42] Mohandes, S. R., Sadeghi, H., Fazeli, A., Mahdiyar, A., Hosseini, M. R., Arashpour, M., & Zayed, T. (2022). Causal analysis of accidents on construction sites: A hybrid fuzzy Delphi and DEMATEL approach. Safety science, 151, 105730. https://doi.org/10.1016/j.ssci.2022.105730

  43. [43] Du, S., Ye, J., Yong, R., & Zhang, F. (2021). Some aggregation operators of Neutrosophic Z-numbers and their multicriteria decision making method. Complex and intelligent systems, 7(1), 429–438. https://doi.org/10.1007/s40747-020-00204-w

  44. [44] Yazdani, M., Ebadi Torkayesh, A., Stević, Ž., Chatterjee, P., Asgharieh Ahari, S., & Doval Hernandez, V. (2021). An interval valued Neutrosophic decision-making structure for sustainable supplier selection. Expert systems with applications, 183, 115354. https://doi.org/10.1016/j.eswa.2021.115354

  45. [45] Ye, J. (2021). Similarity measures based on the generalized distance of Neutrosophic Z-number sets and their multi-attribute decision making method. Soft computing, 25(22), 13975–13985. https://doi.org/10.1007/s00500-021-06199-x

  46. [46] Nafei, A., Huang, C. Y., Javadpour, A., Garg, H., Azizi, S. P., & Chen, S. C. (2024). Neutrosophic fuzzy decision-making using TOPSIS and autocratic methodology for machine selection in an industrial factory. International journal of fuzzy systems, 26(3), 860–886. https://doi.org/10.1007/s40815-023-01640-9

  47. [47] Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International journal of services sciences, 1(1), 83–98. https://doi.org/10.1504/IJSSCI.2008.017590

  48. [48] Torfi, F., Farahani, R. Z., & Rezapour, S. (2010). Fuzzy AHP to determine the relative weights of evaluation criteria and Fuzzy TOPSIS to rank the alternatives. Applied soft computing journal, 10(2), 520–528. https://doi.org/10.1016/j.asoc.2009.08.021

  49. [49] Dinh, T. C. T., & Lee, Y. (2022). “I want to be as trendy as influencers” – how “fear of missing out” leads to buying intention for products endorsed by social media influencers. Journal of research in interactive marketing, 16(3), 346–364. https://doi.org/10.1108/JRIM-04-2021-0127

  50. [50] Gruodytė, E. (2022). Company requirements for selecting social media influencers for collaboration master’s final degree project [Thesis]. https://epubl.ktu.edu/object/elaba:131468894/

  51. [51] Kasumovic, D. (2024). The state of AI in influencer marketing: A comprehensive benchmark report. Influencer Marketing Hub. https://influencermarketinghub.com/ai-in-influencer-marketing/

  52. [52] KBV Research. (2024). Virtual influencer market size & analysis report to 2030. https://www.kbvresearch.com/virtual-influencer-market/

  53. [53] National Statistics Office. (2022). Statistical yearbook of 2022. https://www.nso.gov.vn/en/data-and-statistics/2023/06/statistical-yearbook-of-2022/

  54. [54] Vietnam E-Commerce Association (VECOM). (2023). Vietnam e-commerce business index report _ EBI 2023. http://en.vecom.vn/vietnam-e-commerce-business-index-report-ebi-2023

  55. [55] Decision Lab. (2023). The connected consumer Q4 2022. https://mmaglobal.com/files/documents/the_connect_consumer_q4_2022_0.pdf

  56. [56] GIZ, D. T. C. V. and. (2024). Promoting digital transformation with green transition 2023 annual report on vietnamese enterprises’ digital transformation. www.giz.de/vietnam

  57. [57] Xie-Carson, L., Magor, T., Benckendorff, P., & Hughes, K. (2023). All hype or the real deal? Investigating user engagement with virtual influencers in tourism. Tourism management, 99(2), 104779. https://doi.org/10.1016/j.tourman.2023.104779

  58. [58] Lou, C. (2022). Social media influencers and followers: Theorization of a trans-parasocial relation and explication of its implications for influencer advertising. Journal of advertising, 51(1), 4–21. https://doi.org/10.1080/00913367.2021.1880345

  59. [59] Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982

  60. [60] Mehra, A., Paul, J., & Kaurav, R. P. S. (2021). Determinants of mobile apps adoption among young adults: Theoretical extension and analysis. Journal of marketing communications, 27(5), 481–509. https://doi.org/10.1080/13527266.2020.1725780

  61. [61] Cross, R. L., & Israelit, S. (2021). Absorptive capacity: A new perspective on learning and innovation. Strategic learning in a knowledge economy, 57–86. https://doi.org/10.4324/9780080517889-9

  62. [62] Al-Rahmi, W. M., Yahaya, N., Aldraiweesh, A. A., Alamri, M. M., Aljarboa, N. A., Alturki, U., & Aljeraiwi, A. A. (2019). Integrating technology acceptance model with innovation diffusion theory: An empirical investigation on students’ intention to use e-learning systems. IEEE access, 7, 26797–26809. https://doi.org/10.1109/ACCESS.2019.2899368

  63. [63] Zailani, S., Iranmanesh, M., Nikbin, D., & Beng, J. K. C. (2015). Erratum to: Determinants of RFID adoption in Malaysia’s healthcare industry: Occupational level as a moderator. Journal of medical systems, 39(8), 1. https://doi.org/10.1007/s10916-015-0263-x

  64. [64] Huang, L. Y. (2004). A study about the key factors affecting users to accept chunghwa telecom’s multimedia on demand [Thesis].

  65. [65] Maduka, N. S., Edwards, H., Greenwood, D., Osborne, A., & Babatunde, S. O. (2018). Analysis of competencies for effective virtual team leadership in building successful organisations. Benchmarking, 25(2), 696–712. https://doi.org/10.1108/BIJ-08-2016-0124

  66. [66] Chatterjee, S., Rana, N. P., Dwivedi, Y. K., & Baabdullah, A. M. (2021). Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technological forecasting and social change, 170, 120880. https://doi.org/10.1016/j.techfore.2021.120880

  67. [67] Almahri, F. A. J., Bell, D., & Merhi, M. (2020). Understanding student acceptance and use of chatbots in the United Kingdom universities: A structural equation modelling approach. 2020 6th international conference on information management (ICIM) (pp. 284-288). IEEE. https://doi.org/10.1109/ICIM49319.2020.244712

  68. [68] Chatterjee, S., & Kumar Kar, A. (2020). Why do small and medium enterprises use social media marketing and what is the impact: Empirical insights from India. International journal of information management, 53, 102103. https://doi.org/10.1016/j.ijinfomgt.2020.102103

  69. [69] Rogers, E. M., Singhal, A., & Quinlan, M. M. (2014). Diffusion of innovations. In An integrated approach to communication theory and research (pp. 432–448). Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9780203887011-36/diffusion-innovations-everett-rogers-arvind-singhal-margaret-quinlan

  70. [70] [70] Yang, M. M. (2007). An exploratory study on consumers’ behavioral intention of usage of third generation mobile value-added services [Thesis].

  71. [71] Liu, J. (2021). Social robots as the bride? Understanding the construction of gender in a japanese social robot product. Human-machine communication, 2(1), 105–120. https://doi.org/10.30658/hmc.2.5

  72. [72] Ali Abbasi, G., Abdul Rahim, N. F., Wu, H., Iranmanesh, M., & Keong, B. N. C. (2022). Determinants of SME’s social media marketing adoption: Competitive Industry as a moderator. SAGE open, 12(1), 21582440211067220. https://doi.org/10.1177/21582440211067220

  73. [73] Oliveira, T., Thomas, M., & Espadanal, M. (2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information and management, 51(5), 497–510. https://doi.org/10.1016/j.im.2014.03.006

  74. [74] Awa, H. O., Baridam, D. M., & Nwibere, B. M. (2015). Demographic determinants of electronic commerce (EC) adoption by SMEs: A twist by location factors. Journal of enterprise information management, 28(3), 326–345. https://doi.org/10.1108/JEIM-10-2013-0073

  75. [75] Ifinedo, P. (2011). Internet/e-business technologies acceptance in Canada’s SMEs: An exploratory investigation. Internet research, 21(3), 255–281. https://doi.org/10.1108/10662241111139309

Published

2025-03-22

How to Cite

Nguyen, P.-H. ., Thi Nguyen, L.-A. ., Vu, T.-G., Phan, M.-T., Ngo, V.-H. ., & Do, D.-L. . (2025). Neutrosophic Z-number analytic hierarchy process: A framework for enhanced group decision-making under uncertainty. Uncertainty Discourse and Applications, 2(1), 76-98. https://doi.org/10.48313/uda.v2i1.68

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