Optimizing Employee Performance Evaluation Using Sparse Neutrosophic AHP: Evidence from Tonekabon Electricity Distribution

Authors

https://doi.org/10.22105/tqfb.v2i3.67

Abstract

Employee performance evaluation constitutes a fundamental pillar of organizational effectiveness, especially in technical and service oriented sectors where human capital directly impacts operational reliability and customer satisfaction. Despite its strategic importance, conventional evaluation approaches rely largely on subjective judgments and often lack robustness in the face of uncertainty, incomplete information, and inconsistency in pairwise comparisons. To address these challenges, this study develops a Neutrosophic Sparse Analytic Hierarchy Process (NSAHP) framework that integrates Neutrosophic logic with dispersion regularization and enables simultaneous modeling of uncertainty, indeterminacy, and missing data. The proposed model is empirically applied to Tonekabon Electricity Distribution Company (TEDC) as a real world case study. The results show that through the systematic integration of expert judgments and the effective management of incomplete pairwise comparisons, the NSAHP framework generates stable, interpretable, and employee-friendly rankings. Comparative analyses against Fuzzy Analytic Hierarchy Process (FAHP) and Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) demonstrate the superiority of the proposed approach in terms of consistency, robustness to missing information, and computational efficiency. Experimental findings show a reduction in the average error of about 102 in five iterations, along with an approximate 35% improvement in computational performance compared to conventional fuzzy based models. Overall, this study contributes to the Multi Criteria Decision Making (MCDM) literature by demonstrating how advanced uncertainty aware methods can improve human resource evaluation systems and provide a reliable analytical basis for performance based managerial decision making.

Keywords:

Neutrosophic sparse analytic hierarchy process, Employee performance, Fuzzy analytic hierarchy process, Technique for order of preference by similarity to the ideal solution, Multi criteria decision making, Uncertainty modeling

References

  1. [1] Abdel-Basset, M., Gamal, A., Son, L. H., & Smarandache, F. (2020). A bipolar Neutrosophic multi criteria decision making framework for professional selection. Applied sciences, 10(4), 1202. https://doi.org/10.3390/app10041202
  2. [2] Abdel-Basset, M., Mohamed, R., Zaied, A. E. N. H., & Smarandache, F. (2021). Hybrid Neutrosophic decision-making models: A review. Applied soft computing, 101, 107040. https://doi.org/10.1016/j.asoc.2020.107040
  3. [3] Carlsson, C., & Fullér, R. (2001). On possibilistic distance and ranking of intervals. Fuzzy sets and systems, 120(3), 451–459. https://doi.org/10.1016/S0165-0114(99)00062-6
  4. [4] Zubair, S. A. M. (2023). Single-valued neutrosophic uncertain linguistic set based on multi-input relationship and semantic transformation. Journal of fuzzy extension and applications, 4(4), 257–270. https://doi.org/10.22105/jfea.2023.423474.1319
  5. [5] Saaty, T. L. (2003). Decision-making with the AHP: Why is the principal eigenvector necessary. European journal of operational research, 145(1), 85–91. https://doi.org/10.1016/S0377-2217(02)00227-8
  6. [6] Irvanizam, I., Zi, N. N., Zuhra, R., Amrusi, A., & Sofyan, H. (2020). An extended MABAC method based on triangular fuzzy Neutrosophic numbers for multiple-criteria group decision making problems. Axioms, 9(3), 104. https://doi.org/10.3390/axioms9030104
  7. [7] Banik, B., Chakraborty, A., Barman, A., & Alam, S. (2024). Multi-method approach for new vehicle purchasing problem through MCGDM technique under cylindrical Neutrosophic environment. Soft computing, 17. https://doi.org/10.1007/s00500-023-09520-y
  8. [8] Saaty, T. L. (1980). The analytic hierarchy process. European journal of operational research, 48, 9–26. https://d1wqtxts1xzle7.cloudfront.net/51627807
  9. [9] Ishizaka, A. (2014). Comparison of fuzzy logic, AHP, FAHP and hybrid fuzzy AHP for new supplier selection and its performance analysis. International journal of integrated supply management, 9(1–2), 1–22. https://doi.org/10.1504/IJISM.2014.064353
  10. [10] Ye, J. (2014). A multicriteria decision-making method using aggregation operators for simplified Neutrosophic sets. Journal of intelligent & fuzzy systems, 26(5), 2459–2466. https://doi.org/10.3233/IFS-130916
  11. [11] Saaty, T. L. (1980). The analytic hierarchy process (AHP). McGraw-Hill, New York. https://www.scirp.org/reference/ReferencesPapers?ReferenceID=1943982
  12. [12] Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
  13. [13] Radwan, N. M., Senousy, M. B., & Alaa El Din, M. R. (2016). Neutrosophic AHP multi criteria decision making method applied on the selection of learning management system. International journal of advancements in computing technology (IJACT), 8(5), 95–105. https://fs.unm.edu/neut/NeutrosophicAHP.pdf
  14. [14] Rachel, J., Devarasan, E., Razzaque, A., & Selvakumar, S. (2024). Enhancing biometric system selection: A hybrid AHP-Neutrosophic fuzzy TOPSIS approach. BMC research notes, 17(1), 263. https://doi.org/10.1186/s13104-024-06903-8
  15. [15] Ceballos, B., Lamata, M. T., & Pelta, D. A. (2016). A comparative analysis of multi-criteria decision-making methods. Progress in artificial intelligence, 5(4), 315–322. https://doi.org/10.1007/s13748-016-0093-1
  16. [16] Blavesteghi, I. X. (2024). Reconstruction of sparse dynamic networks via L1 regularization. https://doi.org/10.48550/arXiv.2403.05457
  17. [17] Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy sets and systems, 17(3), 233–247. https://doi.org/10.1016/0165-0114(85)90090-9
  18. [18] Bas, S. A. (2024). A hybrid approach based on consensus decision making for green supplier selection in automotive industry. Sustainability, 16(7), 3096. https://doi.org/10.3390/su16073096
  19. [19] Kao, J. C., Wang, C. N., Nguyen, V. T., & Husain, S. T. (2022). A fuzzy MCDM model of supplier selection in supply chain management. Intelligent automation & soft computing, 31(3), 1452–1466. http://dx.doi.org/10.32604/iasc.2022.021778
  20. [20] Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European journal of operational research, 95(3), 649–655. https://doi.org/10.1016/0377-2217(95)00300-2
  21. [21] Diethelm, K., & Ford, N. J. (2010). The analysis of fractional differential equations. Lecture notes in mathematics, 2004. https://doi.org/10.1006/jmaa.2000.7194
  22. [22] Hwang, C. L., & Yoon, K. (2012). Multiple attribute decision making: Methods and applications a state-of-the-art survey. Springer Science & Business Media. https://doi.org/10.1007/978-3-642-48318-9
  23. [23] Kaur, J., Kumar, R., Agrawal, A., & Khan, R. A. (2023). A Neutrosophic AHP-based computational technique for security management in a fog computing network. The journal of supercomputing, 79(1), 295–320. https://doi.org/10.1007/s11227-022-04674-2
  24. [24] Mohr, H., & Ruge, H. (2021). Fast estimation of L1-regularized linear models in the mass-univariate setting. Neuroinformatics, 19(3), 385–392. https://doi.org/10.1007/s12021-020-09489-1
  25. [25] Opricovic, S., & Tzeng, G. H. (2007). Extended VIKOR method in comparison with outranking methods. European journal of operational research, 178(2), 514–529. https://doi.org/10.1016/j.ejor.2006.01.020
  26. [26] Navarro, I. J., Martí, J. V., & Yepes, V. (2021). Neutrosophic completion technique for incomplete higher-order AHP comparison matrices. Mathematics, 9(5), 496. https://doi.org/10.3390/math9050496
  27. [27] Mohanta, K. K., & Sharanappa, D. S. (2023). Neutrosophic data envelopment analysis: A comprehensive review and current trends. Optimality, 1(1), 10–22. https://doi.org/10.22105/opt.v1i1.19
  28. [28] Smarandache, F. (2005). A unifying field in logics: Neutrosophic logic. neutrosophy, Neutrosophic set, Neutrosophic probability and statistics. Info learn quest. https://digitalrepository.unm.edu/math_fsp/163/
  29. [29] Smarandache, F. (2019). Neutrosophic set is a generalization. Journal of new theory, 29(2019), 01-35. https://arxiv.org/pdf/1911.07333
  30. [30] Azhar, N. A., Radzi, N. A. M., & Wan Ahmad, W. S. H. M. (2021). Multi-criteria decision making: A systematic review. Recent advances in electrical & electronic engineering (formerly recent patents on electrical & electronic engineering), 14(8), 779–801. https://doi.org/10.2174/2352096514666211029112443
  31. [31] Tey, D. J. Y., Gan, Y. F., Selvachandran, G., Quek, S. G., Smarandache, F., Son, L. H., … & Long, H. V. (2019). A novel Neutrosophic data analytic hierarchy process for multi-criteria decision making method: A case study in kuala lumpur stock exchange. IEEE access, 7, 53687–53697. https://doi.org/10.1109/ACCESS.2019.2912913
  32. [32] Triantaphyllou, E. (2000). Multi-criteria decision making methods. In Multi-criteria decision making methods: A comparative study (pp. 5–21). Boston, MA: Springer US. https://doi.org/10.1007/978-1-4757-3157-6_2
  33. [33] Xu, D. L., Zhang, W., & Chen, T. (2014). A survey of fuzzy multi-criteria decision making methods and applications. International journal of computational intelligence systems, 7(1), 255–272. https://doi.org/10.1080/18756891.2014.888689
  34. [34] Ye, J. (2016). Aggregation operators of Neutrosophic linguistic numbers for multiple attribute group decision making. SpringerPlus, 5(1), 1691. https://doi.org/10.1186/s40064-016-3247-5
  35. [35] DeNisi, A., & Smith, C. E. (2014). Performance appraisal, performance management, and firm-level performance: A review, a proposed model, and new directions for future research. Academy of management annals, 8(1), 127–179. https://doi.org/10.5465/19416520.2014.873178
  36. [36] Fatha, A. (2025). A comprehensive review of Neutrosophical statistics for data science. Journal of research in statistical sciences, 18(01), 25–40. https://doi.org/10.13052/jrss0974-8024.1812
  37. [37] Kara, K., Yalçın, G. C., Simic, V., Önden, İ., Edinsel, S., & Bacanin, N. (2024). A single-valued Neutrosophic-based methodology for selecting warehouse management software in sustainable logistics systems. Engineering applications of artificial intelligence, 129, 107626. https://doi.org/10.1016/j.engappai.2023.107626
  38. [38] Sánchez-Garrido, A. J., Navarro, I. J., & Yepes, V. (2021). Neutrosophic multi-criteria evaluation of sustainable alternatives for the structure of single-family homes. Environmental impact assessment review, 89, 106572. https://doi.org/10.1016/j.eiar.2021.106572
  39. [39] Sarucan, A., Emin Baysal, M., & Engin, O. (2021). Physician selection with a Neutrosophic multi-criteria decision making method. Intelligent and fuzzy techniques: Smart and innovative solutions (pp. 319–327). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-51156-2_38%0A%0A
  40. [40] Islam, R., Mazumdar, S., & Islam, R. (2024). An experiment on feature selection using logistic regression. 2024 5th information communication technologies conference (ICTC) (pp. 319–324). IEEE. https://doi.org/10.1109/ICTC61510.2024.10602330

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Published

2025-07-10

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Vol. 2 No. 3 (2025): Transactions on Quantitative Finance and Beyond

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How to Cite

Abdolmaleki, E., Edalatpanah, S. A., & Fakher, H. A. (2025). Optimizing Employee Performance Evaluation Using Sparse Neutrosophic AHP: Evidence from Tonekabon Electricity Distribution. Transactions on Quantitative Finance and Beyond, 2(3), 142-157. https://doi.org/10.22105/tqfb.v2i3.67
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