A Hybrid MCDM Model for Personnel Evaluation: Integrating AHP, Fuzzy TOPSIS and VIKOR with Composite Scoring

Year 2025, Volume: 2 Issue: 1, 44 - 71, 14.07.2025

Hüseyin Ali Sarikaya

Abstract

Personnel selection is a vital strategic function for businesses that work in knowledge-intensive and public-facing sectors, such as science centers. Traditional evaluation methodologies frequently fail to capture the inherent ambiguity and subjectivity in candidate assessments. To address these constraints, this study offers a robust hybrid fuzzy multi-criteria decision-making (MCDM) framework that incorporates the Analytic Hierarchy Process (AHP), Fuzzy TOPSIS, and Fuzzy VIKOR. Furthermore, a unique aggregation mechanism known as the Fuzzy Composite Ranking Score (FCRS) is developed to combine the outputs of Fuzzy TOPSIS and Fuzzy VIKOR into a single ranking score that balances proximity to the ideal solution with compromise among conflicting criteria.
The proposed methodology was applied to a real-world case study at the Konya Science Center (KSC), where seven full-time and seven part-time candidates were evaluated based on seven criteria. The AHP method was used to derive weights for each criterion through expert pairwise comparisons, which were validated through consistency ratio analysis. Expert linguistic evaluations of candidates were converted into triangular fuzzy numbers and processed through Fuzzy TOPSIS and VIKOR models. The final rankings produced by FCRS demonstrated a high degree of agreement with individual method results while addressing contradictions between them.
The results show that the proposed hybrid framework can allow nuanced, transparent, and robust personnel selection decisions in complicated organizational situations. The model is scalable and adaptable to a wide range of decision-making scenarios involving human judgment in uncertain environments.

Keywords

Hybrid MCDM, Personnel selection under uncertainty, AHP-based criteria weighting, Fuzzy TOPSIS–VIKOR fusion, Fuzzy composite ranking score

Personel Değerlendirmesi için Hibrit Bir MCDM Modeli: AHP, Bulanık TOPSIS ve VIKOR'un Bileşik Puanlama ile Entegre Edilmesi

Abstract

Personel seçimi, bilim merkezleri gibi bilgi yoğun ve kamuya dönük sektörlerde çalışan işletmeler için hayati bir stratejik işlevdir. Geleneksel değerlendirme metodolojileri, aday değerlendirmelerindeki doğal belirsizliği ve öznelliği yakalamakta sıklıkla başarısız olmaktadır. Bu kısıtlamaları ele almak için, bu çalışma Analitik Hiyerarşi Süreci (AHP), Bulanık TOPSIS ve Bulanık VIKOR'u içeren sağlam bir hibrit bulanık çok kriterli karar verme (ÇKKV) çerçevesi sunmaktadır. Ayrıca, Bulanık TOPSIS ve Bulanık VIKOR çıktılarını, çatışan kriterler arasında uzlaşma ile ideal çözüme yakınlığı dengeleyen tek bir sıralama puanında birleştirmek için Bulanık Bileşik Sıralama Puanı (FCRS) olarak bilinen benzersiz bir toplama mekanizması geliştirilmiştir.
Önerilen metodoloji, Konya Bilim Merkezi'nde (KSC) yedi tam zamanlı ve yedi yarı zamanlı adayın yedi kritere göre değerlendirildiği gerçek bir vaka çalışmasına uygulanmıştır. AHP yöntemi, tutarlılık oranı analizi ile doğrulanan uzman ikili karşılaştırmaları yoluyla her bir kriter için ağırlıklar türetmek için kullanılmıştır. Adayların uzman dilbilimsel değerlendirmeleri üçgen bulanık sayılara dönüştürülmüş ve Bulanık TOPSIS ve VIKOR modelleri aracılığıyla işlenmiştir. FCRS tarafından üretilen nihai sıralamalar, aralarındaki çelişkileri ele alırken bireysel yöntem sonuçlarıyla yüksek derecede uyum göstermiştir.
Sonuçlar, önerilen hibrit çerçevenin karmaşık organizasyonel durumlarda incelikli, şeffaf ve sağlam personel seçim kararlarına olanak sağlayabileceğini göstermektedir. Model ölçeklenebilir ve belirsiz ortamlarda insan muhakemesini içeren çok çeşitli karar verme senaryolarına uyarlanabilir.

Keywords

Hibrit MCDM, Belirsizlik altında personel seçimi, AHP tabanlı kriter ağırlıklandırması, Bulanık TOPSIS–VIKOR füzyonu, Bulanık bileşik sıralama puanı

References

• Abdulvahitoglu, A., & Kilic, M. (2022). A new approach for selecting the most suitable oilseed for biodiesel production; the integrated AHP-TOPSIS method. Ain Shams Engineering Journal, 13(3), 101604. https://doi.org/10.1016/j.asej.2021.10.002
• Aggarwal, A., Sharma, I., Kukreja, V., Verma, T., & Aggarwal, R. (2025). Assessing and ranking the skills required for IT personnel: a hybrid decision-making model using fuzzy AHP-TOPSIS. Global Knowledge, Memory and Communication, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/GKMC-05-2024-0253
• Ayadi, H., Hamani, N., Kermad, L., & Mounir, B. (2021). Novel Fuzzy Composite Indicators for Locating a Logistics Platform under Sustainability Perspectives. Sustainability. https://doi.org/10.3390/su13073891
• Aytekin, A., Ecer, F., Korucuk, S., & Karamaşa, Ç. (2022). Global innovation efficiency assessment of EU member and candidate countries via DEA-EATWIOS multi-criteria methodology. Technology in Society, 68, 101896. https://doi.org/10.1016/j.techsoc.2022.101896
• Bakioglu, G., & Atahan, A. O. (2021). AHP integrated TOPSIS and VIKOR methods with Pythagorean fuzzy sets to prioritize risks in self-driving vehicles. Applied Soft Computing, 99, 106948. https://doi.org/10.1016/j.asoc.2020.106948
• Büyüközkan, G., & Çifçi, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39(3), 3000–3011. https://doi.org/10.1016/j.eswa.2011.08.162
• Chen, C.-T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1–9. https://doi.org/10.1016/S0165-0114(97)00377-1
• Chen, S.-J., & Hwang, C.-L. (1992). Fuzzy Multiple Attribute Decision Making Methods. In S.-J. Chen & C.-L. Hwang (Eds.), Fuzzy Multiple Attribute Decision Making: Methods and Applications (pp. 289–486). Springer. https://doi.org/10.1007/978-3-642-46768-4_5
• Chung, C., Trinh, V., & Tran, N. (2024). An Integrated Approach of Fuzzy AHP-TOPSIS for Multi-Criteria Decision-Making in Industrial Robot Selection. Processes. https://doi.org/10.3390/pr12081723.
• Daus, M., Weber, D., & Glaser, R. (2023). Application of Fuzzy Composite Programming in a Questionnaire as a Methodological Test to Study the Effect of Reservoir Management on Social Interests—A Survey Based on Two Case Studies in Southern Germany. Environmental Management, 71(6), 1145–1161. https://doi.org/10.1007/s00267-023-01799-9
• Dursun, M., & Karsak, E. (2010). A fuzzy MCDM approach for personnel selection. Expert Syst. Appl., 37, 4324–4330. https://doi.org/10.1016/j.eswa.2009.11.067
• Ertugrul Karsak, E. (2001). Personnel Selection Using a Fuzzy MCDM Approach Based on Ideal and Anti-ideal Solutions. In M. Köksalan & S. Zionts (Eds.), Multiple Criteria Decision Making in the New Millennium (pp. 393–402). Springer. https://doi.org/10.1007/978-3-642-56680-6_36
• Gottwald, D., Chocholáč, J., Kayacı Çodur, M., Čubranić-Dobrodolac, M., & Yazir, K. (2024). Z-Numbers-Based MCDM Approach for Personnel Selection at Institutions of Higher Education for Transportation. Mathematics, 12(4), Article 4. https://doi.org/10.3390/math12040523
• Govindan, K., Mina, H., Esmaeili, A., & Gholami-Zanjani, S. M. (2020). An Integrated Hybrid Approach for Circular supplier selection and Closed loop Supply Chain Network Design under Uncertainty. Journal of Cleaner Production, 242, 118317. https://doi.org/10.1016/j.jclepro.2019.118317
• Gul, M., & Guneri, A. F. (2016). A fuzzy multi criteria risk assessment based on decision matrix technique: A case study for aluminum industry. Journal of Loss Prevention in the Process Industries, 40, 89–100. https://doi.org/10.1016/j.jlp.2015.11.023
• Hu, J., Ma, J., & Zhu, G. (2022). A fuzzy rough number extended AHP and VIKOR for failure mode and effects analysis under uncertainty. Adv. Eng. Informatics, 51, 101454. https://doi.org/10.1016/j.aei.2021.101454.
• Jain, V., Sangaiah, A. K., Sakhuja, S., Thoduka, N., & Aggarwal, R. (2018). Supplier selection using fuzzy AHP and TOPSIS: A case study in the Indian automotive industry. Neural Computing and Applications, 29(7), 555–564. https://doi.org/10.1007/s00521-016-2533-z
• Kahraman, C., Cebeci, U., & Ruan, D. (2004). Multi-attribute comparison of catering service companies using fuzzy AHP: The case of Turkey. International Journal of Production Economics, 87(2), 171–184. https://doi.org/10.1016/S0925-5273(03)00099-9
• Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of Objective Weights Using a New Method Based on the Removal Effects of Criteria (MEREC). Symmetry, 13(4), Article 4. https://doi.org/10.3390/sym13040525
• Konacoglu, J., & Albayrak, I. (2018). A new fuzzy decision making approach for personnel selection problem. Intelligent Decision Technologies, 12(4), 471–482. https://doi.org/10.3233/IDT-180350
• Krishankumar, R., Premaladha, J., Ravichandran, K. S., Sekar, K. R., Manikandan, R., & Gao, X. Z. (2020). A novel extension to VIKOR method under intuitionistic fuzzy context for solving personnel selection problem. Soft Computing, 24(2), 1063–1081. https://doi.org/10.1007/s00500-019-03943-2
• Kurnia, D., Sudrajat, D., Rahaningsih, N., Rinaldi, A., & Pratama, F. (2019). The selection of candidate of call center operator 112 using analytical hierarchy process method. Journal of Physics: Conference Series, 1360, 012015. https://doi.org/10.1088/1742-6596/1360/1/012015
• Kusumawardani, R. P., & Agintiara, M. (2015). Application of Fuzzy AHP-TOPSIS Method for Decision Making in Human Resource Manager Selection Process. Procedia Computer Science, 72, 638–646. https://doi.org/10.1016/j.procs.2015.12.173
• Liou, J. J. H., & and Tzeng, G.-H. (2012). Comments on “Multiple criteria decision making (MCDM) methods in economics: An overview.” Technological and Economic Development of Economy, 18(4), 672–695. https://doi.org/10.3846/20294913.2012.753489
• Mathew, M., Chakrabortty, R. K., & Ryan, M. J. (2020). A novel approach integrating AHP and TOPSIS under spherical fuzzy sets for advanced manufacturing system selection. Engineering Applications of Artificial Intelligence, 96, 103988. https://doi.org/10.1016/j.engappai.2020.103988
• Milojkovic, I., & Prascevic, N. (2024). Project management using the developed AHP–VIKOR method with the fuzzy approach. Water Science and Technology, 90(2), 578–597. https://doi.org/10.2166/wst.2024.204
• Nalbant, K. G. (2025). An Application of the Interval Type-2 Fuzzy TOPSIS Method for the Selection of Project Managers. Engineering Management Journal, 1–14. https://doi.org/10.1080/10429247.2025.2492617
• Opricovic, S., & Tzeng, G.-H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. https://doi.org/10.1016/S0377-2217(03)00020-1
• Opricovic, S. (2011). Fuzzy VIKOR with an application to water resources planning. Expert Systems with Applications, 38(10), 12983–12990. https://doi.org/10.1016/j.eswa.2011.04.097
• Prusak, A. Strojny, J., Stefanów, P., & Machaj, K. (2015). The AHP-based assessment of public services with respect to different groups of customers of polish local government. Chinese Business Review, 14(11). https://doi.org/10.17265/1537-1506/2015.11.004
• Russo, R. de F. S. M., & Camanho, R. (2015). Criteria in AHP: A Systematic Review of Literature. Procedia Computer Science, 55, 1123–1132. https://doi.org/10.1016/j.procs.2015.07.081
• Saaty, T. L. (1980). The Analytic Hierarchy Process. McGraw–Hill.
• Saaty, R. W. (1987). The analytic hierarchy process—What it is and how it is used. Mathematical Modelling, 9(3), 161–176. https://doi.org/10.1016/0270-0255(87)90473-8
• Salehi, K. (2016). An Integrated Approach of Fuzzy AHP and Fuzzy VIKOR for Personnel Selection Problem. Global Journal of Management Studies and Researches, 3(3), 89.
• Samanlioglu, F., Taskaya, Y. E., Gulen, U. C., & Cokcan, O. (2018). A Fuzzy AHP–TOPSIS-Based Group Decision-Making Approach to IT Personnel Selection. International Journal of Fuzzy Systems, 20(5), 1576–1591. https://doi.org/10.1007/s40815-018-0474-7
• Savalan, Ş., & Rouyendegh, B. (2022). An Integrated Fuzzy MCDM Hybrid Methodology to Analyze Agricultural Production. Sustainability. https://doi.org/10.3390/su14084835.
• Sequeira, M., Adlemo, A., & Hilletofth, P. (2023). A hybrid fuzzy-AHP-TOPSIS model for evaluation of manufacturing relocation decisions. Operations Management Research, 16(1), 164–191. https://doi.org/10.1007/s12063-022-00284-6
• Sharma, H., Singh, C., & Singh, K. (2024). Fuzzy TOPSIS and fuzzy VIKOR for parameter selection in technological competency.
• Singh, V., Kumar, V., & Singh, V. B. (2023). A hybrid novel fuzzy AHP-TOPSIS technique for selecting parameter-influencing testing in software development. Decision Analytics Journal, 6, 100159. https://doi.org/10.1016/j.dajour.2022.100159
• Soner, O., Celik, E., & Akyuz, E. (2017). Application of AHP and VIKOR methods under interval type 2 fuzzy environment in maritime transportation. Ocean Engineering, 129, 107–116. https://doi.org/10.1016/j.oceaneng.2016.11.010
• Taylan, O., Alamoudi, R., Kabli, M., AlJifri, A., Ramzi, F., & Herrera-Viedma, E. (2020). Assessment of Energy Systems Using Extended Fuzzy AHP, Fuzzy VIKOR, and TOPSIS Approaches to Manage Non-Cooperative Opinions. Sustainability, 12(7), 2745. https://doi.org/10.3390/su12072745
• Tran, N.-T., Trinh, V.-L., & Chung, C.-K. (2024). An Integrated Approach of Fuzzy AHP-TOPSIS for Multi-Criteria Decision-Making in Industrial Robot Selection. Processes, 12(8), Article 8. https://doi.org/10.3390/pr12081723
• Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of Operational Research, 169(1), 1–29. https://doi.org/10.1016/j.ejor.2004.04.028
• Velmurugan, K., Saravanasankar, S., Venkumar, P., Sudhakarapandian, R., & Bona, G. D. (2022). Hybrid fuzzy AHP-TOPSIS framework on human error factor analysis: Implications to developing optimal maintenance management system in the SMEs. Sustainable Futures, 4, 100087. https://doi.org/10.1016/j.sftr.2022.100087
• Yazdani, M., Torkayesh, A., Stević, Ž., Chatterjee, P., Ahari, S., & 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