Karar Verme Karmaşıklığının Çözülmesi: Yapay Zekâ ile Bulanık AHP-TOPSIS Karşılaştırması

Öz

Bilgi çağının gelişimi ve üretim süreçlerinin otomasyonunun artmasıyla birlikte yöneticiler karar alma ve planlama faaliyetlerini desteklemek için giderek daha fazla gelişmiş bilgi sistemlerine yönelmektedir. Bu sistemlerin en yeni ve dikkat çekici örneklerden biri ise ileri düzey bir sohbet botu olan ChatGPT’dir. Bu bağlamda araştırma, geleneksel bir çok kriterli karar verme yöntemi olan bulanık AHP-TOPSIS ile yapay zeka tabanlı karar verme yaklaşımlarının karşılaştırmalı bir analizini sunmaktadır. Bu yönüyle çalışma, her iki yöntemi bir araya getirerek örgütsel karar verme kalitesini artırmayı amaçlayan yeni bir çerçeve geliştirmeyi amaçlamaktadır. Ayrıca, işe alım kararlarına odaklanarak ChatGPT tarafından üretilen çıktıları geleneksel yöntemlerle karşılaştırmaktadır. Araştırma bulguları neticesinde, ChatGPT gibi yapay zeka tabanlı sistemlerin geleneksel çok kriterli karar verme yöntemlerine göre daha isabetli kararlar verdiğine ulaşılmıştır. Dahası, yapay zeka tarafından verilen kararların, şirketin halihazırda yaptığı seçimlerle büyük ölçüde uyum sağlayarak tahmin doğruluğunu ve işe alım süreçlerini optimize etme potansiyelini gözler önüne sermektedir.

Anahtar Kelimeler

• Yapay zekâ
• Karar verme
• Personel seçimi
• Bulanık AHP-TOPSIS

Unraveling Decision-Making Complexity: Artificial Intelligence versus Fuzzy AHP-TOPSIS

Öz

In the era of the information age and increasing automation of production processes, managers are increasingly relying on advanced information systems to support their decision-making and planning activities. Among these systems, the emergence of chatbots—specifically ChatGPT, a state-of-the-art conversational agent—represents a significant development. This research presents a comparative analysis of fuzzy AHP-TOPSIS, a traditional MCDM, and AI-based decision-making approaches. A novel framework integrating both methods has been developed to enhance the quality of organizational decision-making. The study focuses on recruitment decisions, comparing outputs generated by ChatGPT with those derived from traditional approaches. Findings reveal that artificial intelligence, as demonstrated by ChatGPT, delivers more accurate and reliable decisions than conventional MCDM’s. Moreover, these AI-generated decisions align closely with the actual selections made by the organization, showcasing their predictive accuracy and potential to optimize recruitment processes.

Anahtar Kelimeler

• Artificial intelligence
• Decision making
• Personnel selection
• Fuzzy AHP-TOPSIS

Kaynakça

1. Ablhamid, R. K., Santoso, B., & Muslim, M. A. (2013). Decision making and evaluation system for ewmployee recruitment using fuzzy analytic hierarchy process. International Refereed Journal of Engineering and Science, 2(7), 24-31.
2. Acar, C., Beskese, A., & Temur, G. T. (2018). Sustainability analysis of different hydrogen production options using hesitant fuzzy AHP. International Journal of Hydrogen Energy, 43(39), 18059-18076. https://doi.org/10.1016/j.ijhydene.2018.08.024
3. Afshari, R. A., Nikolic, M., & Cockalo, D. (2014).Applications of fuzzy decision making for personnel selection problem: A review. Journal of Engineering Management and Competitiveness (JEMC), 4(2), 68-77. https://doi.org/10.5937/jemc1402068A
4. Ağaç, G., & Baki, B. (2016). Sağlık alanında çok kriterli karar verme teknikleri kullanımı: Literatür incelemesi. Hacettepe Sağlık İdaresi Dergisi, 19(3), 343-363.
5. Alaaeldin, R., Asfoura, E., Kassem, G., & Abdel-Haq, M. S. (2021). Developing chatbot system to support decision making based on big data analytics. Journal of Management Information and Decision Sciences, 24(2), 1-15.
6. Aljanabi, M. (2023). ChatGPT: Future directions and open possibilities. Mesopotamian Journal of CyberSecurity, (2023), 16-17. https://doi.org/10.58496/MJCS/2023/003
7. Aljanabi, M., Ghazi, M., Ali, A. H., & Abed, S. A. (2023). ChatGpt: Open possibilities. Iraqi Journal for Computer Science and Mathematics, 4(1), 62-64. https://doi.org/10.52866/20ijcsm.2023.01.01.0018
8. Alp, S., & Gündoğdu, C. E. (2012). Kuruluş yeri seçiminde analitik hiyerarşi prosesi ve bulanık analitik hiyerarşi prosesi uygulaması. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 14(1), 7-25.

9. Antaki, F., Touma, S., Milad, D., El-Khoury, J., & Duval, R. (2023). Evaluating the Performance of ChatGPT in Ophthalmology: An Analysis of its Successes and Shortcomings. medRxiv, 2023-01. https://doi.org/10.1101/2023.01.22.23284882
10. Araujo, T., Helberger, N., Kruikemeier, S., & De Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & Society, 35, 611-623. https://doi.org/10.1007/s00146-019-00931-w
11. Arslan, E. T., & Demir, H. (2020). Yöneticilerin karar verme biçiminin çalışanların motivasyonu ve performansı üzerindeki etkisi. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 22(2), 115-131. https://doi.org/10.33707/akuiibfd.703174
12. Ayçin, E., & Aşan, H. (2021). İş zekâsı uygulamaları seçimindeki kriterlerin önem ağırlıklarının FUCOM yöntemi ile belirlenmesi. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 23(2), 195-208. https://doi.org/10.33707/akuiibfd.903563
13. Aydın, Ö., & Karaarslan, E. (2022). OpenAI ChatGPT generated literature review: Digital twin in healthcare . In: Aydın, Ö. (Ed.). Emerging Computer Technologies 2 (pp. 22-31). İzmir: İzmir Akademi Derneği Yayınevi. https://doi.org/10.2139/ssrn.4308687
14. Ayhan, M. B. (2013). A fuzzy AHP approach for supplier selection problem: A case study in a Gearmotor company. International Journal of Managing Value and Supply Chains (IJMVSC), 4(3), 11-23. https://doi.org/10.5121/ijmvsc.2013.4302
15. Baykal, N., & Beyan, T. (2004). Bulanık mantık: Uzman sistemler ve denetleyiciler. Bıçaklar Kitabevi.
16. Bektur, G. (2021). A hybrid fuzzy MCDM approach for sustainable project portfolio selection problem and an application for a construction company. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 23(2), 182-194. https://doi.org/10.33707/akuiibfd.911236
17. Boutkhoum, O., Hanine, M., Agouti, T., & Tikniouine, A. (2017). A decision-making approach based on fuzzy AHP-TOPSIS methodology for selecting the appropriate cloud solution to manage big data projects. International Journal of System Assurance Engineering and Management, 8, 1237-1253. https://doi.org/10.1007/s13198-017-0592-x
18. Cebeci, U. (2009). Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard. Expert systems with applications, 36(5), 8900-8909. https://doi.org/10.1016/j.eswa.2008.11.046
19. 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
20. 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
21. Chen, M. F., & Tzeng, G. H. (2004). Combining grey relation and TOPSIS concepts for selecting an expatriate host country. Mathematical and Computer Modelling, 40(13), 1473-1490. https://doi.org/10.1016/j.mcm.2005.01.006
22. Chen, C. T., Lin, C. T., & Huang, S. F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics, 102(2), 289-301. https://doi.org/10.1016/j.ijpe.2005.03.009
23. Chou, Y. C., Yen, H. Y., Dang, V. T., & Sun, C. C. (2019). Assessing the human resource in science and technology for Asian countries: Application of fuzzy AHP and fuzzy TOPSIS. Symmetry, 11(2), 251. https://doi.org/10.3390/sym11020251
24. Choudhary, D., & Shankar, R. (2012). An STEEP-fuzzy AHP-TOPSIS framework for evaluation and selection of thermal power plant location: A case study from India. Energy, 42(1), 510-521. https://doi.org/10.1016/j.energy.2012.03.010
25. Christensen, C. M., Raynor, M. & McDonald, R. (2015). What is disruptive innovation?. Harvard Business Review, 93(10), 44-53.
26. Cingöz, A. & Akdoğan, A. (2013). İnsan kaynakları yönetiminin stratejik bir boyut kazanması için gerçekleştirilen faaliyetlerin belirlenmesine yönelik bir araştırma. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 42, 91-122.
27. Çınar, N. T. (2011). Grup Kararı Vermede Kullanılan Bula-nık TOPSIS Yöntemleri ve Bir Uygulama: Banka Şube Yeri Seçimi. Sigma, 29(1), 11-24.
28. Dalbudak, E., & Rençber, Ö. F. (2022). Çok kriterli karar verme yöntemleri üzerine literatür incelemesi. Gaziantep Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 4(1), 1-17. https://doi.org/10.55769/gauniibf.1068692
29. Deliktaş, D., & Üstün, Ö. (2018). Multiple criteria decision making approach for industrial engineer selection using fuzzy AHP-fuzzy TOPSIS. Anadolu University Journal of Science and Technology A-Applied Sciences and Engineering, 19(1), 58-82. https://doi.org/10.18038/aubtda.326952
30. Dowling, M., & Lucey, B. (2023). ChatGPT for (finance) research: The Bananarama conjecture. Finance Research Letters, 103662. https://doi.org/10.1016/j.frl.2023.103662
31. Dumanoğlu, S., & Ergül, N. (2010). İMKB’de işlem gören teknoloji şirketlerinin mali performans ölçümü. Muhasebe ve Finansman Dergisi, (48), 101-111.
32. Ekşi, G. G. (2023). Kapsayıcı liderlik. Scientific Journal of Finance and Financial Law Studies, 3(1), 31-40.
33. Erdem, M. B. (2016). A fuzzy analytical hierarchy process application in personnel selection in it companies: A case study in a spin-off company. Acta Physica Polonica A, 130(1), 331-334. https://doi.org/10.12693/APhysPolA.130.331
34. Esmaili-Dooki, A., Bolhasani, P., & Fallah, M. (2017). An integrated fuzzy AHP and fuzzy TOPSIS approach for ranking and selecting the chief inspectors of bank: A case study. Journal of applied research on industrial engineering, 4(1), 8-23.
35. Frieder, S., Pinchetti, L., Griffiths, R. R., Salvatori, T., Lukasiewicz, T., Petersen, P. C., ... & Berner, J. (2023). Mathematical capabilities of ChatGPT. arXiv preprint arXiv: 2301.13867.
36. Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2022). Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers. bioRxiv, 2022-12. https://doi.org/10.1101/2022.12.23.521610
37. Gedik, Y. (2021). Endüstri 4.0 teknolojilerinin ve endüstri 4.0’ın üretim ve tedarik zinciri kapsamındaki etkileri: Teorik bir çerçeve. JOEEP: Journal of Emerging Economies and Policy, 6(1), 248-264.
38. Gilson, A., Safranek, C., Huang, T., Socrates, V., Chi, L., Taylor, R. A., & Chartash, D. (2022). How well does ChatGPT do when taking the medical licensing exams? The implications of large language models for medical education and knowledge assessment. medRxiv, 2022-12. https://doi.org/10.1101/2022.12.23.22283901
39. Gordijn, B., & Have, H. T. (2023). ChatGPT: evolution or revolution?. Medicine, Health Care and Philosophy, 1-2. https://doi.org/10.1007/s11019-023-10136-0
40. Gozalo-Brizuela, R., & Garrido-Merchan, E. C. (2023). ChatGPT is not all you need. A State of the Art Review of large Generative AI models. arXiv preprint arXiv: 2301.04655.
41. Guo, B., Zhang, X., Wang, Z., Jiang, M., Nie, J., Ding, Y., ... & Wu, Y. (2023). How close is ChatGPT to human experts? comparison corpus, evaluation, and detection. arXiv preprint arXiv: 2301.07597.
42. Güngör, Z., Serhadlıoğlu, G., & Kesen, S. E. (2009). A fuzzy AHP approach to personnel selection problem. Applied soft computing, 9(2), 641-646. https://doi.org/10.1016/j.asoc.2008.09.003
43. Heo, E., Kim, J., & Boo, K. J. (2010). Analysis of the assessment factors for renewable energy dissemination program evaluation using fuzzy AHP. Renewable and sustainable energy reviews, 14(8), 2214-2220. https://doi.org/10.1016/j.rser.2010.01.020
44. Hoon, G. K., Yong, L. J., & Yang, G. K. (2020). Interfacing chatbot with data retrieval and analytics queries for decision making. In: RITA 2018: Proceedings of the 6th International Conference on Robot Intelligence Technology and Applications (pp. 385-394). Springer. https://doi.org/10.1007/978-981-13-8323-6_32
45. Hwang, C.L., & Yoon, K. (1981). Methods for multiple attribute decision making. In: Multiple Attribute Decision Making. Lecture Notes in Economics and Mathematical Systems, vol 186. Springer. https://doi.org/10.1007/978-3-642-48318-9
46. Ishizaka, A., & Nemery, P. (2013). Multi-criteria decision analysis: Methods and software. John Wiley & Sons. https://doi.org/10.1002/9781118644898
47. Janjua, S., & Hassan, I. (2020). Fuzzy AHP-TOPSIS multi-criteria decision analysis applied to the Indus Reservoir system in Pakistan. Water Supply, 20(5), 1933-1949. https://doi.org/10.2166/ws.2020.103
48. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577-586. https://doi.org/10.1016/j.bushor.2018.03.007
49. Jiao, W., Wang, W., Huang, J. T., Wang, X., & Tu, Z. (2023). Is ChatGPT a good translator? A preliminary study. arXiv preprint arXiv: 2301.08745.
50. Kabir, G., & Hasin, M. A. A. (2011). Comparative analysis of AHP and fuzzy AHP models for multicriteria inventory classification. International Journal of Fuzzy Logic Systems, 1(1), 1-16.
51. Kamble, P. N., & Parveen, N. (2018). An application of integrated fuzzy AHP and fuzzy TOPSIS method for staff selection. J. Comput. Math. Sci, 9(9), 1161-1169. https://doi.org/10.29055/jcms/855
52. Karakış, E. (2019). Bulanık AHP ve bulanık TOPSIS ile bütünleşik karar destek modeli önerisi: Özel okullarda öğretmen seçimi. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, (53), 112-137. https://doi.org/10.18070/erciyesiibd.414655
53. Keskinkılıç, M., & Kuk, M. (2023). Eğitimde dijital dönüşüm ve EBA farkındalık düzeyinin belirlenmesi. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 25(1), 24-39. https://doi.org/10.33707/akuiibfd.1174281
54. Kılıç, C., & Atilla, G. (2024). Industry 4.0 and sustainable business models: An intercontinental sample. Business Strategy and the Environment. 33(4), 3142-3166. https://doi.org/10.1002/bse.3634
55. 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
56. Liu, Y., Eckert, C. M., & Earl, C. (2020). A review of fuzzy AHP methods for decision-making with subjective judgements. Expert Systems with Applications, 161, 113738. https://doi.org/10.1016/j.eswa.2020.113738
57. Mitrovic, S., Andreoletti, D., & Ayoub, O. (2023). ChatGPT or human? Detect and explain. explaining decisions of machine learning model for detecting short ChatGPT-generated text. arXiv preprint arXiv: 2301.13852.
58. Moayeri, M., Shahvarani, A., Behzadi, M. H., & Hosseinzadeh-Lotfi, F. (2015). Comparison of fuzzy AHP and fuzzy TOPSIS methods for math teachers selection. Indian Journal of Science and Technology, 8(13), 1-10. https://doi.org/10.17485/ijst/2015/v8i13/54100
59. Mutlu, M., & Sarı, M. (2017). Çok kriterli karar verme yöntemleri ve madencilik sektöründe kullanımı. Bilimsel Madencilik Dergisi, 56(4), 181-196. https://doi.org/10.30797/madencilik.391953
60. Myers, J. H., & Alpert, M. I. (1968). Determinant buying attitudes: Meaning and measurement. Journal of Marketing, 32(4), 13-20. https://doi.org/10.2307/1249332
61. Nazim, M., Mohammad, C. W., & Sadiq, M. (2022). A comparison between fuzzy AHP and fuzzy TOPSIS methods to software requirements selection. Alexandria Engineering Journal, 61(12), 10851-10870. https://doi.org/10.1016/j.aej.2022.04.005
62. Özdemir, Y., Nalbant, K. G., & Başlıgil, H. (2018). Personnel selection for promotion using an integrated fuzzy analytic hierarchy process-grey relational analysis methodology: A real case study. Anadolu University Journal of Science and Technology A-Applied Sciences and Engineering, 19(2), 278-292. https://doi.org/10.18038/aubtda.326726
63. Patil, S. K., & Kant, R. (2014). A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management adoption in Supply Chain to overcome its barriers. Expert Systems With Applications, 41(2), 679-693. https://doi.org/10.1016/j.eswa.2013.07.093
64. Perez-Soler, S., Guerra, E., & de Lara, J. (2018). Collaborative modeling and group decision making using chatbots in social networks. IEEE Software, 35(6),48-54. https://doi.org/10.1109/MS.2018.290101511
65. Pomerol, J. C. (1997). Artificial intelligence and human decision making. European Journal of Operational Research, 99(1), 3-25. https://doi.org/10.1016/S0377-2217(96)00378-5
66. Saad, S. M., Kunhu, N., & Mohamed, A. M. (2016). A fuzzy-AHP multi-criteria decision-making model for procurement process. International journal of logistics systems and management, 23(1), 1-24. https://doi.org/10.1504/IJLSM.2016.073295
67. Saatçioğlu, Ö. Y., Tuğdemir, G. K. & Özispa, N. (2018). Endüstri 4.0 ve lojistik sektörüne yansımalarının örnek olay kapsamında değerlendirilmesi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 23 (Endüstri 4.0 ve Örgütsel Değişim Özel Sayısı), 1675-1696.
68. Saaty, T.L. (1988). What is the analytic hierarchy process?. In: Mathematical Models for Decision Support, Mitra, G., Greenberg, H. J., Lootsma, F. A., Rijkaert, M. J., & Zimmermann, H. J. (Ed.). NATO ASI Series, vol 48. Springer. https://doi.org/10.13033/isahp.y1988.054
69. Sadiq, M., & Devi, V. S. (2022). A rough-set based approach for the prioritization of software requirements. International Journal of Information Technology, 14(1), 447-457. https://doi.org/10.1007/s41870-021-00749-0
70. Salomon, V. A. P., & Gomes, L. F. A. M. (2024). Consistency improvement in the analytic hierarchy process. Mathematics, 12(828). https://doi.org/10.3390/math12060828
71. 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, 1576-1591. https://doi.org/10.1007/s40815-018-0474-7
72. Sgarbossa, F., Grosse, E. H., Neumann, W. P., Battini, D., & Glock, C. H. (2020). Human factors in production and logistics systems of the future. Annual Reviews in Control, 49, 295-305. https://doi.org/10.1016/j.arcontrol.2020.04.007
73. Shrestha, Y. R., Ben-Menahem, S. M., & Von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66-83. https://doi.org/10.1177/0008125619862257
74. Shukla, R. K., Garg, D., & Agarwal, A. (2014). An integrated approach of Fuzzy AHP and Fuzzy TOPSIS in modeling supply chain coordination. Production & Manufacturing Research, 2(1), 415-437. https://doi.org/10.1080/21693277.2014.919886
75. Simon, H. A. (1955). A behavioral model of rational choice. The quarterly journal of economics, 69/1, 99-118. https://doi.org/10.2307/1884852
76. Sobania, D., Briesch, M., Hanna, C., & Petke, J. (2023). An analysis of the automatic bug fixing performance of ChatGPT. arXiv preprint arXiv: 2301.08653. https://doi.org/10.1109/APR59189.2023.00012
77. Soylu, A. (2018). Endüstri 4.0 ve girişimcilikte yeni yaklaşımlar. Pamukkale University Journal of Social Sciences Institute. 32, 43-57. https://doi.org/10.30794/pausbed.424955
78. Susnjak, T. (2022). ChatGPT: The end of online exam integrity?. arXiv preprint arXiv: 2212.09292.
79. Tebenkov, E., & Prokhorov, I. (2021). Machine learning algorithms for teaching AI chat bots. Procedia Computer Science, 190, 735-744. https://doi.org/10.1016/j.procs.2021.06.086
80. Tu, R., Ma, C., & Zhang, C. (2023). Causal-Discovery performance of ChatGPT in the context of neuropathic pain diagnosis. arXiv preprint arXiv: 2301.13819.
81. Varmazyar, M., & Nouri, B. (2014). A fuzzy AHP approach for employee recruitment. Decision Science Letters,3(1),27-36. https://doi.org/10.5267/j.dsl.2013.08.006
82. Venkatesh, V. G., Zhang, A., Deakins, E., Luthra, S., & Mangla, S. (2019). A fuzzy AHP-TOPSIS approach to supply partner selection in continuous aid humanitarian supply chains. Annals of Operations Research, 283, 1517-1550. https://doi.org/10.1007/s10479-018-2981-1
83. Wittstruck, D., & Teuteberg, F. (2012). Integrating the concept of sustainability into the partner selection process: a fuzzy-AHP-TOPSIS approach. International Journal of Logistics Systems and Management, 12(2), 195-226. https://doi.org/10.1504/IJLSM.2012.047221
84. Yılmaz, E. S., & Ecemiş, O. (2022). Investigation factors affecting competitive advantage in streaming industry with multi-criteria decision making methods. JOEEP: Journal of Emerging Economies and Policy, 7(1), 239-252.
85. Zanzotto, F. M. (2019). Human-in-the-loop artificial intelligence. Journal of Artificial Intelligence Research, 64, 243-252. https://doi.org/10.1613/jair.1.11345