LBWA, TOPSIS ve GİA Yöntemlerine Göre Personel Seçimi: Dış Ticaret Şirketi Üzerine Örnek Bir Çalışma

Abstract

İşe alım ve personel seçimi çeşitli faktörlerden etkilenmektedir. Dolayısıyla, personel seçimi, firmaların uzun vadede ayakta kalabilmesi için temel karar verme problemlerinden biridir. Bu çalışmanın amacı, Mersin'de faaliyet gösteren bir firmanın ihracat departmanı için LBWA-tabanlı TOPSIS ve GİA yöntemlerini kullanarak en uygun adayın seçilmesidir. Kriterler literatür taraması ve uzman görüşlerine göre belirlenmiştir. Kriterlerin ağırlıkları LBWA yöntemi ile hesaplanmış ve alternatifler (adaylar) TOPSIS ve GİA yöntemleri kullanılarak sıralanmıştır. LBWA sonuçları, yabancı dilde akıcılığın ve takım oyuncusunun sırasıyla en önemli ve en az önemli kriterler olduğunu göstermiştir. Her iki yöntemden (TOPSIS ve GRA) elde edilen sonuçlara göre, ilgili pozisyon için farklı adaylar önerilmiştir. Ayrıca, sonuçların geçerliliği ve sağlamlığı duyarlılık analizi kullanılarak test edilmiştir. Sonuç olarak, bu çalışmadan elde edilen bulguların personel seçim sürecinde yer alan karar vericilere ışık tutacağı düşünülmektedir.

Keywords

• Personel Seçimi
• Dış Ticaret
• LBWA
• TOPSIS
• GİA

Personnel Selection Based on the LBWA, TOPSIS and GRA Methods: A Case Study on Foreign Trade Company

Abstract

Recruitment and personnel selection are affected by significant factors. Thus, personnel selection is one of the main decision-making problems for a company’s long-term survival. The objective of this study is to identify the most suitable candidate for the export department of a company operating in Mersin, using the Level Based Weight Assessment (LBWA)-based Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) and Grey Relational Analysis (GRA) methods. The criteria were determined based on the literature review and experts’ opinions. The weight of criteria was calculated by the LBWA method, and the alternatives (candidates) were ranked using the TOPSIS and GRA methods. The LBWA results showed that fluency in a foreign language and team player were the most and least important criteria, respectively. The results from both methods (TOPSIS and GRA) suggested different candidates for the relevant positions. Furthermore, sensitivity analyses were conducted to assess the validity and robustness of the results. In conclusion, the findings of this study provide valuable insights to decision-makers involved in the personnel selection process.

Keywords

• Personnel Selection
• Foreign Trade
• LBWA
• TOPSIS
• GRA

References

1. Afshari, A., Mojahed, M. & Yusuff, R. M. (2010). Simple Additive Weighting Approach to Personnel Selection Problem. International Journal of Innovation, Management and Technology, 1(5), 511.
2. Altuntas, G. & Yildirim, B. F. (2022). Logistics Specialist Selection with Intuitionistic Fuzzy TOPSIS Method. International Journal of Logistics Systems and Management, 42(1), 1-34.
3. Andrejić, M. & Pajić, V. (2023). Optimizing Personnel Selection in Transportation: An Application of the BWM-CoCoSo Decision-Support Model. Journal of Organizations, Technology and Entrepreneurship, 1(1), 35-46.
4. Ayçin, E. (2020). Personel Seçim Sürecinde CRITIC ve MAIRCA Yöntemlerinin Kullanılması. İşletme, 1(1), 1-12.
5. Baležentis, A., Baležentis, T. & Brauers, W. K. (2012). Personnel Selection Based on Computing with Words and Fuzzy MULTIMOORA. Expert Systems with Applications, 39(9), 7961-7967.
6. Chang, K. L. (2015). The Use of a Hybrid MCDM Model for Public Relations Personnel Selection. Informatica, 26(3), 389-406.
7. Dadelo, S., Krylovas, A., Kosareva, N., Zavadskas, E. K. & Dadeliene, R. (2014). Algorithm of Maximizing the Set of Common Solutions for Several MCDM Problems and Its Application for Security Personnel Scheduling. International Journal of Computers Communications & Control, 9(2), 151-159.
8. Dağdeviren, M. (2010). A Hybrid Multi-Criteria Decision-Making Model for Personnel Selection in Manufacturing Systems. Journal of Intelligent Manufacturing, 21, 451-460.

9. Dai, J., Qi, J., Chi, J., Chen, S., Yang, J., Ju, L. & Chen, B. (2010). Integrated Water Resource Security Evaluation of Beijing Based on GRA and TOPSIS. Frontiers of Earth Science in China, 4, 357-362.
10. Danişan, T., Özcan, E. & Eren, T. (2022). Personnel Selection with Multi-Criteria Decision-Making Methods in the Ready-to-Wear Sector. Tehnički Vjesnik, 29(4), 1339-1347.
11. Demirci, A. (2022). Multi-Criteria Decision-Making Technique for Personnel Selection: PSI Sample. Toros Üniversitesi İİSBF Sosyal Bilimler Dergisi, 9(Special Issue 2nd International Symposium of Sustainable Logistics “Circular Economy”), 10-17.
12. Dursun, M. & Karsak, E. E. (2010). A Fuzzy MCDM Approach for Personnel Selection. Expert Systems with Applications, 37(6), 4324-4330.
13. Ebrahimi, E., Fathi, M. R. & Sobhani, S. M. (2023). A Modification of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) through Fuzzy Similarity Method (A Numerical Example of the Personnel Selection). Journal of Applied Research on Industrial Engineering, 10(2), 203-217.
14. Eroğlu, E., Yıldırım, B. F. & Özdemir, M. (2014). Çok Kriterli Karar Vermede “ORESTE” Yöntemi ve Personel Seçiminde Uygulanması. İstanbul Üniversitesi İşletme Fakültesi İşletme İktisadı Enstitüsü Yönetim Dergisi, 25(76).
15. Gençkaya, Ö., Gündoğdu, H. G., & Aytekin, A. (2021). Büyükşehir belediyeleri web sitelerinin yönetişim ilkeleri açısından değerlendirilmesi. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 16(3), 705-726.
16. Güngör, Z., Serhadlıoğlu, G. & Kesen, S. E. (2009). A Fuzzy AHP Approach to Personnel Selection Problem. Applied Soft Computing, 9(2), 641-646.
17. Hsu, C. I. & Wen, Y. H. (2000). Application of Grey Theory and Multiobjective Programming Towards Airline Network Design. European Journal of Operational Research, 127(1), 44-68.
18. Hwang, C. L. & Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Springer-Verlag, New York.
19. Ilgaz, A. (2018). Lojistik Sektöründe Personel Seçim Kriterlerinin AHP ve TOPSIS Yöntemleri ile Değerlendirilmesi. Süleyman Demirel Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 1(32), 586-605.
20. Kabak, M., Burmaoğlu, S. & Kazançoğlu, Y. (2012). A Fuzzy Hybrid MCDM Approach for Professional Selection. Expert Systems with Applications, 39(3), 3516-3525.
21. Karabašević, D., Stanujkić, D., Urošević, S. & Maksimović, M. (2016). An Approach to Personnel Selection Based on SWARA and WASPAS Methods. Bizinfo (Blace), 7(1), 1-11.
22. Karabasevic, D., Zavadskas, E. K., Stanujkic, D., Popovic, G. & Brzakovic, M. (2018). An Approach to Personnel Selection in the IT Industry Based on the EDAS Method. Transformations in Business & Economics, 17, 54-65.
23. Kelemenis, A. & Askounis, D. (2010). A New TOPSIS-Based Multi-Criteria Approach to Personnel Selection. Expert Systems with Applications, 37(7), 4999-5008.
24. Kenger, M. D., & Organ, A. (2017). Banka Personel Seçiminin Çok Kriterli Karar Verme Yöntemlerinden Entropi Temelli Aras Yöntemi ile Değerlendirilmesi. Adnan Menderes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 4(4), 152-170.
25. Korkmaz, O. (2019). Personnel Selection Method Based on TOPSIS Multi-Criteria Decision-Making Method. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (23), 1-16.
26. Kuo, Y., Yang, T. & Huang, G. W. (2008). The Use of Grey Relational Analysis in Solving Multiple Attribute Decision-Making Problems. Computers & Industrial Engineering, 55(1), 80-93.
27. Lu, H., Zhao, Y., Zhou, X. & Wei, Z. (2022). Selection of Agricultural Machinery Based on Improved CRITIC-Entropy Weight and GRA-TOPSIS Method. Processes, 10(2), 266.
28. Mercan, T. & Can, A. (2023). İşgören Seçiminde Etkili Olan Faktörlerin FUCOM Yöntemi ile Değerlendirilmesi: Bir Havayolu İşletmesinde Uygulama. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 14(40), 1311-1329.
29. Nguyen, P. H., Tsai, J. F., Kumar G, V. A. & Hu, Y. C. (2020). Stock Investment of Agriculture Companies in the Vietnam Stock Exchange Market: An AHP Integrated with GRA-TOPSIS-MOORA Approaches. The Journal of Asian Finance, Economics and Business, 7(7), 113-121.
30. Nyaoga, R., Magutu, P. & Wang, M. (2016). Application of Grey-TOPSIS Approach to Evaluate Value Chain Performance of Tea Processing Chains. Decision Science Letters, 5(3), 431-446.
31. Olson, D. L. (2004). Comparison of Weights in TOPSIS Models. Mathematical and Computer Modelling, 40(7-8), 721-727.
32. Özbek, A. (2015). Akademik Birim Yöneticilerinin MOORA Yöntemiyle Seçilmesi: Kırıkkale Üzerine Bir Uygulama. Erciyes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 1(38), 1-18.
33. Özbek, A. (2017). Çok Kriterli Karar Verme Yöntemleri ve Excel ile Problem Çözümü. Seçkin Yayıncılık, Ankara, 197.
34. Özcan, S., & Çelik, A. K. (2021). A comparison of TOPSIS, grey relational analysis and COPRAS methods for machine selection problem in the food industry of Turkey. International Journal of Production Management and Engineering, 9(2), 81-92.
35. Özdemir, A. I. & Deste, M. (2009). Gri İlişkisel Analiz ile Çok Kriterli Tedarikçi Seçimi: Otomotiv Sektöründe Bir Uygulama. İstanbul Üniversitesi İşletme Fakültesi Dergisi, 38(2), 147-156.
36. Ozgormus, E., Senocak, A. A. & Goren, H. G. (2021). An Integrated Fuzzy QFD-MCDM Framework for Personnel Selection Problem. Scientia Iranica, 28(5), 2972-2986.
37. Pamucar, D., Deveci, M., Canıtez, F. & Lukovac, V. (2020). Selecting an Airport Ground Access Mode Using Novel Fuzzy LBWA-WASPAS-H Decision Making Model. Engineering Applications of Artificial Intelligence, 93, 103703.
38. Pawlewicz, K., & Cieślak, I. (2022). The Use of Level Based Weight Assessment (LBWA) for Evaluating Public Participation on the Example of Rural Municipalities in the Region of Warmia and Mazury. Sustainability, 14(20), 13612.
39. Popović, M. (2021). An MCDM Approach for Personnel Selection Using the CoCoSo Method. Journal of Process Management and New Technologies, 9(3-4), 78-88.
40. Quan, H., Li, S., Wei, H., & Hu, J. (2019). Personalized product evaluation based on GRA-TOPSIS and Kansei engineering. Symmetry, 11(7), 867.
41. Roszkowska, E. (2011). Multi-Criteria Decision Making Models by Applying the TOPSIS Method to Crisp and Interval Data. Multiple Criteria Decision Making/University of Economics in Katowice, 6(1), 200-230.
42. Salgado, J. F. (2017). Personnel Selection. Oxford Research Encyclopedia of Psychology.
43. Şimşek, T. (2022). Personel Seçiminde Çok Kriterli Karar Verme Teknikleri Kullanımının İnsan Kaynakları Yönetimi Perspektifinden Değerlendirilmesi. Third Sector Social Economic Review, 57(3), 1514-1532.
44. Stanujkic, D., Popovic, G. & Brzakovic, M. (2018). An Approach to Personnel Selection in the IT Industry Based on the EDAS Method.
45. Stević, Ž. & Brković, N. (2020). A Novel Integrated FUCOM-MARCOS Model for Evaluation of Human Resources in a Transport Company. Logistics, 4(4), 1-14.
46. Sun, Y. & Cai, Y. (2021). A Flexible Decision-Making Method for Green Supplier Selection Integrating TOPSIS and GRA Under the Single-Valued Neutrosophic Environment. IEEE Access, 9, 83025-83040.
47. Torkayesh, A. E., Pamucar, D., Ecer, F., & Chatterjee, P. (2021). An integrated BWM-LBWA-CoCoSo framework for evaluation of healthcare sectors in Eastern Europe. Socio-Economic Planning Sciences, 78, 101052.
48. Uluskan, M., Topuz, D., & Çimen, C. (2022). AHP, Bulanık AHP, LBWA ve COPRAS Yöntemleri ile Tedarikçi Değerlendirme: Demiryolu Sektöründe Bir Uygulama. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 30(3), 412-430.
49. Ulutaş, A., Popovic, G., Stanujkic, D., Karabasevic, D., Zavadskas, E. K., & Turskis, Z. (2020). A new hybrid MCDM model for personnel selection based on a novel grey PIPRECIA and grey OCRA methods. Mathematics, 8(10), 1698.
50. Urosevic, S., Karabasevic, D., Stanujkic, D. & Maksimovic, M. (2017). An Approach to Personnel Selection in the Tourism Industry Based on the SWARA and the WASPAS Methods. Economic Computation & Economic Cybernetics Studies & Research, 51(1).
51. Uslu, Y. D., Yılmaz, E. & Yiğit, P. (2021). Developing Qualified Personnel Selection Strategies Using MCDM Approach: A University Hospital Practice. Strategic Outlook in Business and Finance Innovation: Multidimensional Policies for Emerging Economies (195-205). Emerald Publishing Limited.
52. Wang, P., Zhu, Z. & Wang, Y. (2016). A Novel Hybrid MCDM Model Combining the SAW, TOPSIS and GRA Methods Based on Experimental Design. Information Sciences, 345, 27-45.
53. Wu, H. H. (2002). A Comparative Study of Using Grey Relational Analysis in Multiple Attribute Decision Making Problems. Quality Engineering, 15(2), 209-217.
54. Yiğit, A. M., & Gök, M. (2017). Tire Selection with TOPSIS and GRA Methods in Multi Criteria Decision Making. Sosyal Bilimler Arastirmalari Dergisi, 7(3).
55. Zhang, S. F. & Liu, S. Y. (2011). A GRA-Based Intuitionistic Fuzzy Multi-Criteria Group Decision Making Method for Personnel Selection. Expert Systems with Applications, 38(9), 11401-11405.
56. Žižović, M. & Pamucar, D. (2019). New Model for Determining Criteria Weights: Level Based Weight Assessment (LBWA) Model. Decision Making: Applications in Management and Engineering, 2(2), 126-137.