ALGORITHM OF MULTI-CRITERION ASSESSMENT AND DECISION-MAKING FOR PERSONNEL SELECTION BASED ON NEURAL NETWORKS
Abstract
The main challenge lies in designing mechanisms that assess not only formal attributes such as education and experience but also subjective factors, including motivation, creativity, and team work skills. The goal of the study is to develop a decision-making algorithm based on ANN for automating the evaluation process of potential candidates. By considering both qualitative and quantitative criteria, such as education, work experience, leadership, and adaptability, the proposed algorithm enhances objectivity and precision in forming optimal project teams. There search outlines a step-by-step methodology for implementing the ANN model. Initially, 19 evaluation criteria were identified, grouped in to categories including professional and personal competencies. A scoring system from 0 to 10 was employed for consistent and measurable assessments. Each candidate's profile was mapped onto a multidimensional vector space, with ANN used to classify and idate as "suitable" or "unsuitable" based on their overall score. To validate the approach, the algorithm was tested on simulated candidate data, demonstrating high classification accuracy. A practical implementation example is provided, detailing how the model can predict the suitability of candidates by processing their input vectors. The findings underscore the efficacy of using ANN for personnel evaluation, providing a scalable solution to optimize team composition. The system's adaptability allows for adjustments in criteria weightage based on specific organizational needs, making it a versatile tool for human resource management.
References
1. Azarova A., Puhach V. (2022) Otsiniuvannia rivnia upravlinnia liudskym kapitalom iz zastosuvanniam neironnoi merezhi Khemminha [Estimating the level of human capital management using the Hamming neural network]. Ekonomika ta suspilstvo. vol. 37. DOI: https://doi.org/10.32782/2524-0072/2022-37-7 (accessed October 11, 2024).
2. Akimenko V. Shtuchni neironni merezhi v zadachakh hrupuvannia ta analizu informatsii [Artificial neural network sintasks of information grouping and analysis]. Available at: https://csc.knu.ua/media/study/asp/art_net_group_ inf_akimenko/lecture/lec1.pdf (accessed October 11, 2024).
3. Boldyreva N. V., TikhonovaYa. M., Mishunina E., Khramtsov B. A. (2019) Suchasni tekhnolohii i metody otsinky personalu: pliusy i minusy [Modern technologies and methods of personnel evaluation: prosandcons.]. Visnyk instytutu svitovykh tsyvilizatsii. vol. 2(23), pp. 46–57. Available at: http://imc-i.ru/userfiles/ufiles/vestnik23.pdf (accessed October 11, 2024).
4. Honcharenko O. H. (2023) Metody otsinky personalu i yikh vplyv na efektyvnist diialnosti orhanizatsiinykh system [Personnel evaluation methods and their impacton the effectiveness of organizational systems.]. Naukovyi visnyk Sivershchyny. Seriia: Osvita. Sotsialni ta povedinkovi nauky. vol. 2 (11), pp. 63–75.
5. Informatsiina storinka arkhitektur neironnykh merezh [ Information page of neural network architectures.]. Available at: https://www.intuit.ru/studies/courses/6/6/lecture/178?page=3 (accessed October 6, 2024).
6. Medvediev V. S. (2012) Vykorystannia neironnykh merezh dlia normuvannia pratsi vyrobnychoho personalu [The use of neural networks to regulate the work of production personnel.]. Biznes-Inform, vol. 8, pp. 131–133.
7. Nadryhailo T. Zh., Molchanova K. A (2011) Analiz neironnykh alhorytmiv [Analysis of neural algorithms.]. Matematychne modeliuvannia, vol. 2 (25), pp. 46–52.
8. Syvolap L. A., Halitsyna A. O. (2021) Doslidzhennia suchasnykh metodiv otsinky personalu [Study of modern methods of personnel evaluation]. Efektyvna ekonomika, vol. 11. DOI: https://doi.org/10.32702/2307-2105-2021.11.24 (accessed November 16, 2024).
9. Skvorchevskyi O. Ye. (2016) Bahatokryterialna optymizatsiia protsesiv pidboru ta rozmishchennia personalu na pidpryiemstvi [Multi-criteria optimization of personnel selection and placement processes at the enterprise ] VisnykNats. tekhn. un-tu "KhPI": zb. nauk. pr. Ser. : Ekonomichni nauky. Kharkiv: NTU "KhPI", vol. 47 (1219), pp. 41–45.
10. Sokolovska V. V., Babchynska O. I., Ivanchenko H. V. (2019) Metody otsinky personalu: rol ta znachennia v upravlinni [Personnel evaluation methods: role and significance in management.]. Ahrosvit. vol. 20, pp. 93–98.
11. Shtuchni neironni merezhi [Artificial neural networks]. Available at: https://www.slideshare.net/LesiaSobolevska/ai-8-75922490 (accessed October 6, 2024).
