Modelling and Analysis of Neuro Fuzzy Employee Ranking System in the Public Sector

Autor: Konstantinos C. Giotopoulos, Dimitrios Michalopoulos, Aristeidis Karras, Christos Karras, Spyros Sioutas
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Algorithms, Vol 16, Iss 3, p 151 (2023)
Druh dokumentu: article
ISSN: 1999-4893
DOI: 10.3390/a16030151
Popis: Human resource management has a significant influence on the performance of any public body. Employee classification and ranking are definitely time-consuming processes, which in many cases lead to controversial results. In addition, assessing employee efficiency through a variety of skills could lead to never-ending calculations and error-prone statistics. On the other hand, hard skill selection is proven to formulate a base for further investigation since subjectivity is not included in the performance equation. This research proposes a ranking model of employee selection based on certain criteria and attributes. The proposed prototype shows a series of results with a low error rate using ANFIS as the base methodology approach. This research was explanatory, and the population of this study consisted of employees with the majority of the sample in the wider region of Western Greece. The results showed a harmonic co-existence of the factors that proportionally affect the productivity of the employees in public service. Therefore, it provides the HR department with valuable information regarding the overall productivity of the public body, as well as significant material based on each profile separately. Therefore, efficiency was achieved through an automated time-saving procedure. The final output will enhance any personnel selection system with data extracted directly from the system, ensuring that the current method outperformed traditional approaches and secured a non-subjective procedure on employee management applied to the public sector.
Databáze: Directory of Open Access Journals
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