Intelligently predict career path based on human resource data

Subject Areas : تحقیق در عملیات

Majid Riazi ¹ , Ghasem Tohidi ² , Mohammadali Keramati ³

1 - Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
2 - Department of Mathematics, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran

Received: 2022-06-29 Accepted : 2023-10-07 Published : 2025-04-08

Keywords: الگوریتم های فراابتکاری, شبکه عصبی-فازی نارکس, مسیر پیشرفت‌شغلی,

Abstract :

Lack of predictable career path for employees in different organizations will cause employee dissatisfaction and its consequences, including reduced productivity of the organization. For this purpose, various organizations must prepare and provide a specific career path for their employees from the beginning of employment. However, the emphasis on this procedure may, due to the extensive and dynamic changes in today’s business word, lead to the failure to achieve the initial career path set in organizations and in addition to creating various problems damage the reputation of the organization among employees. Therefore just as organizations manage the career path of their employees, they should take action to modify the career path of their employees on a scientific and presentable basis, according to the time changes. The most important step in this direction is to identify the possibility of employees being in the defined jobs of any organization at the right time. In this paper, by combining ANFIS neuro-fuzzy network and NARX neural network the use of NARX neuro-fuzzy network is presented as a new method for predicting the career path of employees and an experimental study has been performed on the NARX neuro-fuzzy network using optimized metaheuristic algorithms and proposed algorithm to determine the career path of an organization’s employees over 10 years. The results show that the model designed with NARX neuro-fuzzy network has better performance than the classical models.

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