A Model for Analyzing Employees Career Path Based on Neuro-Fuzzy Network and Simulated Annealing Algorithm

Abstract

In today's business world, the competitiveness and survival of any organization depends on the availability of human resources appropriate to the jobs of that organization, so having an intelligent career path model helps organizations to analyze career path of employees scientifically and efficiently in the best possible time and meet their manpower needs more quickly. In this paper, we present a model for analyzing the career path of employees using neuro-fuzzy network and simulated annealing meta-heuristic algorithm. in the experimental study the outputs of model show potential of each employee to be placed in different jobs of organization, to analyze the career path of employees with the model obtained 10-year data of the employees of a transportation company in Tehran was used. Finally, by using defuzzification a percentage is determined for each of the outputs which predicts the potential of each employee to be placed in organizational jobs and helps the organization to appointment of employees in different jobs. For optimization the simulated annealing algorithm has been compared with the genetic algorithm and ant colony optimization algorithm, that the simulated annealing algorithm shows better results. the presented model has very good prediction accuracy.