Pourkhodabakhsh Nima, Mamoudan Mobina Mousapour, Bozorgi-Amiri Ali
School of Advanced Sciences and Technology, College of Management, Islamic Azad University of Medical Sciences, Tehran, Iran.
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Appl Intell (Dordr). 2023;53(12):16309-16331. doi: 10.1007/s10489-022-04294-6. Epub 2022 Dec 5.
Employee turnover is one of the most important issues in human resource management, which is a combination of soft and hard skills. This makes it difficult for managers to make decisions. In order to make better decisions, this article has been devoted to identifying factors affecting employee turnover using feature selection approaches such as Recursive Feature Elimination algorithm and Mutual Information and Meta-heuristic algorithms such as Gray Wolf Optimizer and Genetic Algorithm. The use of Multi-Criteria Decision-Making techniques is one of the other approaches used to identify the factors affecting the employee turnover in this article. Our expert has used the Best-Worst Method to evaluate each of these variables. In order to check the performance of each of the above methods and to identify the most significant factors on employee turnover, the results are used in some machine learning algorithms to check their accuracy in predicting the employee turnover. These three methods have been implemented on the human resources dataset of a company and the results show that the factors identified by the Mutual Information algorithm can show better results in predicting the employee turnover. Also, the results confirm that managers need a support tool to make decisions because the possibility of making mistakes in their decisions is high. This approach can be used as a decision support tool by managers and help managers and organizations to have a correct insight into the departure of their employees and adopt policies to retain and optimize their employees.
员工流动率是人力资源管理中最重要的问题之一，它是软技能和硬技能的结合。这使得管理者难以做出决策。为了做出更好的决策，本文致力于使用递归特征消除算法和互信息等特征选择方法以及灰狼优化器和遗传算法等元启发式算法来识别影响员工流动率的因素。多准则决策技术的使用是本文用于识别影响员工流动率因素的其他方法之一。我们的专家使用最佳 - 最差方法对每个变量进行评估。为了检验上述每种方法的性能并确定对员工流动率影响最显著的因素，将结果用于一些机器学习算法中，以检验它们在预测员工流动率方面的准确性。这三种方法已在一家公司的人力资源数据集上实施，结果表明互信息算法识别出的因素在预测员工流动率方面能显示出更好的结果。此外，结果证实管理者需要一个决策支持工具，因为他们决策时犯错的可能性很高。这种方法可以被管理者用作决策支持工具，帮助管理者和组织正确洞察员工离职情况，并采取政策来留住和优化员工。
