Business Studies, University of Technology and Applied Sciences, Salalah, Oman.
Department of Management Studies, Government Engineering College Jhalawar, Jhalrapatan, Rajasthan, India.
Comput Intell Neurosci. 2022 Aug 27;2022:5613407. doi: 10.1155/2022/5613407. eCollection 2022.
Business development is dependent on a well-structured human resources (HR) system that maximizes the efficiency of an organization's human resources input and output. It is tough to provide adequate instructions for HR's unique task. In a time when the domestic labor market is still maturing, it is difficult for companies to make successful adjustments in HR structures to meet fluctuations in demand for human resources caused by shifting corporate strategies, operations, and size. Data on corporate human resources are often insufficient or inaccurate, which creates substantial nonlinearity and uncertainty when attempting to predict staffing needs, since human resource demand is influenced by numerous variables. The aim of this research is to predict the human resource demand using novel methods. Recurrent neural networks (RNNs) and grey wolf optimization (GWO) are used in this study to develop a new quantitative forecasting method for HR demand prediction. Initially, we collect the dataset and preprocess using normalization. The features are extracted using principal component analysis (PCA) and the proposed RNN with GWO effectively predicts the needs of HR. Moreover, organizations may be able to estimate personnel demand based on current circumstances, making forecasting more relevant and adaptive and enabling enterprises to accomplish their objectives via efficient human resource planning.
业务发展依赖于一个结构良好的人力资源 (HR) 系统，该系统最大限度地提高了组织人力资源投入和产出的效率。很难为 HR 的独特任务提供充分的说明。在国内劳动力市场仍在成熟的时期，企业很难成功调整 HR 结构，以应对因企业战略、运营和规模变化而导致的人力资源需求波动。企业人力资源数据往往不足或不准确，这在尝试预测人员需求时会产生很大的非线性和不确定性，因为人力资源需求受到许多变量的影响。本研究的目的是使用新方法预测人力资源需求。本研究使用递归神经网络 (RNN) 和灰狼优化 (GWO) 开发了一种新的人力资源需求预测定量预测方法。首先，我们使用归一化方法收集数据集并进行预处理。使用主成分分析 (PCA) 提取特征，并使用带有 GWO 的建议 RNN 有效地预测人力资源需求。此外，组织可以根据当前情况估算人员需求，使预测更具相关性和适应性，使企业能够通过有效的人力资源规划实现其目标。
