A DATA-DRIVEN MADM MODEL FOR PERSONNEL SELECTION AND IMPROVEMENT
开发了一个整合机器学习与多属性决策方法的数据驱动模型，利用实际评估数据帮助管理者更客观地选拔人员并支持其能力提升，并以中国食品公司的人力资源数据为例验证了模型的有效性。
Personnel selection and human resource improvement are characteristically multiple-attribute decision-making (MADM) problems. Previously developed MADM models have principally depended on experts’ judgements as input for the derivation of solutions. However, the subjectivity of the experts’ experience can have a negative influence on this type of decision-making process. With the arrival of today’s data-based decision-making environment, we develop a data-driven MADM model, which integrates machine learning and MADM methods, to help managers select personnel more objectively and to support their competency improvement. First, RST, a machining learning tool, is applied to obtain the initial influential significance-relation matrix from real assessment data. Subsequently, the DANP method is used to derive an influential significance-network relation map and influential weights from the initial matrix. Finally, the PROMETHEE-AS method is applied to assess the gap between the aspiration and current levels for every candidate. An example was carried out using performance data with evaluation attributes obtained from the human resource department of a Chinese food company. The results revealed that the data-driven MADM model could enable human resource managers to resolve the issues of personnel selection and improvement simultaneously, and can actually be applied in the era of big data analytics in the future.