The evaluation of the ability increment of scientific researchers is the most direct method to test the effect of scientific research training. Considering the variety and high professionalism of scientific research positions, and in order to reduce the difficulty of expert evaluation and improve the operability and reliability of the evaluation methods, this paper presents an evaluation method that combines interval evaluation and cloud model theory based on the characteristics of ability increment in scientific researchers. Firstly, the interval weights of each evaluation index are determined based on the interval analytic hierarchy process (IAHP) method without depending on evaluation data. Secondly, the interval weights are converted into value weights based on the constant deviation ratio criterion to avoid cross-hierarchical uncertainty in the evaluation results due to the direct involvement of interval weights in subsequent evaluation. To reversely convert the interval evaluation results of indexes into a cloud model, the probability density function applicable to the interval indexes is analyzed, and the conversion formula from interval-valued evaluation indexes to a cloud model is directly provided based on probability theory. On this basis, this paper describes in detail the method for reversely analyzing the effectiveness of index evaluation based on the characteristics of the cloud model, thus achieving an effective combination of interval evaluation and the cloud model. Finally, effective evaluation of ability increment indexes of scientific researchers at all levels is accomplished by adopting a comprehensive cloud model and a gray cloud clustering method. The effectiveness of the proposed method in this paper is validated through simulation examples.
Yang et al. (Sat,) studied this question.