基于数值分布的激励型综合评价方法

针对激励评价中的等级划分问题,本文提出了一种基于数值分布的等级划分方法,相比于现有的等级划分方法,该方法能够综合考虑数值分布情况来划分等级,并结合本文提出的等级划分法对密度算子进行拓展,提出了一种基于数值分布的激励型综合评价方法。首先本文从数值分布的角度提出了一种新的等级划分方法,从而得出各等级区间的等级区间分界点;其次确定等级系数,并结合指标值和等级区间分界点给出各指标的权向量,给出一种不需要进行归一化处理的等级权向量确定方法,该方法能够较好的解决归一化处理带来的不公平性;再次根据密度算子思想对评价数据进行集结得出评价结果;最后通过一个算例对该方法进行验证,结果表明该方法可以实现对被评价对象科学激励的作用。该方法尤其适用于企业员工激励、省市综合排名、高校人才选拔等问题。

Information Aggregation Method with Incentive Function Based on Distribution of Values

Aiming at ranks division in incentive evaluation, a rank division method based on distribution of values is suggested in this paper. Compared with current rank division method, the situation of distribution of values is involved in this method, and then based on this method this paper extends traditional density operator and presents an information aggregation method with incentive function based on distribution of values. First, a novel rank division method is given from the point of distribution of values, and then cutting point of each rank is determined. Secondly rank factors are given, and we determine the attribute weights with values, ranks factors and cutting points, and then a method determining rank weight vector which does not need normalization is given. It can solve unfairness of normalization, so the final result is gotten by aggregating value. Finally, an example is used to check the method, and the result proves the method can have incentive objects scientifically and reasonably. The method can be used for a way of an incentive to staff in company, evaluation of provinces and cities, and selection of talents in school and so on.