一种基于Rough集理论的偏好规则获得方法研究

提出一种多准则方案对偏好程度粗度量的一般方法。构造了一种从参考方案集中获取偏好规则并用于全体方案排序和优选的方法。将参考方案对应的偏好关系定量分级表示，应用Rough粗集理论的近似原理对综合偏好关系进行粗近似以获取基于偏好信息的决策规则。计算实例表明新方法能充分表示决策者的偏好，有效避免了LERS系统产生的规则不一致性。

A Method of Measuring Preference and Deriving Rules in MCDM Based on Rough Sets Theory

A new method of rough measure of the preference degree between action pairs in MCDM is presented based on the Rough Sets theory. The preference degrees of reference action pairs are quantitatively represented by the sets of intervals derived from criteria value of reference action. The comprehensive preference relations of the reference action pairs are approximated respectively by the upper and the lower sets to derive the preference information potential in reference action set in terms of decision rules with no inconsistence found in LRES method. A computational study shows that this new method achieves significant improvement over the method based on the indiscernibility relation.