A Broad Case-Based Distance Approach for Screening with Different Target Points

摘要

The case-based distance (CBD) methods for screening are helpful for assisting decision makers in filtering out alternatives that are unlikely to be chosen. However, most of these methods based on selected cases and distance measurements from a chosen target point can only solve screening problems with positive criteria weights. This study proposes a two-phase approach based on mixed integer programming to integrate the concept of Data Envelopment Analysis-Discriminant Analysis (DEA-DA) and the extended case-based distance (ECBD) method for screening problems involving uncertain signs of criteria weights and different target points. The results show that the proposed approach can reduce the misclassification rate and address multiple solution problems. In addition, because the proposed approach can solve problems involving negative weights directly, the influences of different target points can be reduced. Therefore, it is helpful for decision makers to conduct scenario analysis based on different chosen target points.