Choosing weights for a complete ranking of DMUs in DEA and cross-evaluation

Abstract

Conventional data envelopment analysis (DEA) assists decision makers in distinguishing between efficient and inefficient decision making units (DMUs) in a homogeneous group. However, DEA does not provide more information about the efficient DMUs. One of the interesting research subjects is to discriminate between efficient DMUs. The aim of this paper is ranking all efficient (extreme and non-extreme) DMUs based on defining the new index which is obtained from basic definitions of models. The proposed method has been able to remove the existing deficiencies in some ranking methods and therefore makes a new contribution to DEA ranking.