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Improved interval DEA models with common weight

Jiasen Sun;  Yajun Miao;  Jie Wu;  Lianbiao Cui;  Runyang Zhong — 2014

Kybernetika

The traditional data envelopment analysis (DEA) model can evaluate the relative efficiencies of a set of decision making units (DMUs) with exact values. But it cannot handle imprecise data. Imprecise data, for example, can be expressed in the form of the interval data or mixtures of interval data and exact data. In order to solve this problem, this study proposes three new interval DEA models from different points of view. Two examples are presented to illustrate and validate these models.

Evaluation of decision-making units based on the weight-optimized DEA model

Jiasen Sun;  Rui Yang;  Xiang Ji;  Jie Wu — 2017

Kybernetika

Data envelopment analysis (DEA) is a methodology for measuring best relative efficiencies of a group of peer decision-making units (DMUs) that take multiple inputs to produce multiple outputs. However, the traditional DEA model only aims to maximize the efficiency of the DMU under evaluation. This usually leads to very small weights (even zero weights) being assigned to some inputs or outputs. Correspondingly, these inputs or outputs have little or even no contribution to efficiency, which is unfair...

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