Approaches to determining the relative importance weights for cross‐efficiency aggregation in data envelopment analysis

Cross‐efficiency evaluation has been widely used for identifying the most efficient decision making unit (DMU) or ranking DMUs in data envelopment analysis (DEA). Most existing approaches for cross‐efficiency evaluation are focused on how to determine input and output weights uniquely, but pay little attention to the aggregation process of cross‐efficiencies and simply aggregate them equally without considering their relative importance. This paper focuses on aggregating cross‐efficiencies by taking into consideration their relative importance and proposes three alternative approaches to determining the relative importance weights for cross‐efficiency aggregation. Numerical examples are examined to show the importance and necessity of the use of relative importance weights for cross‐efficiency aggregation and the most efficient DMU can be significantly affected by taking into consideration the relative importance weights of cross‐efficiencies.