Most, if not all, production technologies are stochastic. This article demonstrates how data envelopment analysis (DEA) methods can be adapted to accommodate stochastic elements in a state-contingent setting. Specifically, we show how observations on a random input, not under the control of the producer and not known at the time that variable input decisions are made, can be used to partition t...

Abstract Data Envelopment Analysis (DEA) models give each decision making unit freedom to assign values the input-output weights maximising its efficiency score. However, there is no guarantee that optimal obtained from a DEA model are always positive which poses problem, in both theory and practice. This paper offers new linear programming deal with zero problem without restricting or placing ...

The classification of fuzzy uncertain data is considered one of the most challenging issues in data analysis. In spite of the significance of fuzzy data in mathematical programming, the development of the analytical methods of fuzzy data is slow. Therefore, the current study proposes a new fuzzy data classification method based on fuzzy data envelopment analysis (DEA) which can handle strea...