Ranking of efficiency in context-dependent with non-discretionary

In has been widely recognized that data envelopment analysis (DEA) lacks discrimination power to distinguish between DEAefficient units. This paper proposes a new methodology for ranking decision making units ( DMUs ). The new methodology ranks DMUs by slack-based context-dependent with non-discretionary data. In the context-dependent DEA, The evaluation contexts are obtained by partitioning a set of DMUs into several levels of efficient frontiers. We assume the $DMU$s is divided into L levels, and o DMU belongs to level l (l=k+1,...,L) . We can obtain the efficiency of o DMU against each level, thereafter we can rank them. The numerical examples are investigated using the proposed ranking methodology to illustrate it.

Ranking of efficiency in context-dependent with non-discretionary Keywords:

Data Envelopment Analysis (DEA) , Context-dependent , Non-discretionary data , Ranking , Efficiency