Keywords: Data envelopment analysis Frontier analysis Resource allocation Target setting Common set of weights a b s t r a c t Data envelopment analysis (DEA) is a data-driven non-parametric approach for measuring the efficiency of a set of decision making units (DMUs) using multiple inputs to generate multiple outputs. Conventionally , DEA is used in ex… (More)
Keywords: Data envelopment analysis Fuzzy data Interval data Overall profit efficiency Profit Malmquist productivity index a b s t r a c t Although crisp data are fundamentally indispensable for determining the profit Malmquist productivity index (MPI), the observed values in real-world problems are often imprecise or vague. These imprecise or vague data… (More)
In this paper, we describe a new method for finding search directions for interior point methods (IPMs) in linear optimization (LO). The theoretical complexity of the new algorithms are calculated and we prove that the iteration bound is O(log(n//)) in this case too.
The conventional Data Envelopment Analysis (DEA) model considers Decision Making Units (DMUs) as a black box, meaning that these models do not consider the connection and the inner structures of DMUs. Moreover, these models consider that the activities of DMUs in each time are independent of other times, but in the real world, the inner structures of DMUs… (More)
Data Envelopment Analysis (DEA) is a technique for measuring the efficiency of a set of Decision Making Units (DMUs) with common data, but in general it is not practical. This paper presents a framework where DEA is used to measure overall profit efficiency with fuzzy data. Specifically, it is shown that as the inputs, outputs and price vectors are fuzzy… (More)