Data Envelopment Analysis (DEA) is widely used in banking sector to measure the efficiency of the banks. This paper evaluates the performance of the banks in India using cost, revenue and profit models of DEA and comes out with a comprehensive efficiency index for banks, by combing the efficiency scores of various DEA models, using the Shannon entropy. In general, the banks included in this stu...

data envelopment analysis (dea) measures the relative efficiency of decision making units (dmus) with multiple inputs and multiple outputs. dea-based malmquist productivity index measures the productivity change over time. we propose a dynamic dea model involving network structure in each period within the framework a dea. we have previously published the network dea (ndea) and the dynamic dea ...

Data envelopment analysis (DEA) is a non-parametric technique for evaluation of relative efficiency of decision making units described by multiple inputs and outputs. It is based on solving linear programming problems. Since 1978 when basic DEA model was introduced many its modifications were formulated. Among them are two or multi-stage models with serial or parallel structure often called net...

Data Envelopment Analysis (DEA) is one of the best methods for measuring the efficiency and productivity of Decision Making Units (DMU). Evaluating the efficiency of DMUs which have two or several stages by using the conventional DEA models, is equal to consider them as black box. This method, omits the effect of intermediate measure on efficiency. Therefore, just the first network inputs and t...

data envelopment analysis (dea) is a method for measuring the efficiency of peer decision making units (dmus) with multiple inputs and outputs. the traditional dea treats decision making units under evaluation as black boxes and calculates their efficiencies with first inputs and last outputs. this carries the notion of missing some intermediate measures in the process of changing the inputs to...

this paper uses integrated data envelopment analysis (dea) models to rank all extreme and non-extreme efficient decision making units (dmus) and then applies integrated dea ranking method as a criterion to modify genetic algorithm (ga) for finding pareto optimal solutions of a multi objective programming (mop) problem. the researchers have used ranking method as a shortcut way to modify ga to d...

Conventional data envelopment analysis (DEA) models are used to measure efficiency score of production systems when they are considered as black boxes and their internal relationship is ignored. This paper deals with a common special case of network systems which is called multi-stage production system and can be generalized to many organizations. A multi stage production system has some stages...

The topology of a network seriously affects its cost, reliability, throughput, and traffic pattern, etc, so we need to simultaneously consider these multiple criteria, which have different units, when evaluating network topologies. However, ordinary methods of network topology design considered only a single criterion. DEA (data envelopment analysis) enables us to simultaneously evaluate multip...

Cost efficiency measures the cost of resource by output production. While conventional cost efficiency models set targets separately for each DMU, There are cases where the Central decision making is seeking the above targets, and at the same tries to obtain the target of Min cost efficiency for the total consumption. in this paper we consider that there is a centralized decision maker (DM). In...

Cost efficiency (CE) evaluates the ability to produce current outputs at minimal cost, given its input prices. In ordinary CE model, the input prices are assumed to be definite. In recent years, various attempts have been made to measuring CE when the input prices are as trapezoidal fuzzy numbers. The main contribution of this paper is to provide a new approach for generalizing the CE of decisi...