in many applications, ranking of decision making units (dmus) is a problematic technical task procedure to decision makers in data envelopment analysis (dea), especially when there are extremely efficient dmus. in such cases, many dea models may usually get the same efficiency score for different dmus. hence, there is a growing interest in ranking techniques yet. the purpose of this paper is ra...

one of the difficulties of data envelopment analysis(dea) is the problem of deciency discriminationamong efficient decision making units(dmus) and hence, yielding large number of dmus as efficientones. the main purpose of this paper is to overcome this inability. one of the methods for rankingefficient dmus is minimizing the coefficient of variation (cv) for inputs-outputs weights. in this pap...

this article will address the extension of super efficiency method to rank the non-extreme efficient decision making units. many methodologies have introduced methods that can rank efficient units, amongst which, the super efficiency method due to its ability to provide meaningful geometrical as well as economic analyses has a significant place. but the common problem with all the super efficie...

Conventional data envelopment analysis (DEA) assists decision makers in distinguishing between efficient and inefficient decision making units (DMUs) in a homogeneous group. However, DEA does not provide more information about the efficient DMUs. One of the interesting research subjects is to discriminate between efficient DMUs. The aim of this paper is ranking all efficient (extreme and non-ex...

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...

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...

  We suggest a method for finding the non-dominated points of the production possibility set (PPS) with variable returns to scale (VRS) technology in data envelopment analysis (DEA). We present a multiobjective linear programming (MOLP) problem whose feasible region is the same as the PPS under variable returns to scale for generating non-dominated points. We demonstrate that Pareto solutions o...