Data envelopment analysis (DEA) is a widely used benchmarking technique. Its strength stems from the fact that it can include several inputs and outputs of not necessarily same type to evaluate efficiency scores. Indeed, aforesaid method based on mathematical optimization. This paper constructs second-order conic optimization problem unifying DEA models. Moreover, presents an algorithm solves f...

in this paper, we show that inverse data envelopment analysis (dea) models can be used to estimate output with fuzzy data for a decision making unit (dmu) when some or all inputs are increased and deficiency level of the unit remains unchanged.

data envelopment analysis (dea) has been widely studied in the literature since its inception with charnes, cooper and rhodes work in 1978. the methodology behind the classical dea method is to determine how much improvements in the outputs (inputs) dimensions is necessary in order to render them efficient. one of the underlying assumptions of this methodology is that the units consume and prod...

generally, a typical problem which is crucial in a manufacturing system is increasing the production rate.  to cope with the problem, different types of techniques are used in companies by trial and error which imposes high costs on them. using simulation as a tool for assessing the effect of alterations on the performance of the overall system might be significant. this paper considers a simul...

Supplier evaluation and selection is a critical decision process for companies in order to gain competitive advantage. Decision models focusing on risk minimization have recently received increasing attention in supplier selection literature. In this paper, risk factors, which incorporate qualitative as well as quantitative data, are employed in fuzzy data envelopment analysis for obtaining eff...

data envelopment analysis (dea) is a technique based on mathematical programming for evaluating the efficiency of homogeneous decision making units (dmus). in this technique, inefficient dmus are projected onto the frontier, which was constructed by the best performers. centralized resource allocation (cra) is a method in which all dmus are projected onto the efficient frontier through solving ...

Data Envelopment Analysis (DEA) is known as a nonparametric mathematical programming approach to productive efficiency analysis. In this paper we show that DEA can be alternatively interpreted as nonparametric least squares regression subject to shape constraints on frontier and sign constraints on residuals. This reinterpretation reveals the classic parametric programming model by Aigner and C...

Performance assessment is a central to the management process in any type of organization. In addition, making rational economical decisions improve organizational performance daunting task, as organization typically multi-faceted entity which rely on complex systems that use uncertain information. Data envelopment analysis (DEA) powerful quantitative tool makes multiple inputs and outputs obta...

This article combines two techniques: data envelopment analysis (DEA) and Factor analysis (FA) to data reduction in decision making units (DMU). Data envelopment analysis (DEA), a popular linear programming technique is useful to rate comparatively operational efficiency of decision making units (DMU) based on their deterministic (not necessarily stochastic) input–output data and factor analysi...