Abstract Data Envelopment Analysis (DEA) is a method for measuring efficiencies of Decision Making Units (DMUs). While it has been widely used in many industrial and economic applications, for large DMUs with many inputs and outputs, DEA would require huge computer resources in terms of memory and CPU time. Several studies have attempted to overcome this problem for large datasets. However, the...

Decision makers (DMs) face different levels of uncertainty throughout the decision making process. In particular, natural language is generally subjective or ambiguous when used to express perceptions and judgments. The aim of this paper is to extend the VIKOR method and develop a methodology for solving multi-criteria decision making (MCDM) problems with stochastic data. The weights of the sto...

The car manufacturing industry, one of the largest energy consuming industries, has been making a considerable effort to improve its energy intensity by implementing energy efficiency programs, in many cases supported by government research or financial programs. While many car manufacturers claim that they have made substantial progress in energy efficiency improvement over the past years thro...

The chief aim of this study was to analyze secondary healthcare providers' efficiency, focusing on the efficiency analysis of Slovene general hospitals. We intended to present a complete picture of technical, allocative, and cost or economic efficiency of general hospitals. METHODS We researched the aspects of efficiency with two econometric methods. First, we calculated the necessary quotien...

In this study, we adopt a three-stage approach, DEA method, Frontier regressions, and DEA method to estimate bank efficiency based on information obtained from 29 banks in Taiwan for the period from 2002 to 2004. In the first stage, we employ super SBM (super Slacks-Based Measure) to estimate the scores relating bank efficiency including internal risk. In the second stage, a stochastic frontier...

Understanding the effects of operational conditions and practices on productive efficiency can provide valuable economic and managerial insights. The conventional approach is to use a twostage method where the efficiency estimates are regressed on contextual variables representing the operational conditions. The main problem of the two-stage approach is that it ignores the correlations between ...

This paper proposes a new model which provides some insights on using Stochastic Data Envelopment Analysis (SDEA) technique in the future performance appraisal for similar units of an organization. This research contributes to advance current knowledge by proposing new model in which, regardless of having benefits of DEA, resolve some head problems of the context. For instance: 1) the efficienc...

Data Envelopment Analysis (DEA) is one of the most widely used technique for evaluating the relative efficiency of a set of homogeneous decision making units. Traditionally, it assumes that input and output variables are known in advance, ignoring the critical issue of data uncertainty. In this paper, we deal with the problem of efficiency evaluation under uncertain conditions by adopting the g...

Intensification of swine production in the U.S. and around the world requires advanced manure management. For swine manure management in the state of North Carolina, one system met all of the required advanced management criteria, and it was qualified as an environmentally superior technology. This investigation was part of the testing for this superior technology. The objectives of this invest...

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). Recently network DEA models been developed to examine the efficiency of DMUs with internal structures. The internal network structures range from a simple two-stage process to a complex system where multiple divisions are linked together with intermediate measures. In general, there ar...