data envelopment analysis (dea) is an eciency measurement tool for evaluation of similar decision making units (dmus). in dea, weights are assigned to inputs and outputs and the absolute eciency score is obtained by the ratio of weighted sum of outputs to weighted sum of inputs. in traditional dea models, this measure is also equivalent with relative eciency score which evaluates dmus in com...

   The performance of a decision making unit (DMU) can be evaluated in either across-sectional or a time-series manner, and data envelopment analysis (DEA) is a useful method for both types of evaluation. The Malmquist productivity index (MPI) evaluates the change in efficiency of a DMU between two time periods. It is defined as the product of the Catch-up and Frontier-shift terms. In this pape...

In this paper, we propose a novel method using multiple-objective programming problems to answer the following question: if among a group of decision making units (DMUs), a subset of DMUs are required to merge and form a new DMU with specific input/output levels and a predefined efficiency target, how much should be the outputs/inputs of the merged DMU? This question answered according to the c...

Although discriminating between all efficient decision making units (DMUs) with identical efficiency is a very important subject in data envelopment analysis (DEA), it may not be an easy task, particularly when the decision maker wants to select one and only one efficient DMU among all. There are some papers that have proposed methods for finding an efficient DMU as the most efficient one but h...

data envelopment analysis (dea) is a method to measure relative efficiency of a set of decision-making units (dmus) which uses multiple inputs and produces multiple outputs. in the conventional dea, crisp inputs and outputs are fundamentally necessary. but the observed values of inputs and outputs in real-world problems are sometimes imprecise. thus, performance measurement often needs to be do...

Abstract In this study, variable returns to scale (VRS) data envelopment analysis was integrated into the Taguchi approach optimize ternary additives for expansive soil enhancement. The selected were sawdust ash (SDA), quarry dust (QD) and ordinary Portland cement (OPC). set as input variables while multiple responses obtained from experiments performed with orthogonal array output variables. E...

Recently, a mixed integer data envelopment analysis (DEA) model has been proposed to find the most BCC-efficient (or the best) decision making unit (DMU) by Toloo (2012). This paper shows that the model may be infeasible in some cases, and when the model is feasible, it may fail to identify the most efficient DMU, correctly. We develop an improved model to find the most BCC-efficient DMU that r...

In order to set up a framework dedicated to the transformation of Digital Mock-Ups (DMUs) into simulation models, this paper exposes the problematic of simulation assembly model preparation. It analyses the differences between standalone component and assembly preparations to justify the reasons of time consuming tasks specific to assemblies. To this end, the content of a DMU is analyzed to exp...