As data envelopment analysis (DEA) has been developed both in theory and application, the calculation of models become more and more important. Although many DEA software tools have been built for the calculation of the DEA models, there are some deficiencies in embedding them into enterprise management information system (MIS). As an extension of this work, an idea was generated in this paper,...

Data Envelopment Analysis (DEA) is one of the most widely used methods in the measurement efficiency and productivity of Decision Making Units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper introduces a neural network backpropagation Data Envelopment Analysis. Neural network requirements of computer memo...

Data Envelopment Analysis (DEA) is a technique for measuring the efficiency of a set of Decision Making Units (DMUs) with common data, but in general it is not practical. This paper presents a framework where DEA is used to measure overall profit efficiency with fuzzy data. Specifically, it is shown that as the inputs, outputs and price vectors are fuzzy numbers, the DMUs cannot be easily evalu...

In this paper, we discuss the relationship between multi-attribute utility theory and DEA models without explicit inputs (DEA-WEI), including dual models and some theoretical analysis of DEA-WEI models. We then propose generic DEA-WEI models with quadratic utility terms. Finally, we provide illustrative examples to show that DEA-WEI with suitable quadratic utility terms are able to reflect some...

Data Envelopment Analysis (DEA) is a relatively new “data oriented” non-parametric approach for evaluating the performance of complex entities called Decision Making Units (DMUs) which convert multiple inputs into multiple outputs. DEA as a linear programming procedure computes a comparative ratio of outputs to inputs for each DMU, which is reported as the relative efficiency score. In a relati...

In this paper, a Network DEA approach to assess the efficiency of NBA teams is proposed and compared with a black-box (i.e. single-process) DEA approach. Both approaches use a Slack-Based Measure of efficiency (SBM) to evaluate the potential reduction of inputs consumed (team budget) and outputs produced (games won by the team). The study considers the distribution of the budget between first-t...

Article history: Received 1 May 2009 Accepted 5 May 2010 Available online 9 May 2010

This study examines the efficiency of 12 Taiwanese laptop manufacturers in 2002. This work demonstrates the feasibility of adopting Data Envelopment Analysis (DEA) to identify individual manufacturing firms that are less efficient than other ones. Efficiency is measured in terms of output variables relative to input variables. For those efficient laptop manufacturers lacking of productivity, th...

Data envelopment analysis (DEA) is an effective method to evaluate the relative efficiency of decision-making units (DMUs). In one hand, the DEA models need accurate inputs and outputs data. On the other hand, in many situations, inputs and outputs are volatile and complex so that they are difficult to measure in an accurate way. The conflict leads to the researches of uncertain DEA models. Thi...

Amiodarone (AMD)-induced pulmonary toxicity (AIPT) is the most life-threatening side-effect of AMD treatment. N-Monodesethylamiodarone (DEA), an active metabolite of AMD, also exhibits cytotoxicity and tends to accumulate in the lung more intensively than AMD. In this study, we characterized the mechanism of DEA accumulation using A549 cells as a model of the alveolar epithelium. Typical ATP-de...