Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision making units (DMUs) on the basis of multiple inputs and outputs. The context-dependent DEA is introduced to measure the relative attractiveness of a particular DMU when compared to others. In real-world situation, because of incomplete or non-obtainable information, the data (Input and ...

This paper introduces discretionary imprecise data in Data Envelopment Analysis (DEA) and discusses the efficiency evaluation of Decision Making Units (DMUs) with non-discretionary imprecise data. Then, suggests a method for evaluation the efficiency of DMUs with non-discretionary imprecise data. When some inputs and outputs are imprecise and non-discretionary, the DEA model becomes non-linear ...

This article integrates fuzzy set theory in Data Envelopment Analysis (DEA) framework to compute technical efficiency scores when input and output data are imprecise. In conventional DEA inputs and outputs data are precise. However, traffic and transportation take place in an uncertain environment and input and output data might be imprecise. This article proposes a possibility approach for sol...

One of the most popular approaches to measuring productivity changes is based on using Malmquist productivity indexes. In this paper we propose a method for obtaining interval Malmquist productivity index (IMPI). The classical DEA models have been before used for measuring the Malmquist productivity index. The current article extends DEA models for measuring the interval Malmquist productivity ...

There exist many different ranking methods for ranking efficient Decision Making Units (DMUs) in Data Envelopment Analysis (DEA). However, since each of these methods considers a certain theory for ranking, they may give different ranks. In practice, choosing a ranking method, the results of which the Decision Maker (DM) would be able to trust is an important issue. In this paper, we consider s...

Vendors play a key role in their company's success, so having a vendor's performance evaluation system in order to identify strengths, areas of improvement and profitability in any company is essential. In this paper, first data envelopment analysis (DEA) which is a technique to evaluate the performance of decision-making units (DMUs) is studied. Then the efficiency scores of 66 automotive vend...

This paper addresses the potential uncertainty in Cook (2007)’s methodology to classify dual-role factors by introducing a useful property. In addition, if the assumption that dual-role factors can be clearly classified into either inputs or outputs, which underlies Cook (2007), doesn't hold, an extension of DEA model incorporating dual-role factors is proposed in this paper. Then a theorem is ...

In this paper, we compare the properties of the traditional additive-based data envelopment analysis (hereafter, referred to as DEA) models and propose two generalized DEA models, i.e., the big M additive-based DEA (hereafter, referred to as BMA) model and the big M additive-based super-efficiency DEA (hereafter, referred to as BMAS) model, to evaluate the performance of the biomass power plant...

This paper applies Data Envelopment Analysis (DEA) and Elasticity Analysis to an empirical assessment study on innovation ability and innovation potentiality. According to inputs and outputs data of these innovative enterprises, we build CCR model, BCC model and Elasticity model to measure their innovation ability and innovation potentiality. The results show that innovation ability is varied b...

Traditional DEA models deal with measurements of relative efficiency of DMUs regarding multiple-inputs vs. multiple-outputs. One of the drawbacks of these models is the neglect of intermediate products or linking activities. After pointing out needs for inclusion of them in DEA models, we propose a slacks-based network DEA model that can deal with intermediate products. Using this model we can ...