Data envelopment analysis (DEA) has been widely applied in evaluating multicriteria decision making problems, which have multi-input and multi-output. However, the traditional DEA method does neither take the decision maker’s subjective preferences to the individual criteria into consideration nor rank the selected options or decision making units (DMUs). On the other hand, Satty’s analytical h...

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...

Analyzing warehouse performance across different environments is critical to improving overall productivity and reducing costs. Although two-stage DEA estimators have been shown to be statistically consistent, the finite sample bias of DEA in the first stage carries over to the secondstage regression, which causes bias in the estimated coefficients of the contextual variables. The bias is parti...

In recent years, determining an appropriate supplier has become a crucial strategic consideration in a competitive market – with data envelopment analysis (DEA) methods increasingly important in this respect. DEA traditionally requires that the values for all inputs and outputs be known exactly. However, this assumption may not be true, because data in many real applications cannot be precisely...

Decision-making based on expert’s opinion by data envelopment analysis (DEA) models requires crisp number. However, the assumption of expert might not always be accurate. This paper introduces fuzzy DEA credibility constrained and relative closeness (RC) index to cope with uncertainty data. The proposed approach transforms traditional events using measure. Furthermore, RC was used increase disc...

This paper proposes two quadratic-constrained DEA models for evaluation of mutual funds performance, from a perspective of evaluation based on endogenous benchmarks. In comparison to previous studies, this paper decomposes two vital factors for mutual funds performance, i.e. risk and return, in order to define mutual funds’ endogenous benchmarks and give insights and suggestions for managements...

 Recently, Khodabakhshi and Aryavash have introduced a ranking method [Applied Mathematics Letters, 25 (2012) 2066-2070.], which is based on an optimistic-pessimistic approach of data envelopment analysis (DEA). This method ranks all decision making units according to a combination of their minimum and maximum possible efficiency scores which are determined by solving two linear programming mo...

In data envelopment analysis (DEA), the curse of dimensionality problem may jeopardize accuracy or even relevance results when there is a relatively large dimension inputs and outputs, for samples. Recently, machine learning approach based on least absolute shrinkage selection operator (LASSO) variable was combined with sign-constrained convex nonparametric squares (SCNLS, special case DEA), du...

The economic development of China’s coastal areas is being constrained by resources and the environment, with sustainable development being the key to solving these problems. The data envelopment analysis (DEA) model is widely used to assess sustainable development. However, indicators used in the DEA model are not selected in a scientific and comprehensive manner, which may lead to unrepresent...

In this paper, we use an input oriented chance-constrained DEA model withrandom inputs and outputs. A super-eciency model with chance constraintsis used for ranking. However, for convenience in calculations a non-linear deterministicequivalent model is obtained to solve the models. The non-linearmodel is converted into a model with quadratic constraints to solve the nonlineardeterministic model...