DATA ENVELOPMENT ANALYSIS (DEA) is a technique used to evaluate the relative efficiency of comparable decision making units (DMUs) with MULTIPLE input-output. It computes a scalar measure of efficiency and discriminates between efficient and inefficient DMUs. It can also provide reference units for inefficient DMUs without consideration of the decision makers’ (DMs) preferences. In this paper, we deal with the problem of incorporating preferences over potential improvements to individual input output levels so that the resultant target levels reflect the DM’s preferences over alternative paths to efficiency. In this way, the paper will establish an equivalence model between DEA and MULTIPLE OBJECTIVE LINEAR PROGRAMMING ((MOLP)) and show how a DEA problem can be solved interactively by transforming it into an (MOLP) formulation. As a result, all efficient units of variable returns to scale technology in DEA can be found by solving the proposed (MOLP) problem by parametric LINEAR PROGRAMMING. Numerical examples confirm the validity of the proposed model as a means for solving different DEA problems.

It is generally accepted that DATA ENVELOPMENT ANALYSIS (DEA) is a method for indicating efficiency. The DEA method has many applications in the field of calculating the relative efficiency of Decision Making Units (DMU) in explicit input-output environments. Regarding imprecise DATA, several definitions of efficiency can be found. The aim of our work is showing an equivalence relation between one of the models of DEA with imprecise DATA and MULTIPLE OBJECTIVE LINEAR PROGRAMMING ((MOLP)). The relation between DEA and (MOLP) was studied to use interactive MULTIPLE OBJECTIVE models for solving the DEA problem in exact situation and find the most preferred solution. The aim of this study is to analyze an equivalent relation between imprecise DEA (IDEA) and (MOLP) models. In this context, we tried to solve IDEA models with interactive project procedure. The Project method is the responsible method, because it can estimate any efficient solution, and it indicates Most Preferred Solution (MPS). In addition, we will use the DATA ENVELOPMENT Scenario ANALYSIS (DESA) model. The main characteristic of DESA model is to decrease all inputs and increase all outputs and estimate one problem instead of n problems.

The present study deals with the inverse DEA using the non-radial Enhanced Russell (ER)-measure in the presence of fuzzy DATA. This paper proposes a technique to treat the fuzzy DATA in the problem of simultaneous estimation of input-output levels. Necessary and sufficient conditions are provided for ER-measure maintaining in the presence of fuzzy DATA. A numerical example with real DATA is presented to show the accuracy of the proposed method.

The purpose of present study is to compare the relative efficiency of Iranian ports and identify the efficient ports in 2009 to 2011. Applied-analytical method was used and statistical population includes important Iranian ports. DATA are gathered from Port and Maritime Organization of Iran. DATA include the years 2009, 2010 and March to November 2011. CCR model is used to determine the efficiency ratio of ports and to distinguish efficient and inefficient ports. For the units that are identified as efficient by CCR model, Anderson–Pearson (AP) was used. For analyzing DATA, LINGO and SPSS software was used. Findings showed that in 2009, 36%, in 2010,  27% and in 2011, 27% of main Iranian ports have been efficient. In 2009 and 2010, Bahonar and in 2011, Rajaei ports were recognized as the most efficient Iranian ports. Finally, virtual ports were introduced as a pattern for inefficient ports. Inefficient ports will achieve efficiency if they can be promoted to achieve the conditions of their similar virtual ports.

A characteristic of DATA ENVELOPMENT ANALYSIS (DEA) is to allow individual decision making units (DMUs) to select the most advantageous weights in calculating their efficiency scores. This flexibility, on the other hand, deters the comparison among DMUs on a common base. For dealing with this difficulty and assessing all the DMUs on the same scale, this paper proposes using a MULTIPLE OBJECTIVE LINEAR PROGRAMMING ((MOLP)) approach for generating a common set of weights in the DEA framework.

A characteristic of DATA ENVELOPMENT ANALYSIS (DEA) is to allow individual decision making units (DMUs) to select the factor weights that are the most advantages for them in calculating their efficiency scores. This flexibility in selecting the weights, on the other hand, deters the comparison among DMUs on a common base. For dealing with this difficulty and assessing all the DMUs on the same scale, this paper proposes to use a MULTIPLE OBJECTIVE LINEAR PROGRAMMING ((MOLP)) approach for generating common set of weights under the DEA framework. This is an advantage of the proposed approach against general approaches in the literature which are based on MULTIPLE OBJECTIVE nonLINEAR PROGRAMMING.

A characteristic of DATA ENVELOPMENT ANALYSIS (DEA) is to allow individual decision making units (DMUs) to select the factor weights which are the most advantageous for them in calculating their efficiency scores. This flexibility in selecting the weights, on the other hand, deters the comparison among DMUs on a common base. For dealing with this difficulty and assessing all the DMUs on the same scale, this paper proposes using a MULTIPLE OBJECTIVE LINEAR PROGRAMMING ((MOLP)) approach based on scalarizing function for generating common set of weights under the DEA framework. This is an advantageous of the proposed approach against general approaches in the literature which are based on MULTIPLE OBJECTIVE nonLINEAR PROGRAMMING.