Data Envelopment Analysis (DEA) is a powerful performance measurement in economic sector and operations research to assess the relative efficiency for each decision making unit (DMU). In general, there are two assumptions in DEA. Firstly, the DEA assumes that all DMUs are homogenous in their environments and secondly, the DEA is a deterministic approach which refers to not allow to noise or err...

The cross-efficiency evaluation is an effective method to evaluate the decision making units (DMUs) with the nonnegative inputs and outputs in data envelopment analysis (DEA) and can be performed with the different formulations. But, in some situations the DMUs encounter with the negative inputs and outputs. This paper generalizes the cross efficiency evaluation for the DMUs with the negative i...

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 ...

One of the existing DEAmethods’ limitations noted in the literature lies in the process of benchmarking of reference targets for inefficient DMUs. Difficulties arising in this process can be summarized to three aspects. First, the reference target might be a hypothetical DMU that does not actually exist (it is difficult and indeed unrealistic to learn from such a DMU). Second, the reference set...

The purpose of this study is to utilize a new method for ranking extreme efficient decision making units (DMUs) based upon the omission of these efficient DMUs from reference set of inefficient and non-extreme efficient DMUs in data envelopment analysis (DEA) models with constant and variable returns to scale. In this method, an L2norm is used and it is believed that it doesn't have any existin...

Determining the optimal scale size of a combined cycle power plant is inherently a complex problem often with multiple and conflicting criteria as well as uncertain factors. The complexity of the problem is compounded by the production of undesirable outputs and the presence of natural and managerial disposability. We propose a customized data envelopment analysis (DEA) method for solving the r...

We present a theoretical and computational study of the impact of inserting a new attribute and removing an old attribute in a DEA model. Our objective is to obviate a portion of the computational effort needed to process such model changes by studying how the efficient/inefficient status of DMUs is affected. Reducing computational efforts is important since DEA is known to be computationally i...

Digital Mock-ups (DMUs) are widespread and form a common basis for product description. However, DMUs produced by industrial CAD systems essentially contain geometric models and their exploitation often requires new input data to derive various simulation models. In this work, analysis and reasoning approaches are developed to automatically enrich DMUs with functional and kinematic properties. ...

Conventional data envelopment analysis (DEA) assists decision makers in distinguishing between efficient and inefficient decision making units (DMUs) in a homogeneous group. However, DEA does not provide more information about efficient units. Super-efficiency DEA models can be used in ranking the performance of efficient DMUs. This research proposes a methodology to determine an Euclidean dist...

Conventional DEA models tend to allocate the fixed resources multiple decision-making units (DMUs) and treat allocated resource as an extra input for every single DMU. However, existing allocation (DEA-RA) methods are applicable exclusively DMUs with exact values of inputs outputs. A lack precision or output data DMUs, such interval data, would cause a failure DMUs. In order resolve this proble...