Operator allocation is one of the most important decisions that can influence productivity in the laborintensive manufacturing system. In this paper 10 operator allocation alternatives are identified with the assistance of computer simulation. To determine the best operator allocation, AHP/DEA and DEA Cross Efficiency are used. The results of both techniques are compared. Based on the results, ...

In this paper, we re-examine Data Envelopment Analysis models from perspectives of preference order, production set and performance measure. we investigate the relationship between Data Envelopment Analysis (DEA) and Multiple Criteria Decision Making Theory. There are three key building blocks in a DEA model: preference order, production possibility set and performance measure. It is shown in t...

In original data envelopment analysis (DEA) models, the data for all inputs and outputs are known exactly. When some inputs and outputs are unknown decision variables, such as interval data, ordinal data, and ratio bounded data, the DEA model is called imprecise DEA (IDEA). In this paper, We develop an alternative approach based upon slacksbased measure of efficiency (SBM) for dealing with inte...

Dissociative electron attachment (DEA) is the major process where molecules are destroyed in low-energy plasmas. DEA cross sections are therefore important for a whole variety of applications but are both hard to measure or compute accurately. A method for estimating DEA cross sections based a simple resonance plus survival model is presented. Test results are presented for DEA of molecular oxy...

This paper discusses alternative methods for determining returns to scale in DEA. The methods for estimating returns to scale in DEA, as developed by Banker (1984), Banker, Charnes and Cooper (1984) and Banker and Thrall (1992), are proved to be conceptually equivalent to the two-stage methods of F~ire, Grosskopf and Lovell (1985) when their assumptions apply. Here the emphasis is on the CCR mo...

Selection of international market (IM) is a critical decision and needs to be made with considerable attention and deliberation. In some situations however, some criteria have the nature of both cost and profit. Likewise, in data envelopment analysis (DEA) some criteria play both input (cost) and output (profit) roles, simultaneously. In the DEA literature such criteria are called dual-role fac...

Data quality is critical to a successful data envelopment analysis (DEA) study. Outlier detection not only identifies suspicious data points and thus prevents the drawing of erroneous conclusions, but also can lead to the discovery of unexpected knowledge. This study develops a unified model to identify outliers in DEA studies by examining how they effect on the boundaries of a data set. The pr...

Sensitivity analysis in DEA is used for improving the efficiency scores of inefficient DMUs for which the efficient units remain unchanged. This paper introduces a generalized sensitivity analysis DEA model by perturbation a given input (or output) for all efficient DMUs. A numerical example illustrates the usefulness of the new model.

Data envelopment analysis (DEA) has been a wildly used powerful method to measure efficiencies of decision making units (DMUs). However, DEA efficiency scores are influenced by uncontrollable factors for respective DMUs. Previous studies attempted separating such factors from DEA scores. Fried et al. [4] proposed a multi-stage data adjustment approach using DEA and a regression model, and sever...

The conventional data envelopment analysis (DEA) measures the relative efficiencies of a set of decision making units (DMUs) with exact value of inputs and outputs. For imprecise data, i.e., mixtures of interval data and ordinal data, some methods have been developed to calculate the interval of the efficiency scores. This paper constructs a procedure to measure the efficiencies of DMUs with mi...