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...

in data envelopment analyze (dea) the scale efficiency in the input-oriented ccr model is less than or equal to the scale efficiency in dea based on the fractional analysis (dea-r). also, the scale efficiency in case of multiple inputs and one output and vice versa the scale efficiencies are equal in dea and dea-r. in this paper, first, dea-r model with weight restrictions when there is relativ...

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 ...

This paper uses the DEA models to rank the efficiency of Indian non-life insurance firms over the period 2008-2013 with Expenses, Capital, Premium and Investment income variables in the modelling. These basic DEA models identify adequately the inefficient firms, but are weak in discriminating among those found to be efficient. To improve the discrimination, we used the Cross-Efficiency and the ...

This paper incorporates cones on virtual multipliers of inputs and outputs into DEA analysis. Cone DEA models are developed to generalize the dual of the BCC models as well as congestion models. Input-output data and/or numbers of DMUs for BCC models are inadequate to capture many aspects where judgments, expert opinions, and other external information should be taken into analysis. Cone DEA mo...

Quantifying cartilage contact stress is paramount to understanding hip osteoarthritis. Discrete element analysis (DEA) is a computationally efficient method to estimate cartilage contact stresses. Previous applications of DEA have underestimated cartilage stresses and yielded unrealistic contact patterns because they assumed constant cartilage thickness and/or concentric joint geometry. The stu...

data envelopment analysis (dea) is a managerial powerful tool for assess the performance of decision making units (dmus). nowadays, multi-objective dea models are an attractive technique for evaluation quantity and quality aspects of performance analysis. but these models are in static. in this paper, we want to present a proposed fuzzy dynamic multi-objective dea model in which data are changi...

Despite the large uses of inverse DEA models, there is not any single application of inverse linear programming in DEA when the definition of inverse linear programming is taken under account. Thus the goal of this paper is applying the inverse linear programming into DEA field, and to provide a streamlined approach to DEA and Additive model. Having the entire efficient DMUs in DEA models is a...

In the data envelopment analysis (DEA) literature, linear fractional non-cooperative network DEA models for two-stage network structures are often transformed into parametric linear models. The transformed parametric linear models are then solved by computing a series of linear models when the parameter is varied. For example, Wu, Zhu, Ji, Chu and Liang (2016) provide a linear fractional non-co...