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...

In this study, the data envelopment analysis (DEA), three-stage DEA (3SDEA) and artificial neural network (ANN) are employed to measure the technical efficiency of 29 semi-conductor firms in Taiwan. Estimated results show that there are significant differences in efficiency scores among DEA, 3SDEA and ANN analysis. The advanced setting of the three stages mechanism of DEA does show some changes...

BACKGROUND The Dog erythrocyte antigen (DEA) 1 blood group system was thought to contain types DEA 1.1 and 1.2 (and possibly 1.3 [A3]). However, DEA 1.2+ dogs are very rare and newer typing methods reveal varying degrees of DEA 1 positivity. OBJECTIVES To assess if variation in DEA 1 positivity is because of quantitative differences in surface antigen expression. To determine expression patte...

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...

The purpose of this paper is twofold. First, we develop a multi-period dynamic multi-process network DEA (data envelopment analysis) model. Second, we apply this methodology to Japanese prefectural time series data. In this framework, we specify that prefectural technology consists of two sectors, called the human capital generating sector and the physical capital formation sector. Each sector ...

Malmquist Productivity Index (MPI) is a numeric index that is of great importance in measuring productivity and its changes. In recent years, tools like DEA have been utilized for determining MPI. In the present paper, some models are recommended for calculating MPI when there are just ratio data available. Then, using DEA and DEA-R, some models are proposed under the constant returns to scale ...

Data Envelopment Analysis (DEA) is one of the most widely used methods in the measurement efficiency and productivity of Decision Making Units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper introduces a neural network backpropagation Data Envelopment Analysis. Neural network requirements of computer memo...

We propose an evaluation approach for a novel travel demand management strategy known as the downtown space reservation system (DSRS). This approach takes into account three perspectives, i.e., transportation service provider’s, the user’s, and the community’s and is based on network-Data Envelopment Analysis (DEA) where the perspectives are inter-related through intermediate inputs/outputs. Tw...

The portfolio is a perfect combination of stock or assets, which an investor buys them. The objective of the portfolio is to divide the investment risk among several shares. Using non-parametric DEA and DEA-R methods can be of great significance in estimating portfolio. In the present paper, the efficient portfolio is estimated by using non-radial DEA and DEA-R models. By proposing non-radial m...

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...