Performance evaluation of Uttarakhand electric utility data using slack based measure and Two-stage DEA modelling

Analysis on the influence of government efficiency on taiwan’s society, economy, and environment using data mining: a case study on open government data

Abstract: This study uses DEA-SBM to obtain the target variables and the decision tree model to produce important indicators. The government can promote Taiwan's development in all aspects by improving social, economic, and environmental efficiency, while ensuring the sustainable development of environmental protection. The 15 social, economic, and environmental variables are used in the decision tree to obtain 3 repeatable important indicators that can affect Taiwan's social change, economic development, operational capability, environmental pollution, and change. K-means divides the 22 counties and cities into 1 capital city, 5 municipalities, and 16 other counties and cities, in order to understand the influence of the economy on social and environmental development during the period of 2013-2015 in Taiwan.

Measuring the efficiency of decision making units

Abstract: A nonlinear (nonconvex) programming model provides a new definition of efficiency for use in evaluating activities of not-for-profit entities participating in public programs. A scalar measure of the efficiency of each participating unit is thereby provided, along with methods for objectively determining weights by reference to the observational data for the multiple outputs and multiple inputs that characterize such programs. Equivalences are established to ordinary linear programming models for effecting computations. The duals to these linear programming models provide a new way for estimating extremal relations from observational data. Connections between engineering and economic approaches to efficiency are delineated along with new interpretations and ways of using them in evaluating and controlling managerial behavior in public programs.

Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in taiwan

Abstract: The efficiency of decision processes which can be divided into two stages has been measured for the whole process as well as for each stage independently by using the conventional data envelopment analysis (DEA) methodology in order to identify the causes of inefficiency. This paper modifies the conventional DEA model by taking into account the series relationship of the two sub-processes within the whole process. Under this framework, the efficiency of the whole process can be decomposed into the product of the efficiencies of the two sub-processes. In addition to this sound mathematical property, the case of Taiwanese non-life insurance companies shows that some unusual results which have appeared in the independent model do not exist in the relational model. In other words, the relational model developed in this paper is more reliable in measuring the efficiencies and consequently is capable of identifying the causes of inefficiency more accurately. Based on the structure of the model, the idea of efficiency decomposition can be extended to systems composed of multiple stages connected in series.

Data Envelopment Analysis

Abstract: A system in which data for controlling amounts of ink for a number of printing machines, having a variety of printing plates, are measured with one measuring device. This invention provides a printing image area measuring device which can detect a printing image area with high accuracy even if the surface of a printing plate is uneven, which device is simple in construction, low in manufacturing cost and high in measurement accuracy, and which can provide in-line automatic measurement in a printing line.

Additive efficiency decomposition in two-stage DEA

Abstract: Kao and Hwang (2008) [Kao, C., Hwang, S.-N., 2008. Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research 185 (1), 418–429] develop a data envelopment analysis (DEA) approach for measuring efficiency of decision processes which can be divided into two stages. The first stage uses inputs to generate outputs which become the inputs to the second stage. The first stage outputs are referred to as intermediate measures. The second stage then uses these intermediate measures to produce outputs. Kao and Huang represent the efficiency of the overall process as the product of the efficiencies of the two stages. A major limitation of this model is its applicability to only constant returns to scale (CRS) situations. The current paper develops an additive efficiency decomposition approach wherein the overall efficiency is expressed as a (weighted) sum of the efficiencies of the individual stages. This approach can be applied under both CRS and variable returns to scale (VRS) assumptions. The case of Taiwanese non-life insurance companies is revisited using this newly developed approach.

Two-Stage DEA: An Application to Major League Baseball

Abstract: We show how to use DEA to model DMUs that produce in two stages, with output from the first stage becoming input to the second stage. Our model allows for any orientation or scale assumption. We apply the model to Major League Baseball, demonstrating its advantages over a standard DEA model. Our model detects inefficiencies that standard DEA models miss, and it can allow for resource consumption that the standard DEA model counts towards inefficiency. Additionally, our model distinguishes inefficiency in the first stage from that in the second stage, allowing managers to target inefficient stages of the production process.