- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Based on the CCR model, we propose an extended data envelopment analysis to evaluate the efficiency of decision making units with historical input and output data. The contributions of the work are threefold. First, the input and output data of the evaluated decision making unit are variable over time, and time series method is used to analyze and predict the data. Second, there are many sample decision making units, which are divided into several ordered sample standards in terms of production strategy, and the constraint condition consists of one of the sample standards. Furthermore, the efficiency is illustrated by considering the efficiency relationship between the evaluated decision making unit and sample decision making units from constraint condition. Third, to reduce the computation complexity, we introduce an algorithm based on the binary search tree in the model to choose the sample standard that has similar behavior with the evaluated decision making unit. Finally, we provide two numerical examples to illustrate the proposed model.
In the conventional DEA model, the inputs and outputs are known exactly, and the constraint condition consists of the evaluated DMUs. However, in many real applications, the observed data of the evaluated DMUs are variable over time. The efficiency of every evaluated DMU in a particular period may not be contrasted with the evaluated DMUs, but with sample standards determined by production strategy. Moreover, the development trend of the evaluated DMU, which is an important index to the budgetary decision-making and management system, is often required to be predicted.
In this paper, we proposed an extended DEA model to evaluate the efficiency of DMUs with historical observed data of inputs and outputs. Firstly, based on the historical observed data, we introduced the time series method to analyze and predict the development trend of the evaluated DMUs. Secondly, in the proposed model, there are many sample DMUs, which are divided into several ordered sample standards in terms of manufacturing parameters, and the constraint condition consists of one of the sample standards. Finally, we employ the algorithm based on a binary search tree to determine the constraint condition in order to reduce the computation complexity. One of the most intriguing and appealing points mentioned is that the paper is suitable for the decision-making, whether the evaluated DMUs are hospitals, universities, branches of a bank, or whatever.