Data envelopment analysis (DEA) is a model for measuring the efficiency of decision-making units (DMUs). The majority of DEA models suffer from drawbacks, in particular, changes in the weights of inputs and outputs. Consequently, the efficiency of DMUs is measured with different weights and so it is important to establish how to evaluate all DMUs using a common weight to optimize their efficiency at the same time. This study provides a new algorithm to overcome the weaknesses of the previous model. The proposed algorithm based on the bootstrap simulation establishes a bound for the input and output weights. Common weights are obtained by solving this model using bounded weights. According to the results of a numerical example solved by this model, it outperforms conventional models in terms of ranking DMUs.

数据包络分析（DEA）是用于衡量决策单位（DMU）效率的模型。大多数DEA模型都有缺点，特别是输入和输出权重的变化。因此，DMU的效率是用不同的权重来衡量的，因此，建立如何使用共同的权重来评估所有DMU的效率以同时优化其效率非常重要。这项研究提供了一种新的算法来克服先前模型的弱点。所提出的基于自举模拟的算法为输入和输出权重确定了界限。通过使用有界权重求解此模型，可以得到公共权重。根据此模型求解的数值示例的结果，就DMU的排名而言，它优于常规模型。