Data Envelopment Analysis (DEA) is a method for evaluating the performance of a set of homogeneous Decision Making Units (DMUs). When there are uncertainties in problem data, original DEA models might lead to incorrect results. In this study, we present two stochastic p-robust two-stage Network Data Envelopment Analysis (NDEA) models for DMUs efficiency estimation under uncertainty based on Stackelberg (leader-follower) and centralized game theory models. This allows a deleterious effect to the objective function to better hedge against the uncertain cases those are commonly ignored in classical NDEA models. In the sequel, we obtained an ideal robustness level and the maximum possible overall efficiency score of each DMU over all permissible uncertainties, and also the minimal amount of uncertainty level for each DMU under proposed models. The applicability of the proposed models is shown in the context of the analysis of bank branches performance.

中文翻译：

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两阶段网络DEA模型的随机p鲁棒方法

数据包络分析（DEA）是一种评估一组同类决策单元（DMU）性能的方法。当问题数据不确定时，原始的DEA模型可能会导致错误的结果。在这项研究中，我们提出了基于Stackelberg（领导者跟随者）和集中式博弈模型的两个随机p稳健两阶段网络数据包络分析（NDEA）模型，用于不确定性下DMU的效率估计。这允许对目标函数产生有害影响，以便更好地对冲那些在经典NDEA模型中通常被忽略的不确定情况。在续篇中，我们获得了理想的鲁棒性水平以及在所有允许的不确定性上每个DMU的最大可能总体效率得分，以及在建议的模型下每个DMU的最小不确定性水平。