Data Envelopment Analysis (DEA) is a technique used to measure the efficiency of decision-making units (DMUs). In order to measure the efficiency of DMUs, the essential requirement is input-output data. Data is usually collected by humans, machines, or both. Due to human/machine errors, there are chances of having some missing values or inaccuracy, such as vagueness/uncertainty/hesitation in the collected data. In this situation, it will be difficult to measure the efficiencies of DMUs accurately. To overcome these shortcomings, a method is presented that can deal with missing values and inaccuracy in the data. To measure the performance efficiencies of DMUs, an input minimization BCC (IMBCC) model in a fully intuitionistic fuzzy (IF) environment is proposed. To validate the efficacy of the proposed fully intuitionistic fuzzy input minimization BCC (FIFIMBCC) model and the technique to deal with missing values in the data, a real-life application to measure the performance efficiencies of Indian police stations is presented.

翻译：数据渗透分析(DEA)是用来衡量决策单位效率的一种技术。为了衡量数据传输单位的效率,基本要求是输入产出数据。数据通常由人、机器或两者兼而有之地收集。由于人为/机器错误,在所收集的数据中有可能有一些缺失的值或不准确性,例如模糊/不确定/偏差。在这种情况下,很难准确衡量数据传输单位的效率。为了克服这些缺陷,提出了一种能够处理数据缺失的值和不准确性的方法。为了衡量数据传输单位的性能效率,提议在完全直觉的烟雾环境中尽量减少BCC(IMBCC)的输入模型。为了验证拟议的完全直觉的烟雾输入最小化模型和处理数据缺失值的技术的有效性,提出了衡量印度警察局性能效率的实际应用。