论文标题
开发完全直觉的模糊数据包络分析模型，该模型缺少数据：印度警察部门的申请
Development of fully intuitionistic fuzzy data envelopment analysis model with missing data: an application to Indian police sector
论文作者
论文摘要
数据包络分析（DEA）是一种用于衡量决策单元（DMU）效率的技术。为了衡量DMU的效率，基本要求是输入输出数据。数据通常由人类，机器或两者收集。由于人类/机器的错误，有可能存在一些缺失的值或不准确性，例如收集到的数据中的模糊/不确定性/犹豫。在这种情况下，很难准确地测量DMU的效率。为了克服这些缺点，提出了一种可以处理数据中缺失值和不准确性的方法。为了衡量DMU的性能效率，提出了完全直觉的模糊（如果）环境中的输入最小化BCC（IMBCC）模型。为了验证拟议的完全直觉的模糊输入最小化BCC（FIFIMBCC）模型的功效，以及处理数据中缺失价值的技术，提出了一个现实生活中的应用程序，以衡量印度警察局的绩效效率。
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.
