Data Envelopment Analysis is one of the paramount mathematical methods to compute the general performance of organizations, which utilizes similar sources to produce similar outputs. Original DEA schemes involve crisp information of inputs and outputs that may not always be accessible in real-world applications. Nevertheless, in some cases, the values of the data are information with indeterminacy, impreciseness, vagueness, inconsistent, and incompleteness. Furthermore, the conventional DEA models have been originally formulated solely for desirable outputs. However, undesirable outputs may additionally be present in the manufacturing system, which wishes to be minimized. To tackle the mentioned issues and in order to obtain a reliable measurement that keeps original advantage of DEA and considers the influence of undesirable factors under the indeterminate environments, this paper presents a neutrosophic DEA model with undesirable outputs. The recommended technique is based on the aggregation operator and has a simple construction. Finally, an example is given to illustrate the new model and ranking approach in details.

中文翻译：

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基于中智方法的数据包络分析，输出不理想

数据包络分析是计算组织总体绩效的最重要的数学方法之一，它利用相似的来源产生相似的输出。原始的DEA方案涉及输入和输出的清晰信息，这些信息在现实应用中可能并不总是可以访问的。但是，在某些情况下，数据的值是具有不确定性，不精确性，模糊性，不一致和不完整的信息。此外，传统的DEA模型最初只是为获得期望的输出而制定的。然而，在制造系统中可能另外存在不希望的输出，希望将其最小化。为了解决上述问题，并为了获得一种可靠的测量方法，该方法保持了DEA的原始优势并考虑了不确定环境下不良因素的影响，本文提出了一种中性DEA模型，其输出不理想。推荐的技术基于聚合运算符，并且结构简单。最后，给出一个例子来详细说明新模型和排名方法。