This paper presents a new version of the TOPSIS method for sorting problems. The proposed method, called Preference Disaggregation on Technique for Order of Preference by Similarity to Ideal Solution - Sort (PDTOPSIS-Sort), is based on nonlinear programming for inferring parameters and uses expert's holistic evaluations. The existing TOPSIS-Sort method demands a significant number of parameters from the expert, including the definition of boundary profiles and weights. The proposed method contributes to the literature by relieving the demand for cognitive effort observed in prior methods. Instead of providing boundary profiles for the limit between every two consecutive classes, the expert provides decision examples. In addition, the specification of weights is not required. A numerical validation of PDTOPSIS-Sort was undertaken based on results previously obtained from the literature for TOPSIS-Sort, which has been presented in detail as supplementary material. In addition, the first analysis of Brazilian corporate bonds supported by an MCDM/A model is presented. To do so, data were collected from the financial statements published by the issuers of these bonds. In total, the method proposed classified 50 debentures and the results were consistent with the preferences of the decision-maker, an investment-banking expert.

本文介绍了用于排序问题的TOPSIS方法的新版本。所提出的方法被称为“基于与理想解决方案排序相似的优先顺序技术的优先分解”（PDTOPSIS-排序），该方法基于用于推断参数的非线性编程，并使用了专家的整体评估。现有的TOPSIS-Sort方法需要专家提供大量参数，包括边界轮廓和权重的定义。所提出的方法通过减轻对先前方法中观察到的认知努力的需求而为文献做出了贡献。专家提供决策示例，而不是提供每两个连续的类之间的极限的边界分布图。另外，不需要重量规格。基于先前从TOPSIS-Sort文献中获得的结果对PDTOPSIS-Sort进行了数值验证，该结果已作为补充材料进行了详细介绍。此外，还介绍了由MCDM / A模型支持的巴西公司债券的首次分析。为此，从这些债券发行人发布的财务报表中收集了数据。总共，该方法提出了对50种债券的分类，其结果与投资银行专家决策者的偏好一致。数据是从这些债券发行人发布的财务报表中收集的。总共，该方法提出了对50种债券的分类，其结果与投资银行专家决策者的偏好一致。数据是从这些债券发行人发布的财务报表中收集的。总共，该方法提出了对50种债券的分类，其结果与投资银行专家决策者的偏好一致。