Multicriteria Decision Making (MCDM) has a huge role to play while ruling out one suitable alternative among a pool of alternatives governed by predefined multiple criteria. Some of the factors like imprecision, lack of information/data, etc., which are present in traditional MCDM processes have showcased their lack of efficiency and hence eventually it has paved the ways for the development of Fuzzy multicriteria decision making (FMCDM). In FMCDM processes, the decision makers can model most of the real-life phenomena by fuzzy information-based preferences. The availability of a wide literature on similarity measure (SM) emphasizes the vital role of SM of generalized fuzzy numbers (GFNs) to conduct accurate and precise decision making in FMCDM problems. Despite having few advantages, most of the existing approaches possessed a certain degree of counter intuitiveness and discrepancies. Thus, we have attempted to propose a novel SM for generalized trapezoidal fuzzy numbers (GTrFNs) which could deliberately overcome the impediments associated with the earlier existing approaches. Moreover, a meticulous comparative study with the existing approaches is also presented. This paper provides us with an improved method to obtain the similarity values between GTrFNs and the proposed SM consists of calculating the prominent features of fuzzy numbers such as expected value and variance. We use fourteen different sets of GTrFNs, to compare the fruition of the present approach with the existing SM approaches. Furthermore, to show the utility and applicability of our proposed measure, we illustrate few practical scenarios such as the launching of an electronic gadget by a company, a problem of medical diagnosis and finally, a proper anti-virus mask selection in light of the recent COVID-19 pandemic. The obtained results with our proposed SM, for the mentioned FMCDM problems, are analytically correct and they depict the efficiency and novelty of the present article.

多标准决策制定 (MCDM) 在从预定义的多个标准管理的备选方案池中排除一个合适的备选方案时发挥着巨大的作用。传统 MCDM 过程中存在的一些因素，如不精确、缺乏信息/数据等，已经显示出它们缺乏效率，因此最终为模糊多标准决策 (FMCDM) 的发展铺平了道路。在 FMCDM 过程中，决策者可以通过基于模糊信息的偏好对大多数现实生活现象进行建模。关于相似性度量 (SM) 的大量文献的可用性强调了广义模糊数 (GFN) 的 SM 在 FMCDM 问题中进行准确和精确决策的重要作用。尽管优点不多，大多数现有方法都具有一定程度的反直觉性和差异性。因此，我们试图为广义梯形模糊数 (GTrFN) 提出一种新的 SM，它可以有意地克服与早期现有方法相关的障碍。此外，还介绍了与现有方法的细致比较研究。本文为我们提供了一种改进的方法来获得 GTrFN 之间的相似性值，所提出的 SM 包括计算模糊数的显着特征，例如期望值和方差。我们使用十四组不同的 GTrFN 来比较当前方法与现有 SM 方法的结果。此外，为了显示我们提出的措施的效用和适用性，我们举例说明了几个实际场景，例如公司推出电子产品、医疗诊断问题以及最后根据最近的 COVID-19 大流行选择合适的防病毒口罩。对于上述 FMCDM 问题，我们提出的 SM 获得的结果在分析上是正确的，它们描述了本文的效率和新颖性。