Single-valued neutrosophic set (SVNS) is considered as generalization and extension of fuzzy set, intuitionistic fuzzy set (IFS), and crisp set for expressing the imprecise, incomplete, and indeterminate information about real-life decision-oriented models. The theme of this research is to develop a solution approach to solve constrained bimatrix games with payoffs of single-valued trapezoidal neutrosophic numbers (SVTNNs). In this approach, the concepts and suitable ranking function of SVTNNs are defined. Hereby, the equilibrium optimal strategies and equilibrium values for both players can be determined by solving the parameterized mathematical programming problems, which are obtained from two novel auxiliary SVTNNs programming problems based on the proposed ranking approach of SVTNNs. Moreover, an application example is examined to verify the effectiveness and superiority of the developed algorithm. Finally, a comparison analysis between the proposed and the existing approaches is conducted to expose the advantages of our work.

中文翻译：

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收益由单值梯形中智数表示的约束双矩阵博弈的最优解

单值中智集（SVNS）被认为是模糊集、直觉模糊集（IFS）和清晰集的泛化和扩展，用于表达现实生活中决策模型的不精确、不完整和不确定的信息。本研究的主题是开发一种解决方案，以解决具有单值梯形中智数 (SVTNN) 收益的约束双矩阵博弈。在这种方法中，定义了 SVTNN 的概念和合适的排序函数。因此，两个参与者的均衡最优策略和均衡值可以通过求解参数化数学规划问题来确定，这些问题是从基于所提出的 SVTNN 排序方法的两个新的辅助 SVTNN 规划问题中获得的。而且，通过一个应用实例来验证所开发算法的有效性和优越性。最后，对所提出的方法和现有方法进行了比较分析，以揭示我们工作的优势。