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A Novel Ranking Approach to Solving Fully LR-Intuitionistic Fuzzy Transportation Problems

机译:一种解决完全LR直觉模糊运输问题的新排序方法

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To the extent of our knowledge, there is no method in fuzzy environment to solving the fully LR-intuitionistic fuzzy transportation problems (LR-IFTPs) in which all the parameters are represented by LR-intuitionistic fuzzy numbers (LR-IFNs). In this paper, a novel ranking function is proposed to finding an optimal solution of fully LR-intuitionistic fuzzy transportation problem by using the distance minimizer of two LR-IFNs. It is shown that the proposed ranking method for LR-intuitionistic fuzzy numbers satisfies the general axioms of ranking functions. Further, we have applied ranking approach to solve an LR-intuitionistic fuzzy transportation problem in which all the parameters (supply, cost and demand) are transformed into LR-intuitionistic fuzzy numbers. The proposed method is illustrated with a numerical example to show the solution procedure and to demonstrate the efficiency of the proposed method by comparison with some existing ranking methods available in the literature.
机译:据我们所知,在模糊环境中没有方法可以解决其中所有参数均由LR直觉模糊数(LR-IFN)表示的完全LR直觉模糊运输问题(LR-IFTPs)。本文提出了一种新颖的排序函数,通过使用两个LR-IFN的距离最小化器找到完全LR直觉的模糊运输问题的最优解。结果表明,所提出的LR直觉模糊数的排序方法满足了排序函数的一般公理。此外,我们已经应用排序方法来解决将所有参数(供应,成本和需求)都转换为LR直觉模糊数的LR直觉模糊运输问题。通过数值示例对提出的方法进行了说明,通过与文献中现有的一些排名方法进行比较,展示了该方法的求解过程并证明了该方法的效率。

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