Mobile edge caching scheme (MECS) can determine where, how, and what to cache on user equipment by employing its own storage. When considering the performance of MECS, it is often full of uncertainty. The q‐rung orthopair fuzzy set (q‐ROFS), characterized by membership and nonmembership degrees with adjustable parameter $q$, is quite a high‐efficiency way to capture uncertainty. In this paper, first, information measure (entropy, distance measure, and similarity measure)‐based area difference under the q‐rung orthopair fuzzy (q‐ROF) circumstance is studied along with their detailed proofs. Then, we present a comprehensive weight‐determination method by combining objective weights (determining by entropy) and subjective weights (given by experts) as combined weights, which can effectually alleviate the unconscionable influence of extreme data on evaluation results and simultaneously reflect objective data and subjective emotion. Moreover, q‐ROF score function‐based distance measure is presented for dealing with a value comparison problem. Later, q‐ROF multicriteria decision‐making (MCDM) method called total area based on orthogonal vector (TAOV) is introduced. Moreover, its feasibility is illustrated by MECS selection problem. Finally, a comparison of some existing MCDM methods and the proposed method is constructed for displaying their effectiveness. This proposed method can effectively avoid counterintuitive phenomena, eliminate antilogarithm by negative and zero issue, and has no division by zero issue.

中文翻译：

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整合移动边缘缓存方案偏好的q-Rung Orthopair模糊决策框架

移动边缘缓存方案（MECS）可以通过使用自己的存储来确定在用户设备上缓存的位置，方式和内容。考虑到MECS的性能时，通常充满不确定性。所述Q-梯级orthopair模糊集（Q- ROFS），其特征在于，具有可调节的参数成员资格和非成员度$q$，是捕获不确定性的一种非常有效的方法。在本文中，首先，研究了基于q-梯级正交对（q- ROF）情况下基于信息量度（熵，距离量度和相似性量度）的面积差异以及它们的详细证明。然后，我们通过将客观权重（由熵决定）和主观权重（由专家给出）作为组合权重，提出了一种综合的权重确定方法，可以有效地减轻极端数据对评估结果的不合理影响，同时反映客观数据和主观情感。此外，提出了基于q -ROF得分函数的距离度量，用于处理值比较问题。后来，q-介绍了基于正交向量（TAOV）的ROF多准则决策（MCDM）方法。此外，通过MECS选择问题说明了其可行性。最后，对一些现有的MCDM方法和所提出的方法进行了比较，以显示其有效性。该方法可以有效避免反直觉现象，消除负数和零问题的对数，并且不除以零问题。