The theory of multi-source information fusion plays a pivotal role in decision-making, especially when handling uncertain or imprecise information. Among the existing frameworks, evidence theory has proven effective for integrating diverse information sources to support informed decision-making. Recently, Random Permutation Set Theory (RPST), an extension of evidence theory, has shown significant practical value due to its ability to leverage the additional information inherent in event permutations. This insight inspires the utilization of permutation data to enhance the decision-making process. When employing RPST for decision-making and fusion, the order in which the fusion is performed can substantially influence the final results. To address this issue, we propose a novel approach that utilizes Fisher Scores to extract additional permutation information to guide decision-making within the RPST framework. Experimental results on the Iris dataset validate the feasibility and effectiveness of the proposed method. Compared to fusion methods employing weighted averaging, our approach, which leverages additional information to determine the fusion order, demonstrates superior accuracy across various training set proportions, achieving an accuracy of 96.26% at an 80% training set proportion. This provides an enhanced strategy for decision-making under uncertainty.