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An Iterative Transient Rank Aggregation Technique for Mitigation of Rank Reversal

机译：缓解等级逆转的迭代暂态等级聚合技术

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摘要

With the advent of decision science, significant elucidation has been sought in the literature of multi criteria decision making. Often, it is observed that for the same MCDM problem, different methods fetch way-apart ranks and the phenomenon leads to rank reversal. To alleviate this problem, different methodologies like the Borda rule, the Copeland method, the Condorcet method, the statistical Thurstone scaling, and linear programming methods are readily available in the literature. In connection with the same, the authors proposed a novel technique to aggregate the ranks laid by different methods. The algorithm initially assigns equal weights to the methods involved to avoid biasness to a particular method and a simple average rank was obtained. Then, after the separation measures of individual methods with respect to average rank were calculated. Considering the separation measure the higher the weightage, the dynamic weights are ascertained to declare the weighted aggregate rank subjected to the terminal condition which include whether the previous rank equals to the current rank or not. To substantiate the proposed algorithm, a materials selection problem was taken into consideration and solved with the proposed technique. Moreover, the same problem was solved by existing voting techniques like the Borda and the Copeland-Condoract methods. The authors found a correlation of more than 85% between the proposed and existing methodologies.
机译：随着决策科学的到来，在多准则决策的文献中一直寻求大量的解释。通常，可以观察到，对于相同的MCDM问题，不同的方法会按不同的方式获取等级，而这种现象会导致等级反转。为了缓解这个问题，文献中很容易获得诸如Borda规则，Copeland方法，Condorcet方法，统计Thurstone标度和线性规划方法之类的不同方法。与此相关，作者提出了一种新颖的技术来汇总由不同方法得出的等级。该算法首先为所涉及的方法分配相等的权重，以避免对特定方法的偏倚，从而获得简单的平均排名。然后，计算出各个方法相对于平均等级的分离度。考虑到分离措施的权重越高，确定动态权重以声明处于终止条件的加权合计等级，其中包括先前等级是否等于当前等级。为了证实所提出的算法，考虑了材料选择问题并利用所提出的技术对其进行了解决。此外，现有的投票技术（例如Borda和Copeland-Condoract方法）解决了相同的问题。作者发现，提议的方法与现有方法之间的相关性超过85％。

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