Performance measurement is a complex but important task required in all sectors. The problem however arises when usage of different methods for performance assessment provides different results. Under such circumstances when there is a difference of opinions, rank aggregation methods can be used to provide the best solution to decision-makers (DMs). Such approaches, also known as data fusion approaches, combine ranked lists from various methods to generate a consensus. In this study, a novel rank aggregation method is proposed for addressing the problem of conflicting MCDM ranking results. The suggested method uses genetic algorithm (GA) to minimize the Euclidean distance between the ideal ranking and the ranking computed by multiple MCDM methods. This model is embedded into a hybrid multi-criteria decision-making (HMCDM) approach, which is divided into three distinct phases. The first phase identifies the most efficient alternatives; the second analyses the rankings obtained through various MCDM methods; and finally, a compromise ranking result is generated. The proposed approach is employed to measure the performance of Indian Pulp and Papermaking Industries (IPPI).

绩效衡量是所有部门都需要的一项复杂但重要的任务。然而，当使用不同的绩效评估方法提供不同的结果时，就会出现问题。在这种意见分歧的情况下，可以使用等级聚合方法为决策者（DM）提供最佳解决方案。这种方法，也称为数据融合方法，结合了来自各种方法的排序列表以产生共识。在这项研究中，提出了一种新的排名聚合方法来解决 MCDM 排名结果冲突的问题。建议的方法使用遗传算法 (GA) 来最小化理想排名与多个 MCDM 方法计算的排名之间的欧几里得距离。该模型嵌入到混合多标准决策 (HMCDM) 方法中，分为三个不同的阶段。第一阶段确定最有效的替代方案；第二个分析通过各种MCDM方法获得的排名；最后生成折衷排序结果。所提出的方法用于衡量印度纸浆和造纸工业 (IPPI) 的绩效。