Bonferroni mean based similarity based TOPSIS

In this paper we introduce a generalization of the similarity based TOPSIS by using the Bonferroni mean. By generalizing similarity computation between an alternative and the ideal solution we also create an effect, where the ranking of alternatives can change depending on parameter value selection. For this purpose we apply the histogram ranking method to take into consideration the variability of rankings for a more robust holistic ranking of the alternatives. The proposed method is applied to a patent portfolio selection problem.