Titlebook: Deriving Priorities from Incomplete Fuzzy Reciprocal Preference Relations; Theories and Methodo Yejun Xu Book 2023 The Editor(s) (if applic

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Normalizing Rank Aggregation-Based Method,, and it is described in Eq. (.). In this chapter, we further investigate the parameter . and call it normalizing rank aggregation-based method when .?=?./2 or .?=?(.???1)/2. Additionally, we will show that it is more reasonable when .?=?./2 or .?=?(.???1)/2 than .?=?0.5, which is extensively used in the existing literatures.

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Eigenvector Method,tiplicative preference relation, which was proposed by Saaty (1980). Lipovetsky and Conklin (2002) and Wang and Parkan (2005) investigated the eigenvector problem for complete fuzzy reciprocal preference relation. In the following, we show that the EM can also be used to derive the weights for incomplete fuzzy reciprocal preference relation.

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The Finnish Model of Higher Education AccessIn this chapter, we propose another method called least deviation method (LDM).

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Tiago Geraldo,Nuno Igreja MatosIn the former chapters, we have introduced several priority methods for incomplete fuzzy reciprocal preference relations. In this chapter, we introduce another preference relation called hesitant fuzzy reciprocal preference relation and present how to derive the priority weights from incomplete hesitant fuzzy reciprocal preference relations.