Deriving Priorities from Incomplete Fuzzy Reciprocal Preference Relations
Theories and Methodologies
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Beschreibung
As we know, multiplicative preference relations (or called pairwise comparisons in AHP) were proposed by Dr. Thomas L Saaty. One important work is to derive its priority from pairwise comparisons. It has been proposed many methods to derive priority for multiplicative preference relation. On the basis of fuzzy sets, the fuzzy reciprocal preference relation is proposed and is extended to the incomplete contexts. However, how to derive the priorities from incomplete fuzzy reciprocal preference relations is an interesting and challenging work. This book systematically presents the theories and methodologies for deriving priorities from incomplete fuzzy reciprocal preference relations. This book can be divided into three parts. In the first part, this book introduces the basic concepts of fuzzy reciprocal preference relations and incomplete fuzzy reciprocal preference relations. Then, two consistencies of complete fuzzy reciprocal preference relations are introduced: additive consistency and multiplicative consistency. Then, the relationships between the fuzzy reciprocal elements and the weights are showed. Afterward, in the second part, different priority methods are presented. The inconsistency repairing procedures are also proposed. Last, the priority method for incomplete hesitant fuzzy reciprocal preference relations is presented. This book can be used as a reference for researchers in the areas of management science, information science, systems engineering, operations research, and other relevant fields. It can also be employed as a textbook for upper-level undergraduate students and graduate students.
As we know, multiplicative preference relations (or called pairwise comparisons in AHP) were proposed by Dr. Thomas L Saaty. One important work is to derive its priority from pairwise comparisons. It has been proposed many methods to derive priority for multiplicative preference relation. On the basis of fuzzy sets, the fuzzy reciprocal preference relation is proposed and is extended to the incomplete contexts. However, how to derive the priorities from incomplete fuzzy reciprocal preference relations is an interesting and challenging work. This book systematically presents the theories and methodologies for deriving priorities from incomplete fuzzy reciprocal preference relations. This book can be divided into three parts. In the first part, this book introduces the basic concepts of fuzzy reciprocal preference relations and incomplete fuzzy reciprocal preference relations. Then, two consistencies of complete fuzzy reciprocal preference relations are introduced: additive consistency and multiplicative consistency. Then, the relationships between the fuzzy reciprocal elements and the weights are showed. Afterward, in the second part, different priority methods are presented. The inconsistency repairing procedures are also proposed. Last, the priority method for incomplete hesitant fuzzy reciprocal preference relations is presented. This book can be used as a reference for researchers in the areas of management science, information science, systems engineering, operations research, and other relevant fields. It can also be employed as a textbook for upper-level undergraduate students and graduate students.
Help master various methods for deriving priorities from incomplete fuzzy preference relations. Inspire readers to extend these methods to other preference relations. The first book on theories and methodologies on deriving priority from incomplete fuzzy preference relation.
Autor*in
Yejun Xu
Themen in »Deriving Priorities from Incomplete Fuzzy Reciprocal Preference Relations«
Hesitant fuzzy preference relation Ranking Fuzzy preference relation Incomplete fuzzy preference relation Prioirty weights
Stimmen zu »Deriving Priorities from Incomplete Fuzzy Reciprocal Preference Relations«
“This book develops forms of deriving priority weights with additive or multiplicative consistency from the elements of a fuzzy reciprocal preference relation or from an incomplete fuzzy reciprocal preference relation, in individual and group decision problems. … An approach to derive priority vectors from fuzzy best-worst matrices is developed. Rules to derive a fuzzy reciprocal preference relation from an incomplete fuzzy reciprocal preference relation are presented. Illustrative examples are provided for each situation.” (Annibal Parracho Sant’Anna, zbMATH 1537.91003, 2024)
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