Detailed Information on Publication Record
2022
On the derivation of weights from incomplete pairwise comparisons matrices via spanning trees with crisp and fuzzy confidence levels
MAZUREK, Jiří and Konrad KULAKOWSKIBasic information
Original name
On the derivation of weights from incomplete pairwise comparisons matrices via spanning trees with crisp and fuzzy confidence levels
Authors
MAZUREK, Jiří (203 Czech Republic, guarantor, belonging to the institution) and Konrad KULAKOWSKI (616 Poland)
Edition
International Journal of Approximate Reasoning, 2022, 0888-613X
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
United States of America
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
RIV identification code
RIV/47813059:19520/22:A0000288
Organization unit
School of Business Administration in Karvina
UT WoS
000860466600003
Keywords in English
Pairwise comparisons; Fuzzy numbers; Priority vector; Spanning tree; Multiple-criteria decision making
Tags
International impact, Reviewed
Links
GA21-03085S, research and development project.
Změněno: 11/4/2023 11:07, Miroslava Snopková
Abstract
V originále
In this paper, we propose a new method for the derivation of a priority vector from an incomplete pairwise comparisons (PC) matrix. We assume that each entry of a PC matrix provided by an expert is also evaluated in terms of the expert’s confidence in a partic- ular judgment. Then, from corresponding graph representations of a given PC matrix, all spanning trees are found. For each spanning tree, a unique priority vector is obtained with the weight corresponding to the confidence levels of entries that constitute this tree. At the end, the final priority vector is obtained through an aggregation of priority vectors achieved from all spanning trees. Confidence levels are modeled by real (crisp) numbers and triangular fuzzy numbers. Numerical examples and comparisons with other methods are also provided. Last, but not least, we introduce a new formula for an upper bound of the number of spanning trees, so that a decision maker gains knowledge (in advance) on how computationally demanding the proposed method is for a given PC matrix