TY - JOUR
T1 - A family of fuzzy multi-criteria sorting models FTOPSIS-Sort
T2 - Features, case study analysis, and the statistics of distinctions
AU - Yatsalo, Boris
AU - Radaev, Alexander
AU - Haktanir, Elif
AU - Skulimowski, Andrzej M.J.
AU - Kahraman, Cengiz
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2024/3/1
Y1 - 2024/3/1
N2 - A family of fuzzy multi-criteria sorting models, FTOPSIS-Sort, as a fuzzy extension of Multi-Criteria Decision Analysis (MCDA) ordinary method TOPSIS, is introduced and analyzed. Models from this family differ by approaches to determining functions of fuzzy numbers (approximate computations, standard fuzzy arithmetic, and transformation method) and by methods for ranking of fuzzy numbers (two defuzzification based ranking methods are considered). The features of developing and adjusting Fuzzy TOPSIS (FTOPSIS) models to sorting problematic are presented. The developed models are implemented in the case study on a healthcare supply chain alternative selection problem. For exploring distinctions in sorting alternatives by FTOPSIS-Sort models, the special algorithms have been developed along with their integrating with Monte Carlo simulation of a large number of input scenarios, each of which is a separate (and independent of the others) multicriteria problem on sorting alternatives. The results of such an analysis demonstrate a significant distinction in sorting alternatives by different FTOPSIS-Sort models. The latter has theoretical, methodological, and applied significance within the use of Fuzzy TOPSIS (Fuzzy MCDA) sorting models.
AB - A family of fuzzy multi-criteria sorting models, FTOPSIS-Sort, as a fuzzy extension of Multi-Criteria Decision Analysis (MCDA) ordinary method TOPSIS, is introduced and analyzed. Models from this family differ by approaches to determining functions of fuzzy numbers (approximate computations, standard fuzzy arithmetic, and transformation method) and by methods for ranking of fuzzy numbers (two defuzzification based ranking methods are considered). The features of developing and adjusting Fuzzy TOPSIS (FTOPSIS) models to sorting problematic are presented. The developed models are implemented in the case study on a healthcare supply chain alternative selection problem. For exploring distinctions in sorting alternatives by FTOPSIS-Sort models, the special algorithms have been developed along with their integrating with Monte Carlo simulation of a large number of input scenarios, each of which is a separate (and independent of the others) multicriteria problem on sorting alternatives. The results of such an analysis demonstrate a significant distinction in sorting alternatives by different FTOPSIS-Sort models. The latter has theoretical, methodological, and applied significance within the use of Fuzzy TOPSIS (Fuzzy MCDA) sorting models.
KW - Fuzzy MCDA
KW - Fuzzy multicriteria sorting
KW - Fuzzy numbers
KW - Fuzzy ranking methods
KW - Fuzzy TOPSIS
KW - Monte Carlo simulation for distinctions analysis
UR - http://www.scopus.com/inward/record.url?scp=85171613679&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2023.121486
DO - 10.1016/j.eswa.2023.121486
M3 - Article
AN - SCOPUS:85171613679
SN - 0957-4174
VL - 237
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 121486
ER -