A fuzzy Einstein-based decision support system for public transportation management at times of pandemic
Abstract
Optimal decision-making has become increasingly more difficult due to their inherent complexity exacerbated by uncertain and rapidly changing environmental conditions in which they are defined. Hence, with the aim of improving the uncertainty management and facilitating the weighting criteria, this paper introduces an improved fuzzy Einstein Combined Compromise Solution (CoCoSo) method-ology. Such a CoCoSo model improves previous CoCoSo proposals by using nonlinear fuzzy weighted Einstein functions for defining weighted sequences. In addition, it proposes a novel algorithm for determining the criteria weights based on the fuzzy logarithmic function, therefore it allows decision -makers a better perception of the relationship between the criteria, as it considers the relationships between adjacent criteria; high consistency of expert comparisons; and enables the definition of weighting coefficients of a larger set of criteria, without the need to cluster (group) the criteria. Nonlinear ...fuzzy Einstein functions implemented in the fuzzy Einstein CoCoSo methodology enable the processing of complex and uncertain information. Such characteristics contribute to the rational definition of compromise strategies and enable objective reasoning when solving real-world decision problems. The efficiency, effectiveness, and robustness of the proposed fuzzy Einstein CoCoSo model are illustrated by a case study to create a conceptual framework to evaluate and rank the prioritization of public transportation management at the time of the COVID-19 pandemic. The results reveal its good performance in determining the transportation management systems strategy.
Keywords:
Public transportation management / Logarithmic additive function / Fuzzy sets / Einstein norms / CoCoSoSource:
Knowledge-Based Systems, 2022, 252Publisher:
- Elsevier, Amsterdam
DOI: 10.1016/j.knosys.2022.109414
ISSN: 0950-7051
WoS: 000853871500012
Scopus: 2-s2.0-85134799911
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Institution/Community
Fakultet organizacionih naukaTY - JOUR AU - Deveci, Muhammet AU - Pamučar, Dragan AU - Gokasar, Ilgin AU - Delen, Dursun AU - Martinez, Luis PY - 2022 UR - https://rfos.fon.bg.ac.rs/handle/123456789/2353 AB - Optimal decision-making has become increasingly more difficult due to their inherent complexity exacerbated by uncertain and rapidly changing environmental conditions in which they are defined. Hence, with the aim of improving the uncertainty management and facilitating the weighting criteria, this paper introduces an improved fuzzy Einstein Combined Compromise Solution (CoCoSo) method-ology. Such a CoCoSo model improves previous CoCoSo proposals by using nonlinear fuzzy weighted Einstein functions for defining weighted sequences. In addition, it proposes a novel algorithm for determining the criteria weights based on the fuzzy logarithmic function, therefore it allows decision -makers a better perception of the relationship between the criteria, as it considers the relationships between adjacent criteria; high consistency of expert comparisons; and enables the definition of weighting coefficients of a larger set of criteria, without the need to cluster (group) the criteria. Nonlinear fuzzy Einstein functions implemented in the fuzzy Einstein CoCoSo methodology enable the processing of complex and uncertain information. Such characteristics contribute to the rational definition of compromise strategies and enable objective reasoning when solving real-world decision problems. The efficiency, effectiveness, and robustness of the proposed fuzzy Einstein CoCoSo model are illustrated by a case study to create a conceptual framework to evaluate and rank the prioritization of public transportation management at the time of the COVID-19 pandemic. The results reveal its good performance in determining the transportation management systems strategy. PB - Elsevier, Amsterdam T2 - Knowledge-Based Systems T1 - A fuzzy Einstein-based decision support system for public transportation management at times of pandemic VL - 252 DO - 10.1016/j.knosys.2022.109414 UR - conv_2763 ER -
@article{ author = "Deveci, Muhammet and Pamučar, Dragan and Gokasar, Ilgin and Delen, Dursun and Martinez, Luis", year = "2022", abstract = "Optimal decision-making has become increasingly more difficult due to their inherent complexity exacerbated by uncertain and rapidly changing environmental conditions in which they are defined. Hence, with the aim of improving the uncertainty management and facilitating the weighting criteria, this paper introduces an improved fuzzy Einstein Combined Compromise Solution (CoCoSo) method-ology. Such a CoCoSo model improves previous CoCoSo proposals by using nonlinear fuzzy weighted Einstein functions for defining weighted sequences. In addition, it proposes a novel algorithm for determining the criteria weights based on the fuzzy logarithmic function, therefore it allows decision -makers a better perception of the relationship between the criteria, as it considers the relationships between adjacent criteria; high consistency of expert comparisons; and enables the definition of weighting coefficients of a larger set of criteria, without the need to cluster (group) the criteria. Nonlinear fuzzy Einstein functions implemented in the fuzzy Einstein CoCoSo methodology enable the processing of complex and uncertain information. Such characteristics contribute to the rational definition of compromise strategies and enable objective reasoning when solving real-world decision problems. The efficiency, effectiveness, and robustness of the proposed fuzzy Einstein CoCoSo model are illustrated by a case study to create a conceptual framework to evaluate and rank the prioritization of public transportation management at the time of the COVID-19 pandemic. The results reveal its good performance in determining the transportation management systems strategy.", publisher = "Elsevier, Amsterdam", journal = "Knowledge-Based Systems", title = "A fuzzy Einstein-based decision support system for public transportation management at times of pandemic", volume = "252", doi = "10.1016/j.knosys.2022.109414", url = "conv_2763" }
Deveci, M., Pamučar, D., Gokasar, I., Delen, D.,& Martinez, L.. (2022). A fuzzy Einstein-based decision support system for public transportation management at times of pandemic. in Knowledge-Based Systems Elsevier, Amsterdam., 252. https://doi.org/10.1016/j.knosys.2022.109414 conv_2763
Deveci M, Pamučar D, Gokasar I, Delen D, Martinez L. A fuzzy Einstein-based decision support system for public transportation management at times of pandemic. in Knowledge-Based Systems. 2022;252. doi:10.1016/j.knosys.2022.109414 conv_2763 .
Deveci, Muhammet, Pamučar, Dragan, Gokasar, Ilgin, Delen, Dursun, Martinez, Luis, "A fuzzy Einstein-based decision support system for public transportation management at times of pandemic" in Knowledge-Based Systems, 252 (2022), https://doi.org/10.1016/j.knosys.2022.109414 ., conv_2763 .