Integrated MADM approach based on extended MABAC method with Aczel-Alsina generalized weighted Bonferroni mean operator
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F24%3A50021917" target="_blank" >RIV/62690094:18450/24:50021917 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/content/pdf/10.1007/s10462-024-10980-3.pdf?utm_source=scopus&getft_integrator=scopus" target="_blank" >https://link.springer.com/content/pdf/10.1007/s10462-024-10980-3.pdf?utm_source=scopus&getft_integrator=scopus</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10462-024-10980-3" target="_blank" >10.1007/s10462-024-10980-3</a>
Alternative languages
Result language
angličtina
Original language name
Integrated MADM approach based on extended MABAC method with Aczel-Alsina generalized weighted Bonferroni mean operator
Original language description
Currently, q-rung orthopair (q-ROF) set theory is one of the most effective set theories in dealing uncertainty associated with imprecise information. In complex decision-making problems, input variables can be described by q-ROF numbers to cope ambiguity. While, generalized weighted Bonferroni mean (GWBM) operator can reflect correlation among input arguments. Aczel–Alsina operations underline fair and accurate evaluation of decision-makers. Harnessing these benefits, a pioneering extension of the GWBM operator based on Aczel–Alsina operations is introduced. Simultaneously, a novel generalized distance measure is crafted, drawing inspiration from Dice and Jaccard similarities. Beside these, using stepwise weight assessment ratio analysis (SWARA) and multi-attribute border approximation area comparison (MABAC) methods, this study pioneers an integrated method, q-ROF-SWARA-MABAC for assessing and prioritizing factors and alternatives on q-ROF environment. Later, with the suggested model, a case study on high-speed rail corridor (HSRC) for India is solved, revealing Varanasi-Howrah HSRC as the most preferable choice. Moving forward, detailed sensitive analysis of suggested model is performed to explore the pertinence and supremacy. Eventually, the outcomes manifest that novel framework is flexible, reliable, accurate and could be viable option to consider for future use. © The Author(s) 2024.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Artificial Intelligence Review
ISSN
0269-2821
e-ISSN
1573-7462
Volume of the periodical
58
Issue of the periodical within the volume
1
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
45
Pages from-to
"Article number: 27"
UT code for WoS article
001365464200002
EID of the result in the Scopus database
2-s2.0-85210527627