Integrated MADM approach based on extended MABAC method with Aczel-Alsina generalized weighted Bonferroni mean operator

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Integrated MADM approach based on extended MABAC method with Aczel-Alsina generalized weighted Bonferroni mean operator

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Integrated MADM approach based on extended MABAC method with Aczel-Alsina generalized weighted Bonferroni mean operator

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Artificial Intelligence Review

ISSN

0269-2821

e-ISSN

1573-7462

Svazek periodika

58

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

45

Strana od-do

"Article number: 27"

Kód UT WoS článku

001365464200002

EID výsledku v databázi Scopus

2-s2.0-85210527627