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

Abstract

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.