0600589 20241115104831.820241111235959.9 85207351706 001349997400001 10.1016/j.cie.2024.110661 12 s. P cav_un_epca*02525540360-8352198Elsevier Decision-making theory Decision support systems Analytic hierarchy process Intransitive preferences Skew-symmetric bilinear representation cav_un_auth*0476093 Carpitella S. US cav_un_auth*0216188 Kratochvíl Václav UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0234872 Pištěk Miroslav UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory CZ Ústav teorie informace a automatizace AV ČR, v. v. i. https://library.utia.cas.cz/separaty/2024/MTR/kratochvil-0600589.pdf https://www.sciencedirect.com/science/article/pii/S0360835224007836 The Analytic Hierarchy Process (AHP) is a widely used method for multi-criteria decision-making that relies on consistency in pairwise comparisons. However, decision-makers often struggle to provide fully consistent judgments in real-world scenarios. This article introduces a decision-making framework that operates independently of consistency. Utilizing the Skew-Symmetric Bilinear representation of preferences allows decision-makers to more accurately evaluate alternatives and criteria, making this framework more applicable in practical settings. The proposed method is validated through practical examples and an in-depth case study in the textile industry, effectively resolving a complex decision-making problem related to acquiring a data analytics tool for supplier selection. The results underscore the robustness and flexibility of this consistency-independent technique as an alternative to traditional AHP methods. WOS BC 10000 10200 10201 2025 3 RVO:67985556 https://hdl.handle.net/11104/0358247 S 110661 A COMPUTERSCIENCE.INTERDISCIPLINARYAPPLICATIONS|ENGINEERING.INDUSTRIAL 7 1.4 4.1 0.03708 1.254 37818 176 1.628 56.74 8.07 21265 Q1 5.800 812 Q1 84.8 1.23 Q1 86.2 2024 85207351706 SCOPUS PUBMED 001349997400001 WOS cav_un_epca*0252554 Computers & Industrial Engineering Roč. 198 č. 1 2024 0360-8352 1879-0550 Elsevier