Abstract

In an era where informed decision-making depends on reliable and robust information systems, this paper presents an innovative approach to the evaluation of multi-criteria decision-making problems with unknown criteria weights. Focused on selecting a city electric vehicle for personal use, the study extensively explores criteria weight scenarios within the Stable Preference Ordering Towards Ideal Solution (SPOTIS) method. Using a novel fuzzy ranking concept for ranking definition under multiple evaluation scenarios enhances decision reliability under varied input conditions. Emphasizing the significance of reliable information systems and customer support, this study aims to empower decision-makers with comprehensive insights into complex decision problems.

Recommended Citation

Więckowski, J. & Sałabun, W. (2024). Innovative Information System Approach for Robust Multi-Criteria Decision Making with Unknown Weights. In B. Marcinkowski, A. Przybylek, A. Jarzębowicz, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Harnessing Opportunities: Reshaping ISD in the post-COVID-19 and Generative AI Era (ISD2024 Proceedings). Gdańsk, Poland: University of Gdańsk. ISBN: 978-83-972632-0-8. https://doi.org/10.62036/ISD.2024.69

Paper Type

Poster

DOI

10.62036/ISD.2024.69

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Innovative Information System Approach for Robust Multi-Criteria Decision Making with Unknown Weights

In an era where informed decision-making depends on reliable and robust information systems, this paper presents an innovative approach to the evaluation of multi-criteria decision-making problems with unknown criteria weights. Focused on selecting a city electric vehicle for personal use, the study extensively explores criteria weight scenarios within the Stable Preference Ordering Towards Ideal Solution (SPOTIS) method. Using a novel fuzzy ranking concept for ranking definition under multiple evaluation scenarios enhances decision reliability under varied input conditions. Emphasizing the significance of reliable information systems and customer support, this study aims to empower decision-makers with comprehensive insights into complex decision problems.