Version 1
: Received: 18 October 2024 / Approved: 18 October 2024 / Online: 18 October 2024 (15:35:57 CEST)
How to cite:
Rahman, S.; Alali, A. S.; Baro, N.; Ali, S.; Kakati, P. A Novel TOPSIS Framework for Multi-Criteria Decision Making with Random Hypergraphs: Enhancing Decision Processes. Preprints2024, 2024101511. https://doi.org/10.20944/preprints202410.1511.v1
Rahman, S.; Alali, A. S.; Baro, N.; Ali, S.; Kakati, P. A Novel TOPSIS Framework for Multi-Criteria Decision Making with Random Hypergraphs: Enhancing Decision Processes. Preprints 2024, 2024101511. https://doi.org/10.20944/preprints202410.1511.v1
Rahman, S.; Alali, A. S.; Baro, N.; Ali, S.; Kakati, P. A Novel TOPSIS Framework for Multi-Criteria Decision Making with Random Hypergraphs: Enhancing Decision Processes. Preprints2024, 2024101511. https://doi.org/10.20944/preprints202410.1511.v1
APA Style
Rahman, S., Alali, A. S., Baro, N., Ali, S., & Kakati, P. (2024). A Novel TOPSIS Framework for Multi-Criteria Decision Making with Random Hypergraphs: Enhancing Decision Processes. Preprints. https://doi.org/10.20944/preprints202410.1511.v1
Chicago/Turabian Style
Rahman, S., Shakir Ali and Pankaj Kakati. 2024 "A Novel TOPSIS Framework for Multi-Criteria Decision Making with Random Hypergraphs: Enhancing Decision Processes" Preprints. https://doi.org/10.20944/preprints202410.1511.v1
Abstract
In today’s complex decision-making landscape, multi-criteria decision-making (MCDM) frameworks play a crucial role in addressing conflicting criteria. This paper introduces a novel framework that combines the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with random hypergraphs to enhance decision processes. Traditional MCDM methods often face challenges due to uncertainty and interdependencies among criteria. Our approach leverages random hypergraphs to better capture the relationships between criteria, offering a refined representation of decision problems. We delve into the theoretical foundations of this framework, detailing its algorithmic implementation and methodologies for evaluating alternatives under uncertainty. Performance comparisons illustrate the advantages of the proposed TOPSIS framework, emphasizing how random hypergraphs enrich TOPSIS’s analytical capabilities. This research advances the theoretical understanding of MCDM frameworks while providing practical insights for practitioners seeking robust solutions in complex and uncertain decision-making environments.
Keywords
Random Hypergraph; TOPSIS; MCDM
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
