Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering
Nour El Houda Fethellah
Institut Hydrométéorologique de Formation et de Recherches (IHFR)
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Comparative analysis of multi criteria decision making method performance for hotel selection: a meta-decision framework using grey wolf optimization Nour El Houda Fethellah
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1998-2013

Abstract

The selection of an optimal hotel is a classic multi criteria decision making (MCDM) problem involving multiple conflicting criteria. While numerous MCDM methods exist, their results often vary significantly, creating uncertainty for decision-makers. To address this challenge, this study introduces meta-decision framework that leverages the grey wolf optimizer (GWO) to compare thirteen MCDM methods for hotel selection. The framework employs GWO to establishing a common foundation for impartial evaluation. Each method is assessed through a comprehensive suite of metrics quantifying consensus alignment, stability, Pareto efficiency, and ranking quality. The robustness of each method was evaluated using Monte Carlo simulations, while results were aggregated via Borda Count, Jaccard similarity indices, and Pareto efficiency analysis. The results showed that individual methods frequently disagreed. However, a clear consensus emerged: hotel H415 ranked first across seven methods and achieved the highest overall score, confirming it as the best choice. Among the methods, TOPSIS proved the most stable under changing conditions, while SAW and WASPAS aligned most closely with other methods. The proposed ensemble approach mitigates single-method bias, offering a more reliable foundation for complex decision-making and valuable insights for enhancing decision reliability in MCDM applications.
Co-Authors