<![CDATA[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.]]>
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