A Comparative Analysis of Multi-Criteria Decision-Making Methods
Received: 16 June 2025 | Revised: 30 June 2025 | Accepted: 9 July 2025 | Online: 28 July 2025
Corresponding author: Vu Quang Minh
Abstract
Multi-Criteria Decision-Making (MCDM) techniques have found widespread application across diverse fields. The rapid evolution of MCDM algorithms has led to the development of hundreds of methods, each employing a distinct approach. Due to the inherent algorithmic differences, various MCDM methods often produce divergent results when applied to the same problem. This study presents a comparative analysis of 4 such methods: Root Assessment Method (RAM), Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA), Faire Un Choix Adéquat (FUCA), and Collaborative Unbiased Rank List Integration (CURLI). The context of this evaluation is the ranking of 30 Vietnamese banks based on 6 criteria: capital adequacy, asset quality, management capability, earning ability, liquidity, and sensitivity to the market risk. Prior to this analysis, these banks had also been ranked by the CAMELS rating system. The CAMELS rankings serve as a benchmark to assess the performance of the RAM, MOORA, FUCA, and CURLI methods. This study’s findings indicate that FUCA and CURLI are the most suitable methods for this application. On the contrary, RAM and MOORA proved unsuitable when compared to the CAMELS ranking.
Keywords:
MCDM, bank ranking, CAMELS rating systemDownloads
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