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A framework to select multi criteria decision making (MCDM) methods for either ranking or choosing problem

Osman Pala

International Journal of Information Technology and Decision Making2026https://doi.org/10.1142/s0219622026500471article
ABDC C
Weight
0.50

What the paper says

Determining the correct tool to perform decision analysis in multi-criteria decision analysis is an important topic. There are numerous methods that can solve a problem in decision science. Practitioners cannot foresee which of these methods they will use, which can produce different results on the same problem. In this study, it is proposed a problem-specific method selection framework in terms of decomposition level, statistical sensitivity, classical sensitivity, and rank reversal for objective decision-making. A total of seventy two hybrid multi criteria decision making methods were used in the study including six objective criteria weighting methods and twelve alternative evaluation techniques. Analysis was carried out through a demonstration example and a real-life application of the ranking problem of the European Union member states in terms of the Economic Freedom Index. It was observed that the framework could make an effective and fathomable evaluation.

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https://doi.org/https://doi.org/10.1142/s0219622026500471

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@article{osman2026,
  title        = {{A framework to select multi criteria decision making (MCDM) methods for either ranking or choosing problem}},
  author       = {Osman Pala},
  journal      = {International Journal of Information Technology and Decision Making},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1142/s0219622026500471},
}

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A framework to select multi criteria decision making (MCDM) methods for either ranking or choosing problem

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.