Using PROMETHEE Method for Multi-Criteria Decision Making: Applications and Procedures
Bibliographic record
Abstract
PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) is one of the main MCDM methods helping decision-makers to investigate a set of alternatives considering different criteria. This method is particularly useful when the decision-makers need to compare a set of alternatives based on multiple criteria. The PROMETHEE method has been applied in various fields, including business, finance, hydrology, and water management. In business, for instance, PROMETHEE can be used to evaluate different investment opportunities based on various criteria such as return on investment, risk, and strategic fit. In water management, PROMETHEE can be used to evaluate alternative strategies for water allocation or pollution control, considering factors such as environmental impact, cost, and social acceptability. Different versions of PROMETHEE have been developed, each with its own specific characteristics and requirements. This paper describes the steps of the PROMETHEE I and II procedures, which are among the most widely used versions of the method. The PROMETHEE I procedure is used for ranking alternatives based on a single criterion, while PROMETHEE II is used for ranking alternatives based on multiple criteria.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".