A hybrid of Delphi, AHP and TOPSIS Methods for project portfolio management
Bibliographic record
Abstract
Due to the importance and complexity of the portfolio management issue, over 100 different techniques have already been presented.In general, the final result of these tools is to create a prioritized list of the projects that must be selected for allocating resources.The use of financial strategies may be misleading in some cases, and it is necessary to combine these methods with other methods such as strategic approaches in order to guarantee a balanced portfolio toward the organizational strategies.On the other, categorizing projects into different baskets allows the organizations to select, evaluate and prioritize the projects in a subset using a set of similar criteria and techniques.In this article, by choosing agriculture sector as a case study, an attempt has been made to study the evaluation, ranking and management of projects with investment classifying strategy of the projects using Delphi, TOPSIS and AHP methods.The results reveal that in similar cases we can use the presented model by determining the type of activity and investment and localization of the indexes.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".