Hybrid fuzzy MADM project-selection model for diversified construction companies
Bibliographic record
Abstract
Projects are the life blood of construction companies. Appropriate project selection is a crucial multicriteria decision that influences the future of such organizations. This paper presents a construction project-selection model that notes the influences of the current projects of a company or what is called the portfolio effect. The model applies a multistage fuzzy multi-attribute decision making (MADM) method to determine whether one should offer or not offer a tender. The final output of the model is the decision to be made about selecting a project for bidding considering three probable policies: (1) diversification, (2) concentration, or (3) neutral policy. The model has been applied in a case study. Practitioners perceived the model as a useful tool for their project-selection decisions. The results of a statistical experiment indicate significant results in accordance with the model’s comprehensiveness, applicability, reliability, and user-friendliness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".