Project appraisal and selection using the analytic network process
Bibliographic record
Abstract
Where prioritization and selection process of large-scale construction projects is concerned, governments should consider various quantitative and qualitative criteria when choosing the best project alternative. The traditional benefit–cost (BC) analysis has some drawbacks in terms of analyzing qualitative attributes that cannot be easily expressed in monetary terms. To eliminate this limitation, multicriteria decision-making techniques are proposed for the solution of project prioritization problems. In this paper, an analytic network process (ANP) model is developed to demonstrate how the project selection process can be carried out by considering both quantitative and qualitative factors, as well as their interrelations. The decision network is grouped under four subnetworks, namely benefits, costs, opportunities, and risks. The selected alternatives are real highway projects that are in the investment agenda of the Turkish government. Using the proposed network model, four investment alternatives are assessed by a team of experts and achieved results demonstrate that the ranking of project alternatives may significantly change when the ANP model is used instead of the classical B/C approach. Key words: analytic network process (ANP), multi-criteria decision making, project selection, benefit–cost analysis.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".