Project Delivery Systems Selection for Capital Projects Using the Analytical Hierarchy Process and the Analytical Network Process
Bibliographic record
Abstract
In this paper, analytical hierarchy process (AHP) and analytical network process (ANP) are compared as methods for determining relative weights of factors in selecting the most suitable project delivery system (PDS) for capital projects. The AHP considers the elements of each cluster as only affecting the elements of one other cluster and being affected by elements of one other cluster, whereas the ANP considers additional dependencies among elements. In selecting a PDS, interdependencies among factors of different categories exist, therefore ANP is considered here for its expected suitability. ANP requires additional effort in constructing a network and additional judgments. A network was developed by adding dependencies between specific elements to a hierarchy. Both methods were applied to a case study. ANP generally favored the factors that influenced additional elements through network connections. In the example analyzed, the overall ranking of factors by ANP was not consistent with all the pairwise comparisons, which reveals a limitation of the ANP. This paper augments the research in evaluating the appropriateness of AHP versus that of ANP in selecting the most suitable project delivery system. It provides an example of how the priorities of factors by hierarchy and by network differ for an actual decision problem.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".