Project Delivery Systems Selection for Capital Projects Using the Analytical Hierarchy Process and the Analytical Network Process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.027 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it