Graphical Approach for Manpower Planning in Infrastructure Networks
Bibliographic record
Abstract
Infrastructure networks such as highways and pipelines have recently been at the center of attention for contractors and owner organizations. Due to their large size and their repetitive/distributed nature, construction and/or maintenance operations for such networks become complex tasks that require huge resources, particularly manpower. In order to provide a transparent tool for quick manpower planning and sensitivity analysis, a graphical approach (using nomographs) is introduced in this paper. The nomographs encode the mathematical formulation, and the results of many optimization experiments, of a distributed model for scheduling large projects with multiple sites. Accordingly, the nomographs can be readily utilized by practitioners to estimate the manpower needed to meet a predefined deadline, under anticipated network-level risks due to unfavorable site conditions. Details on the development of the nomographs are presented in the paper along with an example to demonstrate their usefulness for supporting manpower planning decisions and for what-if analysis. The nomographs also present researchers with a simple yet powerful approach to make research results readily usable by practitioners.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".