Bibliographic record
Abstract
This paper presents a model, designed to optimize scheduling of linear projects. The model employs a two-state-variable, N-stage, dynamic programming formulation, coupled with a set of heuristic rules. The model is resource-driven, and incorporates both repetitive and nonrepetitive activities in the optimization process to generate practical and near-optimal schedules. The model optimizes either project construction duration, total cost, or their combined impact for what is known as cost-plus-time bidding, also referred to as A+B bidding. The model has a number of interesting and practical features. It supports multiple crews to work simultaneously on any activity, while accounting for: (1) multiple successors and predecessors with specified lead and lag times; (2) the impact of transverse obstructions, such as rivers and creeks, on crew assignments and associated time and cost; (3) the effect of inclement weather and learning curve on crew productivity; and (4) variations in quantities of work in repetitive activities from one unit to another. The model is implemented in a prototype software that operates in Windows® environment. It is developed utilizing object-oriented programming, and provides for automated data entry. Several graphical and tabular output reports can be generated. An example project, drawn from the literature, is analyzed to demonstrate the features of the developed model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".