Bibliographic record
Abstract
Scheduling linear repetitive construction projects, such as highways and pipelines, poses unique challenges due to maintaining crew work continuity. An efficient method is presented, developed to accelerate the delivery of this class of projects so as to meet a specified deadline with least associated cost. The method is simple and ensures crew work continuity. An iterative approach is employed, where, in each iteration, the project schedule is analysed and an activity is identified as the controlling activity. A controlling activity is an activity that if accelerated, would reduce project duration at least additional cost. Upon its identification, the method selects an expediting strategy that would reduce project duration, and the project is rescheduled. Several expediting strategies are considered, including working overtime, double shifts and weekends. The method is implemented in a prototype software that operates in a Windows® environment, providing a user‐friendly graphical interface. It has an open architecture, enabling the user to actively participate in tailoring the generated schedule to suit the requirements of the project at hand. The proposed method accounts for incentives and liquidated damages to aid users in identifying the most cost‐efficient schedule. A relational database model is implemented in Microsoft Access® to store typical crews and their associated productivity, as well as their availability dates. A project, drawn from the literature, is analysed to demonstrate the basic features of the proposed method and highlight its capabilities.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".