A heuristic method to determine optimum degree of activity accelerating and overlapping in schedule compression
Bibliographic record
Abstract
Schedule compression is an attempt to reduce project durations for early delivery, or to catch up on occurred delays. This is usually performed using “crashing”, “overlapping” and “substitution” of activities. “Crashing” is shortening task duration by adding more resource hours. “Substitution” is changing the method or tool by which the activity is performed. “Overlapping” is performing sequential activities in parallel. Few studies can be found in the literature that compare costs and benefits of each technique and recommend the optimum combination of these techniques in project schedules. In fact, the implications of each technique are inherently different since crashing and substitution impose extra cost whereas overlapping adds the risk of changes and rework. Therefore, it is a challenge to develop a reliable analytical tool for performing time–cost trade-offs using the three methods. There are also few studies in the literature to propose a practical method for performing schedule compression by combining these techniques. The purpose of this paper is to discuss these schedule compression techniques in detail, and present and validate a heuristic method to perform the combination of crashing, overlapping and substitution in a project schedule, to reach the maximum benefit while meeting the project target dates.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".