Flexible Activity Relations to Support Optimum Schedule Acceleration
Bibliographic record
Abstract
In construction schedules, a logical relationship between two activities is specified by the relationship type and a fixed lag time. This rigid representation, however, does not consider the situation when two activities have a degree of flexibility in their relationship. Such flexibility, or soft relation, can be very beneficial as it provides a range of overlapping options that can be utilized in situations that require the schedule to be optimally accelerated. This paper thus proposes a formalization of a generic logical relationship (hard or soft) of any type (finish-to-start, etc.) between any two activities. Using the generic representation, modified activity start and finish time computations are presented to accommodate the overlaps associated with soft relations, and are used in a schedule-crashing model. The model determines the optimum combination of activity crashing and overlapping decisions that minimizes project cost without violating the resource constraints. The new activity relationship and crashing model are beneficial to both researchers and practitioners as they facilitate schedule optimization decisions for projects that exercise a good level of overlapping, such as fast-track projects.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".