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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".