Simulation of scheduling logic using dynamic functions
Bibliographic record
Abstract
Since the late 1950s, researchers have studied the soft logic of scheduling, in particular the precedence constraint between activities used to compute the critical path. However, by proposing only external constraints and simulating work production through lags, the precedence logic lacks precision. These gaps diminish the reliability of the schedule and impair the internal monitoring of activity interdependencies. Chronographic logic addresses such limitations by introducing the internal division and proposing internal monitoring as a function of production. This paper proposes the concept of probabilistic production-based dynamic functions which would replace internal divisions with a mathematical function that permits the tracking of the dynamic interdependencies between two in-progress activities. A case study compares the overall schedule calculation using traditional precedence logic with the dynamic production-based function. This simulation was designed to investigate the overall impact on the critical path and the criticality of each activity. The result is a new method of implementing scheduling logic that takes into account the impact of the internal changes of workload and allows the use of internal margins. These self-adaptations provide a better simulation of construction-site conditions which help to produce more realistic results.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".