Developing Robust Project Scheduling Methods for Uncertain Parameters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A common problem arising in project management is the fact that the baseline schedule is often disrupted during the project execution because of uncertain parameters. As a result, project managers are often unable to meet the deadline time of the milestones. Robust project scheduling is an effective approach in case of uncertainty. Upon adopting this approach, schedules are protected against possible disruptions that may occur during project execution. In order to apply robust scheduling principles to real projects, one should make assumptions close to the actual conditions of the project as much as possible. In this paper, in terms of uncertainty in both activities duration and resources availability, some methods are proposed to construct the robust schedules. In addition, various numerical experiments are applied to different problem types with the aid of simulation. The main purpose of those is to assess the performance of robust scheduling methods under different conditions. Finally, we formulate recommendations regarding the best method of robust scheduling based on the results of these experiments.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it