Scheduling simulation-based techniques for earned value management on resource-constrained schedules under delayed scenarios
Why this work is in the frame
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Bibliographic record
Abstract
Earned value management (EVM) is widely utilized for project progress monitoring and cost control purposes. However, traditional EVM techniques are intended for ideal scheduling scenarios without considering the effect of activity-level and project-level delays. The EVM fails to account for dynamic changes of project status in terms of project time extension and associated cost overrun, potentially generating misleading project performance tracking indicators. This research proposes a refined EVM approach based on discrete event simulation (scheduling simulation) to tackle complicated resource-constrained scheduling. A case study is used to demonstrate its applications on a resource-constrained schedule under postulated delay scenarios. It is found that this approach is conducive to truthfully reflecting the project performance status given a resource-constrained schedule subject to complicated activity-project delay. Conclusions are drawn by recapitulating the research contributions and addressing the limitations in the end.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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