Balancing trade-offs between first three moments of completion times for one-stage production
Why this work is in the frame
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Bibliographic record
Abstract
For one-stage production, operations management faces the following three challenges to make decisions, which are inconsistencies between key performance indicators (KPIs) for production, trade-offs between the expected return and the risk in modern portfolio theory (MPT), and uncertainties in processing times. Traditionally, total completion time ( TCT ) and variance of completion times ( VCT ) are two KPIs for one-stage production scheduling, which relate to the first and second moments of completion times, respectively. We question whether the third moment of completion times is good to address the three challenges. In this paper, we introduce the skewness of completion times ( SCT ) in scheduling, and propose the ToB( a , b ) heuristics for trade-off balancing. Through case studies with 5 levels of processing time uncertainties and compared to existing ToB( α ) heuristics which balance trade-offs between TCT and VCT , we show that our ToB( a , b ) heuristics dominate ToB( α ) heuristics in terms of smaller expected values ( E ) of weighted sum of deviations from the best solutions of KPIs and smaller risks ( σ ) associated with these KPI deviations. Therefore, our ToB( a , b ) heuristics are more robust to balance trade-offs between the three KPIs under processing time uncertainties.
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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 | 0.000 | 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 it