Balancing trade-offs between first three moments of completion times for one-stage production
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".