Optimal lot-sizing with capacity constraints and auto-correlated interarrival times
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Bibliographic record
Abstract
There have been recent advances in using queuing relationships to determine lot sizes that minimize mean flowtimes when multiple product types are being produced at capacity-constrained resources. However, these relationships assume lot interarrival times are independent, which is not the case in most manufacturing scenarios. This study examines the performance lot-sizing optimization relationships based on GI/G/1 relationships when lot interarrival times are auto-correlated. Simulation and response surface modeling are used to experimentally determine lot sizes for a sample problem. The flowtimes for optimal lot sizes determined analytically are found to compare poorly with with the best flowtimes obtained experimentally. An approah is then developed that uses feedback during simulation to adjust parameters within queuing heuristics that support dynamic lot-size optimization. Performance using this approach compares well with the best performance obtained using the much more difficult experimental approach.
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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