Continuous-Time Models For Production Scheduling In Constrained Subcontracting Conditions
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Bibliographic record
Abstract
AbstractThis paper focuses on an optimal control approach to continuous-time multi-item scheduling of parallel flexible machines in typical subcontracting conditions. The conditions comprise subcontracting of: constant in-time amount of items along the planning horizon; arbitrary changing in-time amounts; and limited-change in-time amounts of items. Mathematical formulations are presented to model the three typical subcontracting conditions and are studied with the aid of the maximum principle. Based on the properties of the optimal solutions derived, efficient time-decomposition methods are suggested for solving the corresponding problems.RésuméCet article se concentre à la portée de controle optimale pour la prévision de plusieurs articles à temps continuel de quelques machines flexibles parallelement à des conditions caractérisées de sous-traitance. Ces conditions de sous-traitance comprenent: une quantité d’articles constante à temps pendant la durée du plan; des quantités arbitraires changeantes à temps, et des quantités d’articles dont le changement de temps est limité. Les formulations mathematiques sont proposées pour modeler les trois conditions de sous-traitance étudiées à l’aide de principe maximum. En se basant sur les qualités des solutions optimales qui ont été trouvées, des méthodes effectives de la décomposition à temps sont suggerées à la résolution des problèmes correspondants. Additional informationNotes on contributors’ Konstantin KoganKonstantin Kogan obtained his Ph.D. from the Moscow Institute of Mechanization and Power Engineering, Russia. He is currently a Senior Lecturer in the Department of Computer Systems at Holon Center for Technological Education and a visiting Senior Lecturer at the Department of Industrial Engineering at Tel-Aviv University. His research interests are in production control and scheduling of flexible manufacturing systems.
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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 | 0.000 |
| 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 | 0.002 |
| 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