Hybrid MTS/MTO production scheduling with cloud orders: a mathematical model based on an empirical study
Bibliographic record
Abstract
Integrated optimization for production systems plays a vital role in today’s competitive economy because it has always attracted valuable and realistic classes of production problems. So that it has recently been the focus of dominant research studies in the field of Production and Operations Management (POM). In this research, a cloud-based order collection model has initially been presented which works in parallel with the regular order system. Moreover, the predictive maintenance (PdM) is improved more by the preventive strategy. Finally, an Order Acceptance and Scheduling (OAS) model, which uses the hybrid Make to Stock/Order (MTS/MTO) production strategy, is presented as the core of an empirical company’s decision-making and planning. In this model, regular orders and cloud-based orders are produced by MTS and MTO strategies, respectively. The model formulation is a MILP, and its objective function maximizes profit. The model is solved using the exact solution method with CPLEX Solver. Computational results for improving productivity in the company studied after conducting this research are presented compared to its previous statufs. This research proposes a novel insight into the OAS problem through a practical approach and offers a new opportunity to include the cloud and regular order fulfilment integration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".