Modelling and optimal lot-sizing of integrated multi-level multi-wholesaler supply chains under the shortage and limited warehouse space: generalised outer approximation
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Bibliographic record
Abstract
Optimal lot-sizing policy in supply chain (SC) has an important role in companies applying SC management to their system. An excellent lot-sizing policy will control and manage the inventory costs of SCs. By managing lot sizes in the SCs, companies become capable of bringing down additional costs and delivering extra value to the consumers. In this paper, a multi-product, multi-wholesaler, multi-level, and integrated SC under the shortage and the limited warehouse space is modelled. In this model, there are some real stochastic constraints. The objectives are both, to determine the optimum number of lots and the optimum lot volumes in order to minimise the total cost of SC, while the stochastic constraints are satisfied. All of the products are single-stage and the shortage is allowed for products in each one of the chain levels. Resources follow normal distributions with known means and variances. The model is mixed integer nonlinear programming (MINLP) type, large-scale and hard to solve. In this regard, generalised outer approximation based on decomposition principles, outer-approximation, and relaxation is utilised to optimise the MINLP model of research. The results and analyses demonstrate that proposed algorithm has excellence and acceptable performance.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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