Modelling and optimal lot-sizing of integrated multi-level multi-wholesaler supply chains under the shortage and limited warehouse space: generalised outer approximation
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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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 | 0.001 |
| 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) | 0.002 | 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".