Vendor-managed inventory for joint replenishment planning in the integrated qualitative supply chains: generalised benders decomposition under separability approach
Bibliographic record
Abstract
Vendor managed inventory (VMI) is a well-established supply chain (SC) practice where the supplier is responsible for managing the inventory level at the retail point. In this paper, we engage the VMI in a multi-product integrated qualitative supply chain in order to make the best joint replenishment policy. Accordingly, a screening process classifies the products into good and defective products. This process imposes the reworking cost, the disposal cost, the holding cost, and the screening cost on the model. A penalty mechanism will penalise the supplier, if the replenishment quantity exceeds the certain upper bound agreed upon the VMI contract. The model comes with some real stochastic constraints. The mathematical formulation of the model is stochastic, Mix Integer Nonlinear Programming (MINLP), and hard to solve. In this regards, Generalised Benders Decomposition (GBD) under separability approach is employed for optimising the decision variables, including the number of shipments and shipment quantities. The results of numerical analyses showed an excellent performance of the provided method with respect to the optimality criteria like number of taken iterations, optimality error, infeasibility, and complementarity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".