Evaluating Lateral Transshipment Policy in a Two-Echelon Inventory System
Bibliographic record
Abstract
Emergency shipments from higher and/or same echelon levels are one of the popular tools to handle the stock-out position at some warehouse. Our paper deals with a lateral stock transshipment model involving one plant and two warehouses, lateral transshipment is considered as an option at each re-order decision under the standard (r,Q) inventory replenishment policy. We focus on incorporating the above stock transfer feature in the order fulfillment decision and designed an simulation to find the effect of lateral stock transfer policy on various parameters viz. average inventory at each warehouse, average number of stock-out days at each warehouse, total cost (comprising of inventory cost, stock-out cost and transportation cost). The experimental results show that the stock transfer policy has the potential to reduce the total cost, average inventory and average stock-out days. We have also compared the cases where information is shared online or with some delay. The delay is because of serial communication between the supply chain players. The results show that there are benefits of no information delay i.e. online information sharing over the case with information delay.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".