Optimal inventory policy for the two-level supply chain with defective items
Bibliographic record
Abstract
This project focuses on two-level closed-loop supply chains with defective items. The objective of this project is to develop and design a model that minimizes the total expected cost per unit time, which includes set-up costs, holding costs, transportation/shipping costs, and screening costs of the integrated two-level close-loop supply chain. The model also finds the optimum order size and optimum number of shipments. The buyer screens the products received from the vendor to find the defective items. The holding costs of the defective items at the buyer's end is paid by the vendor. After the screening process, the defective items are shipped back to the vendor and the vendor has to carry the shipping costs of the defective items. Two scenarios may arise: where both the vendor and buyer are domestic or international, where vendor and buyer are located in two different countries. In the case of an international supply chain, exchange rate between two countries has also been considered. In current world since the business growing fast, the inventory management of any business enterprise improving their performance financially by minimizing the holding cost. The analysis shows how the percentage of defective item affects the total expected cost. The project work has an important involvement for improvement in the vendor-buyer correlated high-tech supply chain industries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".