Consignment stock partnership in multi-vendor multi-buyers supply chains
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we present an integrated optimization model for simultaneously addressing the problem of optimizing delivery quantities and determining optimal production lot sizes in a consignment stock partnership between vendors and buyers. The objective is to minimize the total cost of ordering, holding, setup, and transportation in a two-echelon supply chain. We propose three coordination policies to solve this problem. The first policy involves each vendor producing and delivering a batch to all customers in equal and proportional quantities to their demand. The second policy involves vendors only delivering the product upon receiving an order. The third policy involves each vendor making deliveries to all customers, but not necessarily at the same time. Additionally, a fourth model integrating the previous three policies is proposed. Numerical examples demonstrate the benefits of this integrated model, while sensitivity analyses highlight the impact of key parameters on the total cost.
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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 | 0.000 |
| Scholarly communication | 0.000 | 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