Transshipment of Inventories: Dual Allocations vs. Transshipment Prices
Why this work is in the frame
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Bibliographic record
Abstract
We study a newsvendor game with transshipments, in which n retailers face a stochastic demand for an identical product. Before the demand is realized, each retailer independently orders her initial inventory. After the demand is realized, the retailers select an optimal transshipment pattern and ship residual inventories to meet residual demands. Unsold inventories are salvaged at the end of the period. We compare two methods for distribution of residual profit—transshipment prices (TPs) and dual allocations (DAs)—that were previously analyzed in literature. TPs are selected before the demand is known, and DAs, which are obtained by calculating the dual prices for the transshipment problem, are calculated after observing the true demand. We first study the conditions for the existence of the Nash equilibria under DA and then compare the performance of the two methods and show that neither allocation method dominates the other. Our analysis suggests that DAs may yield higher efficiency among “more asymmetric” retailers, whereas TPs work better with retailers that are “more alike,” but the difference in profits does not seem significant. We also link expected dual prices to TPs and use those results to develop heuristics for TPs with more than two symmetric retailers. For general instances with more than two asymmetric retailers, we propose a TP agreement that uses a neutral central depot to coordinate the transshipments (TPND). Although DAs in general outperform TPND in our numerical simulations, its ease of implementation makes TPND an attractive alternative to DAs when the efficiency losses are not significant (e.g., high critical fractiles or lower demand variances).
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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