Optimal inventory and admission policies for drop-shipping retailers serving in-store and online customers
Bibliographic record
Abstract
This article studies the optimal inventory and dynamic admission policies of two physical retailers who, besides selling through their traditional in-store channels, also act as drop-shippers for an online retailer (e-tailer). The e-tailer carries no inventory of its own and always turns to one of the two physical retailers for order fulfillment. The considered scenario is the one in which retailer 1 (R1) and retailer 2 (R2) act as the primary and secondary drop-shippers of the e-tailer, respectively. While trying to maximize their respective revenues, both retailers face the problem of whether or not to accept the e-tailer's order-fulfillment request. It is initially assumed that the initial inventory levels of each retailer are fixed and that R1 shares his inventory information with R2. By adopting a revenue management framework, the dynamic admission policies of both retailers are studied and it is shown that R1 and R2 should implement one-dimensional and two-dimensional threshold policies, respectively. The scenario in which R1 does not share his inventory information with R2 is considered. For this scenario two heuristic policies for R2 are proposed and they are compared to the optimal policy when information is shared. A detailed sensitivity analysis for varying parameter value is presented, which shows the impact of information sharing between the two retailers. Finally, the assumption of fixed initial inventory levels is relaxed and the optimal initial inventory levels of each retailer that maximize their expected profits are determined.
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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 | 0.006 |
| 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 | 0.003 | 0.002 |
| Open science | 0.002 | 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".