Joint Inventory and Pricing for a One-Warehouse Multistore Problem: Spiraling Phenomena, Near Optimal Policies, and the Value of Dynamic Pricing
Bibliographic record
Abstract
In “Joint Inventory and Pricing for a One-Warehouse Multistore Problem: Spiraling Phenomena, Near Optimal Policies, and the Value of Dynamic Pricing,” Lei, Liu, Jasin, and Vakhutinsky consider a joint inventory and pricing problem with one warehouse and multiple stores with lost sales. The retailer makes a one-time decision on the amount of inventory to be placed at the warehouse at the beginning of the selling season, followed by periodic joint replenishment and pricing decisions for each store throughout the season. The authors first analyze the performance of two popular and simple heuristic policies that directly implement the solution of a deterministic approximation of the original stochastic problem. They show that simple reoptimization of the deterministic approximation may worsen the performance by causing a “spiraling up” movement in expected lost sales quantity. The authors further propose two improved heuristic policies with provably near-optimal performance. In particular, the first policy achieves the best possible performance among all policies that rely on static pricing, and the second policy outperforms the first one because of its use of carefully designed dynamic pricing scheme.
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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 | 0.005 | 0.014 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".