Stochastic Multi-Product Inventory Models with Limited Storage
Bibliographic record
Abstract
This paper studies multi-product inventory models with stochastic demands and a warehousing constraint. Finite horizon as well as stationary and nonstationary discounted cost infinite horizon problems are addressed. Existence of optimal feedback policies is established under fairly general assumptions. Furthermore, the structure of optimal policies is analyzed when ordering cost is linear and inventory/backlog cost is convex. The optimal policies generalize the base-stock policies in the single-product case. Finally, in the stationary infinite horizon case, a myopic policy is proved to be optimal if the product demands are independent and cost functions are separable. (MULTIPRODUCT INVENTORY MODEL, WAREHOUSING CONSTRAINT, DYNAMIC PROGRAMMING, FINITE AND INFINITE HORIZON, GENERALIZED BASE-STOCK POLICIES, MYOPIC POLICIES) This research was supported by the NSERC grant A4619. The paper has benefited from the comments of the participants in the OM seminar at University of Toronto, where ...
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".