Inventory Replenishment for Profit Maximization over a Finite Horizon under One-time Cost Changes
Bibliographic record
Abstract
This article considers the optimal inventory ordering, purchasing and holding policies of the profit-maximization problem, as against the well-known cost-minimization case, over a finite horizon of length H, under two special conditions. First, there is change in at least one of the inventory costs, that is, in the cost of ordering and/or purchasing/holding, at some point, T c < H, during the planning horizon. Second, it is not necessary to satisfy the demand, at a rate of R units per year, for the entire horizon. Rather, the objective is to meet the demand for a period of length H 1 ≤ H. In fact, if the retailer does not have the obligation to meet the entire demand, this article shows the conditions wherein it may be more profitable to meet only a portion or may be even none of the demand. Further, such a determination can be made up front, with H 1 as a decision variable and the optimal policies of the cost-minimization models, by fulfilling the entire demand, will result in lower profits. Numerical examples are included to identify the demand fulfilment and the profit differences between the cost-minimization and profit-maximization optimal policies, under the different one-time cost changes.
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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 | 0.003 | 0.007 |
| 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 | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".