Retailer's optimal ordering policy in the EOQ model with imperfect‐quality items under limited storage capacity and permissible delay
Bibliographic record
Abstract
With a view to reducing inventory and increase sales, a supplier frequently offers its buyers a permissible delay in payment to attract new retailers for bulk purchase, and so extra storage spaces are needed for the buyers. Moreover, in a real environment, some defective items are produced because not only the production processes but also the inspection processes are not perfect, thereby generating defects then resulting in extra costs. Keeping these facts in mind, this article proposes a profit‐maximizing economic order quantity model that incorporates both imperfect production quality and permissible delay in payments in the case when the own warehouse with limited capacity is not sufficient to store the ordered quantity and, therefore, a rented warehouse is needed to store the excess units over the capacity of the owned warehouse. Mathematical model and solution procedures are developed with major insight into its functional characteristics. Numerical examples and sensitivity analysis are provided to illustrate and analyze the model performances. It is observed that our model has significant impacts on the optimal lot size and the optimal profit of the mathematical model, which is considered in this article.
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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 | 0.003 |
| 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 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".