Retailer's optimal ordering policy in the EOQ model with imperfect‐quality items under limited storage capacity and permissible delay
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it