Two-warehouse optimized inventory model for time dependent decaying items with ramp type demand rate under inflation
Bibliographic record
Abstract
This paper deals with developing an inventory model for two warehouses.In today's business era, there are various types of conditions such as discounts, bulk storage and seasonal products forcing the buyer to purchase the order more than owned warehouse capacity.To store the excess unit of purchase order, buyer arrange additional storage space called as rented warehouse.It is known that the demand of the seasonal products (as woolen garments) increases at the beginning of the season up to a certain time and then stabilizes to a constant rate for the remaining time of the season.The ramp type demand rate forces the buyer to store a higher quantity of the product at the beginning of the season.Most of the physical goods undergo decay or deterioration over time so we study deteriorating seasonal products in this paper.This two warehouse inventory model is developed with inflation and shortages.The model starts with rent warehouse, in first rent warehouse's inventory level is depleted due to demand and deterioration.At this time own warehouse is depleted due to deterioration only.But after that the inventory level of owned warehouse is depleted due to both demand and deterioration.The shortages are considered in owned warehouse, which is partially backlogged.Numerical solution of the model is obtained to verify the optimal solution.Comprehensive sensitivity analysis has been carried out for showing the effect of variations in the parameters.The model is solved analytically by minimizing the total cost.
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.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".