Effect of learning and salvage worth on an inventory model for deteriorating items with inventory-dependent demand rate and partial backlogging with capability constraints
Bibliographic record
Abstract
In this paper, an inventory model is built with inventory-dependent demand rate and twowarehouses in which demand rate is a polynomial of current inventory level. Moreover, it is typically the case in which the value of system engaged in repetitive operations decreases. Thus, the impact of learning from repetitive method cannot be unnoticed while developing the inventory model with two-level storage. Here, we tend to assume that the capability of the own warehouse, holding value of own and rented warehouse is partly constant and partly decreasing in every cycle, merit to learning impact. Impact of salvage worth is additionally thought of for the deteriorating things. Additionally, we tend to give shortages and assume that the backlogging demand rate depends on the period of the stock-out. The solution is found with the help of some numerical examples. Sensitivity analysis in relation to numerous parameters is also shown.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".