Mathematical analytic techniques for determining the optimal ordering strategy for the retailer under the permitted trade-credit policy of two levels in a supply chain system
Bibliographic record
Abstract
In this article, we explore a certain kind of two-level trade credit in order to reflect the real-life situations. With this objective in view, we consider the case when the supplier offers two-level trade credit for the retailer for settling the account. If the retailer pays off all accounts at the end of the first credit period, then he/she can utilize the sales revenue to earn interest until the inventory cycle time. On the other hand, if the retailer cannot pay off the unpaid balance at the end of the first credit period, then he/she can decide to pay off the unpaid balance either after the end of the first credit period or after the second credit period. Here, in this situation, the retailer reduces the financed loan from constant sales and revenue received gradually and he/she still can utilize the sales revenue to earn interest when he/she pays off all accounts. Maximizing the profit is used as the objective to develop the inventory model. Based upon the obtained properties of the optimal solution, two theorems are developed to determine the optimal replenishment policy. Finally, computational developments are presented in order to illustrate numerically the main theoretical results which are proven in this article by using some mathematical solution procedures.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".