Optimal ordering policy and preservation technology for deteriorating items with maximum lifetime under a resilient hybrid payment decision
Bibliographic record
Abstract
This study demonstrates an inventory system with items changing value over time under various realistic environments. It is assumed that the time-varying deterioration rate depending on the maximum lifetime of items and the items exceeding the maximum lifetime are regarded as scarp and no longer serviceable. As a result, the retailer will invest in preservation technology to reduce the reckless deterioration. On the other hand, the retailer receives an upstream advance-cash-credit payment plan from the supplier while offering a downstream cash-credit payment plan to customers to stimulate sales. As above description, we incorporate the relevant phenomena into the proposed inventory model, then the primary objective is to determine the replenishment cycle time and the preservation technology which maximizes the retailer's total profit. Consequently, the contributions of this study have three parts as follows: (1) Addressing the economic (total profit) and technology (preservation technology) impacts simultaneously; (2) The propositions and theorems are derived along with a solution procedure; (3) It is proved that the optimal solution not only exists but also is unique under some conditions. Next, an algorithm is developed which simplifies the search for the sustainable optimal ordering strategies. Numerical examples and a sensitivity analysis are elaborated to validate the mathematical formulation. Findings are summarized and managerial implications are also discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".