Optimizing Order Decisions for Perishable Product Inventory Management: A Policy-Based Dynamic Programming Approach
Bibliographic record
Abstract
Managing inventory for perishable products presents unique challenges due to limited shelf life, demand uncertainty, and the risk of spoilage. This paper proposes a policy-based Dynamic Programming (DP) approach to find the best policy that optimizes order decisions for perishable product inventory management. The model considers factors such as demand variability, changes in price, and product shelf life to maximize profit while considering waste.A set of 1000 simulation experiments, inspired by real-world perishable product retail scenarios, demonstrates that the Predictive Policy significantly outperforms traditional inventory methods. The results indicate that the Predictive Policy increases total profit by 27% compared to the Base Stock Policy and by 16.9% compared to the Order-Up-To Policy. Additionally, the Predictive Policy reduces expired stock by 69.6% compared to the Random Policy and by 47.5% compared to the Base Stock Policy, highlighting its efficiency in waste reduction. These findings emphasize the effectiveness of data-driven decision-making in improving supply chain profitability and sustainability for perishable goods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".