Optimal ordering policy for perishable products by incorporating demand forecasts
Bibliographic record
Abstract
Inventory management of perishable products has seen extensive study over the years; the perishable nature capturing the real-world phenomena of expiration after a limited shelf life. Such problems are challenging as they involve balancing demand fulfillment with minimal wastage. An added dimension to such problems, given the rise of machine learning, is that demand predictions are often available. In this paper, we study the structural properties of the optimal ordering policy for a perishable product with a fixed shelf life in a periodic-review single-item inventory system over a finite horizon, where demand predictions are available. We consider both lost-sales and backlogging cases. The objective is to find the optimal ordering policy that minimizes the total expected cost over a finite horizon. The total expected cost consists of linear ordering cost, inventory holding cost, wastage cost, and shortage cost. By using the concept of L ♮ -convexity, we show that under particular assumptions on the demand forecasts, the optimal policy is a state-dependent base-stock policy in which the base-stock values are a function of the system’s state, the inventory level, a vector of current and previous demand forecasts, and previous demand values. Moreover, we explore the monotonicity properties of the optimal policy. The monotonicity properties motivate us to propose a heuristic in which the order quantity is an affine function of the inventory level and forecast-dependent target inventory levels. Numerical results show that the proposed heuristic is effective in minimizing the total cost while maintaining low on-hand inventory levels.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".