A two-period lot sizing and pricing model under multiplicative error demand
Bibliographic record
Abstract
In this study, we present a two-period lot sizing and pricing model for a seasonal good, assuming that the random error in the demand function is multiplicative. The proposed model extends the classical price-setting newsvendor problem in which the selling price is held constant during the entire season. We divide the season into two periods and assume that the reseller can change the selling price mid-season. Using the service level approach, we develop a stochastic optimisation procedure for determining the lot size, period-1 price, and the rule for setting period-2 price tailored to the supply available at the beginning of period 2. We show that the two-period recourse price approach has a higher expected profit than the single-period method. The model is also applicable when a warehouse delivers a perishable good at a fixed interval, and the units lying on the shelf at any time have the same best-before date. A retailer may follow such an approach to ensure that the consumers see the items with the same best-before date at any time and do not have to search for an article with the farthest best-before date.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".