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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".