Optimal Pricing and Capacity Under Well-Defined and Well-Known Deterministic Demand Fluctuations
Bibliographic record
Abstract
Fluctuations in demand require diverse considerations with respect to planned capacity. At peak periods, decreased capacity may result in supply shortages and thus in lower revenues and unachievable profits. In contrast, smaller capacity at off-peak periods reduces the substantial costs of large and unutilized capacity. The questions to be addressed ask (i) what the optimal pricing policies are at peak and off-peak periods; (ii) what the optimal capacity is for profit maximization of the supplier; and furthermore (iii) how the shifting of demands from peak to off-peak periods may reduce fluctuation and impact profits. The present paper develops a model that compares two cases. In Case 1 it is not possible to transfer partial demand from a peak period to an off-peak period, while in Case 2 it is possible to do so. The comparison between the cases illustrates various results, some of which are less intuitive than others. For instance, a larger gap between the peak and off-peak periods leads to a larger optimal capacity in Case 1 than in Case 2. However, a smaller gap presents a different picture. When there is less willingness to switch demand between the periods, the capacity of Case 2 is larger than that of Case 1.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".