Capacity Selection under Uncertainty with Ratio Objectives
Bibliographic record
Abstract
Capacity choice or expansion, whether organic or via mergers and acquisitions, creates firms of widely varying scales. The ex-post profitability of such a transformed firm relative to its original size will typically be evaluated on ratio (rate) measures like earnings per share or profits to asset ratio, as such are the only meaningful ways to compare profitability of firms of substantially different sizes. It thus seems desirable that ex-ante capacity selection decisions will also be guided by a ratio objective. We explore capacity choice decisions under demand uncertainty through the ratio measures: (1) Expected newsvendor (i.e., single period) costs per unit capacity. (2) Expected (newsvendor) profit per unit of capacity. (3) Expected profit per costs of acquiring capacity. (4) A weighted average of a ratio and non-ratio objectives. We allow the capacity-acquisition costs and capacity's production capability to be general non-linear functions. Cost and profit objectives cease to be equivalent when ratio objectives are involved. We show that cost ratio optimizers will select a larger capacity than absolute cost optimizers, while profit ratio optimizers will select smaller capacities than absolute counterpart. We provide examples which show that the difference in optimal capacities, and the associated difference in objective value can be substantial. We also perform comparative statics analyses of the effect of change in item's shortage cost and of stochastic shifts in demand.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".