Simple Policies for Managing Flexible Capacity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In many scenarios, a fixed capacity is shared flexibly between multiple products. To manage such multiproduct systems, firms need to make two sets of decisions. The first one requires setting an inventory target for each product, and the second requires dynamically allocating the scarce capacity among the products. It is not known how to make these decisions optimally. In this paper, we propose easily implementable policies that have both theoretical and practical appeal. We first suggest simple and intuitive allocation rules that determine how such scarce capacity is shared. Given such rules, we calculate the optimal inventory target for each product. We demonstrate analytically that our policies are optimal under two asymptotic regimes represented by high service levels (i.e., high shortage costs) and heavy traffic (i.e., tight capacity). We also demonstrate that our policies outperform current known policies over a wide range of problem parameters. In particular, the cost savings from our policies become more significant as the capacity gets more restrictive. The online appendix is available at https://doi.org/10.1287/msom.2017.0648 .
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it