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Bayesian Chance-Constrained Planning Under Limited Sampling for Sectional Warping
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DOI：10.3390/appliedmath6040055.png)
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Abstract
En 中文
Sectional warping requires selecting a final operating length when only a small sample of residual cone masses can be measured. This paper proposes a Bayesian chance-constrained planning rule that combines a conjugate log-space model with fast posterior predictive simulation of the population minimum to recommend a risk-limited band length. The method provides a transparent risk parameter, efficient computation, and direct comparison with heuristic, bootstrap, distribution-free, and tail-model baselines. In an industrial-like synthetic study, the Bayesian policy reduced the mean remainder relative to a tuned sample-minimum rule while maintaining controlled shortage risk, and the results clarify why fully distribution-free guarantees are impractical under typical sampling budgets.
Keywords:
sectional warping
creel
Bayesian decision theory
chance constraint
order statistics
tolerance limits
bootstrap
extreme value theory
waste minimization
Journal
A
IF:
0.7
Papers:
111
Citations:
0
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Cited Papers