BAYESIAN INVENTORY CONTROL UNDER UNCERTAIN DEMAND: A DATA-DRIVEN LEARNING APPROACH

Effective inventory management under uncertainty remains a central challenge in supply chain systems, particularly when demand is stochastic, non-stationary, or partially observed. Classical inventory control methods rely on fixed probabilistic assumptions that often fail to capture evolving demand...

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Hlavní autoři:	Marcel ILIE, Augustin SEMENESCU
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Editura Academiei Oamenilor de Știință din România 2026-05-01
Edice:	Annals: Series on engineering sciences (Academy of Romanian Scientists)
Témata:	bayesian inventory control; stochastic inventory management; (s s) policy; demand forecasting; uncertainty quantification; machine learning forecasting models
On-line přístup:	https://aos.ro/doi/engineering-2026-1-36-bayesian-inventory-control-under-uncertain-demand-a-data-driven-learning-approach
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