Managing the unknown: A distributionally robust model for the admission planning problem under uncertain length of stay

Batista, Ana Paula; Pozo, David; Vera, Jorge

doi:10.1016/j.cie.2020.107041

Cited by 3 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The complex nature of SVIs, coupled with the need to handle distributional uncertainty, presents unique challenges that require specialized methodologies and algorithms. Developing robust learning and optimization techniques for distributionally robust SVIs is crucial to enhance decision-making processes in various domains (Sapkota et al, 2021) (Kuhn et al, 2019)(Van Parys et al, 2021 (Batista et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Robust learning and optimization in distributionally robust stochastic variational inequalities under uncertainty

Sihotang,

Marsoit,

Marsoit

2023

emod

View full text Add to dashboard Cite

Robust learning and optimization in distributionally robust stochastic variational inequalities under uncertainty is a crucial research area that addresses the challenge of making optimal decisions in the presence of distributional ambiguity. This research explores the development of methodologies and algorithms to handle uncertainty in variational inequalities, incorporating a distributionally robust framework that considers a range of possible distributions or uncertainty sets. By minimizing the worst-case expected performance across these distributions, the proposed approaches ensure robustness and optimality in decision-making under uncertainty. The research encompasses theoretical analysis, algorithm development, and empirical evaluations to demonstrate the effectiveness of the proposed methodologies in various domains, such as portfolio optimization and supply chain management. The outcomes of this research contribute to the advancement of robust optimization techniques, enabling decision-makers to make reliable and robust decisions in complex real-world systems

show abstract

Section: Introductionmentioning

confidence: 99%