A distributionally robust optimization approach for stochastic elective surgery scheduling with limited intensive care unit capacity

DOI10.1016/j.ejor.2020.09.001zbMath1487.90340OpenAlexW3083431672MaRDI QIDQ2029907

Karmel S. Shehadeh, Rema Padman

Publication date: 4 June 2021

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2020.09.001

zbMATH Keywords

mixed-integer programmingdownstream resource constraintOR in health servicessurgery schedulingdistributionally robust optimizationcolumn-and-constraint generation

Mathematics Subject Classification ID

Mixed integer programming (90C11) Stochastic programming (90C15) Stochastic scheduling theory in operations research (90B36) Robustness in mathematical programming (90C17)

Related Items (7)

Stochastic optimization approaches for elective surgery scheduling with downstream capacity constraints: models, challenges, and opportunities ⋮ Data-driven distributionally robust surgery planning in flexible operating rooms over a Wasserstein ambiguity ⋮ Adopting a hierarchical diagnosis and treatment system to optimize elective surgery scheduling ⋮ A data-driven distributionally robust approach for the optimal coupling of interdependent critical infrastructures under random failures ⋮ Wasserstein distributionally robust chance-constrained program with moment information ⋮ Stochastic weekly operating room planning with an exponential number of scenarios ⋮ Wasserstein distributionally robust chance-constrained optimization for energy and reserve dispatch: an exact and physically-bounded formulation

Uses Software

• RSOME

Cites Work

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• Integer Programming Approaches for Appointment Scheduling with Random No-Shows and Service Durations
• Robust Defibrillator Deployment Under Cardiac Arrest Location Uncertainty via Row-and-Column Generation

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