Two-Stage Robust Optimization with Applications in Health Care and Combinatorial Optimization

dc.contributor.advisor	Hoffman, KarlaBerg, Bjorn
dc.contributor.author	Neyshabouri, Saba
dc.creator	Neyshabouri, Saba
dc.date.accessioned	2017-01-29T01:17:30Z
dc.date.available	2017-01-29T01:17:30Z
dc.date.issued	2016
dc.description.abstract	The development of new robust optimization models is motivated by the need for risk-based decision making in health care operations. Surgery scheduling has attracted a great deal of attention due to its importance in health care outcomes and costs. We apply robust optimization theory to the surgery scheduling problem and downstream capacity planning problem to address important questions regarding the impact of uncertainty in surgery duration and length-of-stay (LOS) in the surgical intensive care units on hospital resource planning and scheduling operations.
dc.format.extent	167 pages
dc.identifier.uri	https://hdl.handle.net/1920/10631
dc.language.iso	en
dc.rights	Copyright 2016 Saba Neyshabouri
dc.subject	Operations research
dc.subject	Decision-dependent uncertainty
dc.subject	Downstream resource constraint
dc.subject	Robust generalized assignment
dc.subject	Surgery scheduling
dc.subject	Two-stage robust optimization
dc.title	Two-Stage Robust Optimization with Applications in Health Care and Combinatorial Optimization
dc.type	Dissertation
thesis.degree.discipline	Systems Engineering and Operations Research
thesis.degree.grantor	George Mason University
thesis.degree.level	Ph.D.

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