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  2. Theses and Dissertations
  3. ASU Electronic Theses and Dissertations
  4. Data-Driven Robust Optimization in Healthcare Applications
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Data-Driven Robust Optimization in Healthcare Applications

Full metadata

Description

Healthcare operations have enjoyed reduced costs, improved patient safety, and

innovation in healthcare policy over a huge variety of applications by tackling prob-

lems via the creation and optimization of descriptive mathematical models to guide

decision-making. Despite these accomplishments, models are stylized representations

of real-world applications, reliant on accurate estimations from historical data to jus-

tify their underlying assumptions. To protect against unreliable estimations which

can adversely affect the decisions generated from applications dependent on fully-

realized models, techniques that are robust against misspecications are utilized while

still making use of incoming data for learning. Hence, new robust techniques are ap-

plied that (1) allow for the decision-maker to express a spectrum of pessimism against

model uncertainties while (2) still utilizing incoming data for learning. Two main ap-

plications are investigated with respect to these goals, the first being a percentile

optimization technique with respect to a multi-class queueing system for application

in hospital Emergency Departments. The second studies the use of robust forecasting

techniques in improving developing countries’ vaccine supply chains via (1) an inno-

vative outside of cold chain policy and (2) a district-managed approach to inventory

control. Both of these research application areas utilize data-driven approaches that

feature learning and pessimism-controlled robustness.

Date Created
2018
Contributors
  • Bren, Austin (Author)
  • Saghafian, Soroush (Thesis advisor)
  • Mirchandani, Pitu (Thesis advisor)
  • Wu, Teresa (Committee member)
  • Pan, Rong (Committee member)
  • Arizona State University (Publisher)
Topical Subject
  • engineering
  • Data-Driven Learning
  • Model Ambiguity
  • Percentile Optimization
Resource Type
Text
Genre
Doctoral Dissertation
Academic theses
Extent
256 pages
Language
eng
Copyright Statement
In Copyright
Reuse Permissions
All Rights Reserved
Primary Member of
ASU Electronic Theses and Dissertations
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.49194
Level of coding
minimal
Note
Doctoral Dissertation Industrial Engineering 2018
System Created
  • 2018-06-01 08:04:51
System Modified
  • 2021-08-26 09:47:01
  •     
  • 1 year 7 months ago
Additional Formats
  • OAI Dublin Core
  • MODS XML

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