Chapters
Optimizing the blood supply chain: A data-driven approach to improve blood product utilization and patient safetyDownload thesis from university repository (when available)
Chapter 1
Introduction
Part I Reducing alloimmunization risks through extended red blood cell matching
Chapter 2
Introducing a new model for matching extensively typed red blood cells
Chapter 3
Considering patiënt alloimmunization risk for extensive matching
Chapter 4
Optimizing matching policies to reduce long-term immune responses
Chapter 5
Investigating the use of Deep Q-learning to find optimal matching policies
Part II Improving efficiency by optimizing parts of the supply chain
Chapter 6
Determining optimal locations for blood distribution centers
Chapter 7
Developing a robust autonomous method for blood demand forecasting
Chapter 8
Using deep reinforcement learning to find platelet inventory-allocation policies
Chapter 9
Disscussion
