It is necessary to control patient congestion in medical centers during pandemics where
medical demand grows rapidly. Also, managing generated medical waste is critical since …

Rationing of healthcare resources is a challenging decision that policy makers and
providers may be forced to make during a pandemic, natural disaster, or mass casualty …

This paper studies a canonical general scheduling model that captures the fundamental
trade-off between processing jobs and performing diagnostics (testing). In particular, testing …

To leverage prediction models to make optimal scheduling decisions in service systems, we
must understand how predictive errors impact congestion due to externalities on the delay of …

Traditional queuing theory assumes types are known or perfectly observed, and each type is
typically put in its type-specific queue which is prioritized using some version of the …

Many service systems, such as emergency departments (EDs), differentiate among
customer's types to meet specific service level (SL) targets. To meet these targets, some EDs …

Traditional queuing theory assumes that customer types are known or perfectly observed,
and each customer is placed in its type-specific priority queue; we call this type-driven …

In the past decade, artificial intelligence (AI) algorithms have made promising impacts in
many areas of healthcare. One application is AI-enabled prioritization software known as …

This thesis addresses the problem of scheduling with explorable uncertainty on a single
machine. We consider the scenario where n jobs have a uniform upper limit on the …

Over the past few years, healthcare providers have significantly expanded telemedicine
adoptions. On one hand, telemedicine has the potential to increase the accessibility of …