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U-M Industrial & Operations Engineering pres.

IOE PhD Seminar Series - Karmel Shehadeh

Novel Stochastic Mixed-Integer Programming and Distributionally Robust Optimization Models and Frameworks for Outpatient Scheduling

Open to all IOE graduate students and faculty. Lunch will be provided. In order to get an accurate count for food, please RSVP by noon on Wednesday, December 12.

Outpatient clinics (OPC) are increasingly growing as a central component of health care systems. They offer a variety of medical services and benefits such as short hospital stays, high patient safety outcomes, and low costs of care. They also introduce new challenges for appointment planning and scheduling primarily due to the heterogeneity of and variability in patients characteristic, the existence of multiple competing performance criteria, and the need to deliver care within a tight time window. Ignoring the variability in patient characteristic when designing appointment schedules may have negative consequences such as patient delays and clinic overtime. Conversely, accounting for uncertainty in the scheduling decision process has the potential to create more efficient schedules that mitigate these adverse outcomes. However, many challenges arise when attempting to model and solve appointment scheduling problems accounting for uncertainty. In this talk, we present novel stochastic mixed-integer programming and distributionally robust optimization models and frameworks to optimize appointment planning and scheduling decisions under uncertainty in the context of three outpatient scheduling problems with broader applications within and outside of healthcare. In each of these three problems, we focus on efficiently accounting for uncertainty in the scheduling decision process and proposing tractable and implementable appointment scheduling models.

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