Abstract: Decision-making in healthcare systems plays a crucial role in many contexts, from disease prevention, control, and treatment to hospital operations. Vaccines can prevent life-threatening diseases effectively. The increasing number of available vaccines and complex dosage regimens make recommending personalized childhood immunization schedules difficult. We develop a discrete optimization model to solve both de novo (for all children, starting at birth) and catch-up (for children who are behind on one or more vaccinations) scheduling problems, given the child’s age and immunization history. The model is the first in the literature to solve both types of scheduling problems and provide vaccine selection decisions to achieve a balanced combination of higher protection against diseases and fewer clinic visits.

In the context of hospital operations, for example, where physicians need to balance different responsibilities, we consider a two-stage service system with two types of servers, namely subordinates (e.g., residents) who perform the first-stage service and supervisors (e.g., attending physicians) who have their own responsibilities in addition to collaborating with the subordinates on the second-stage service. Rewards are earned when first- or second-stage service is completed and when supervisors finish one of their own responsibilities. Costs are incurred when impatient customers abandon without completing the second-stage service. We introduce a Markov decision process (MDP) formulation, prove that one of two policies will maximize the long-run average profit, and show that the optimality condition is a simple threshold on the system parameters.

Short Bio: April Yu is an assistant professor at the Department of Industrial & Systems Engineering, North Carolina State University. She received her Ph.D. and M.S. in Operations Research from the School of Industrial & Systems Engineering, Georgia Tech. Her research interests focus on stochastic modeling and optimization. Her recent works address a wide range of real-world problems, from healthcare systems to supply chain management.