'Interpretable Vaccine Administration and Inventory Replenishment Policies via Smooth-in-Expectation Decision Rules', a seminar by Dr Merve Bodur (Edinburgh)

Interpretable Vaccine Administration and Inventory Replenishment Policies via Smooth-in-Expectation Decision Rules

by Dr Merve Bodur 

Abstract

The effective management of vaccine administration and inventory is critical for achieving widespread immunization coverage, yet developing policies that are both optimal and interpretable remains a significant challenge. We address this by proposing a novel, flexible multistage stochastic programming model that handles realistic operational complexities like patient queueing and early clinic closures. To overcome the black-box nature of typical solutions, we employ smooth-in-expectation decision rules—a first-of-its-kind application, also extended to handle integer first-stage decisions—to generate interpretable policies as intuitive flowcharts. Our solution algorithm combines batch stochastic gradient descent with a rounding heuristic and is warm-started by our novel two-stage stochastic program, which also provides a strong lower bound and is orders of magnitude faster than comparable models. Numerical experiments demonstrate our flowchart policies are near-optimal, while our Pareto frontier analysis offers practical insights into policy trade-offs.

About the speaker

Merve Bodur is a Reader (Associate Professor) in the School of Mathematics at The University of Edinburgh. She obtained her Ph.D. from the University of Wisconsin-Madison, her B.S. in Industrial Engineering and her B.A. in Mathematics from Bogazici University, Turkey. Her main research area is optimization under uncertainty, primarily for discrete optimization problems, with applications in a variety of areas such as scheduling, transportation, healthcare, telecommunications, and power systems. She serves on the editorial boards of INFORMS Journal on Computing, Operations Research Letters, Omega, and INFOR. She is currently the Vice Chair/Chair-Elect of the INFORMS Computing Society, and serves on the Committee on Stochastic Programming and Mathematical Optimization Society Council.