Plenary Speakers

Got (Optimal) Milk?

Timothy Chan

Professor
University of Toronto

Human donor milk is considered the ideal nutrition for millions of infants that are born preterm each year. Donor milk is collected, processed, and distributed by milk banks. The macronutrient content of donor milk is directly linked to infant brain development and can vary substantially across donations, which is why multiple donations are typically pooled together to create a final product. Approximately half of all milk banks in North America do not have the resources to measure the macronutrient content of donor milk, which means pooling is done heuristically. We propose a data-driven framework combining machine learning and optimization to predict macronutrient content of deposits and then optimally combine them in pools, respectively. In collaboration with our partner milk bank, we collect a data set of milk to train our predictive models. We rigorously simulate milk bank practices to fine-tune our optimization models and evaluate operational scenarios such as changes in donation habits during the COVID-19 pandemic. Finally, we conduct a yearlong trial implementation, where we observe the current nurse-led pooling practices followed by our intervention. Pools created by our approach meet clinical macronutrient targets between 31% and 76% more often than the baseline, while taking 67% less recipe creation time. This is the first paper in the broader blending literature that combines machine learning and optimization. We demonstrate that such pipelines are feasible to implement and can yield significant improvements over current practices. Our insights can guide practitioners in any application area seeking to implement machine learning and optimization-based decision support.

About Timothy Chan

Timothy Chan is the Associate Vice President and Vice Provost, Strategic Initiatives of the University of Toronto, the Canada Research Chair in Novel Optimization and Analytics in Health, a professor in the department of Mechanical and Industrial Engineering, and a Senior Fellow of Massey College. His primary research interests are in operations research, optimization, and applied machine learning, with applications in healthcare, medicine, sustainability, and sports. He received his B.Sc. in applied mathematics from the University of British Columbia (2002), and his Ph.D. in operations research from the Massachusetts Institute of Technology (2007). Before coming to Toronto, he was an associate in the Chicago office of McKinsey and Company (2007-2009), a global management consulting firm. During that time, he advised leading companies in the fields of medical device technology, travel and hospitality, telecommunications, and energy on issues of strategy, organization, technology and operations.