Title:
Got (Optimal) Milk? Pooling Donations in Human Milk Banks with Machine Learning and Optimization
Timothy C. Y. Chan , Rafid Mahmood , Deborah L. O’Connor , Debbie Stone, Sharon Unger , Rachel K. Wong, Ian Yihang Zhu

Abstract:
Problem definition: Human donor milk provides critical nutrition for millions of infants who 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. For these milk banks, an approach is needed to optimize pooling decisions. Methodology/results: We propose a data-driven framework combining machine learning and optimization to predict macronutrient content of donations 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 year-long trial implementation, where we observe the current nurse-led pooling practices followed by our intervention. Pools created by our approach meet clinical macronutrient targets approximately 31% more often than the baseline, although taking 60% less recipe creation time. Managerial implications: 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 in a healthcare setting 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.

Bio:
Timothy Chan is the Associate Vice-President and Vice-Provost, Strategic Initiatives at 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. He was previously Director of the Centre for Healthcare Engineering, Director of the Centre for Analytics and AI Engineering, and Associate Director, Research and Thematic Programming, of the Data Sciences Institute. His primary research interests are in operations research, optimization, and applied machine learning, with applications in healthcare, medicine, sustainability, and sports.

Professor Chan 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.

Professor Chan currently holds editorial roles in seven academic journals, including Operations Research, Management Science, and M&SOM. He has served in a variety of leadership and service roles at INFORMS and CORS, including as President of the INFORMS Health Application Society. He has over 120 publications in refereed journals, and is co-author of an upcoming book entitled “Introduction to Markov Decision Processes”. He has graduated over 50 graduate students and postdoctoral fellows, and takes great pride in cultivating a healthy, inclusive, and productive lab environment.

Professor Chan has received numerous awards and honours for his research, teaching and service. Recent highlights include the President’s Teaching Award from the University of Toronto in 2024, 1st place in the research paper competition at the MIT Sloan Sports Analytics Conference in 2024, the INFORMS Prize for Teaching OR/MS Practice in 2023, the Pierskalla Best Paper Award from INFORMS Health Applications Society in 2023, 1st place in the INFORMS Case Competition in 2022, and the CORS Eldon Gunn Service Award in 2022. His research has been featured by the CBC, CTV News, Global News, Reuters, CNN, the Globe and Mail, the Toronto Star, Boston Globe, ESPN, Canadian Business Magazine, and World Economic Forum.