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 () and Ian Yihang Zhu ()
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Timothy C. Y. Chan: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada
Rafid Mahmood: Telfer School of Management, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
Deborah L. O’Connor: Department of Nutritional Sciences, University of Toronto, Toronto, Ontario M5S 1A8, Canada
Debbie Stone: Rogers Hixon Ontario Human Milk Bank, Sinai Health, Toronto, Ontario M5G 1X5, Canada
Sharon Unger: Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
Rachel K. Wong: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada
Ian Yihang Zhu: NUS Business School, National University of Singapore, Singapore 119245

Manufacturing & Service Operations Management, 2025, vol. 27, issue 6, 1721-1739

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.

Keywords: healthcare operations management; machine learning; optimization; simulation; milk banks; neonatal care; trial implementation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:27:y:2025:i:6:p:1721-1739

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