Team Members: Eva Breiland, Sophia Kim, and Audrey Wijono 

Department: Applied Mathematics and Statistics 

Our project uses multi-objective optimization to explore how food systems can be designed to balance affordability, nutrition, and environmental sustainability across different country contexts. Using datasets from the United States and Argentina, we built a model that generates optimal daily food bundles subject to nutritional constraints while minimizing both cost and greenhouse gas emissions. By analyzing Pareto-optimal solutions, we demonstrate that while low-cost diets can meet nutritional requirements, achieving sustainability often introduces tradeoffs that vary by country due to differences in food prices, availability, and production systems. 

Our findings reveal that cost-efficient diets tend to rely on a small set of staple foods, while environmentally conscious solutions shift consumption toward lower-emission ingredients such as plant-based proteins. Through this work, we highlight the importance of integrating quantitative modeling into food policy and decision-making. More broadly, our project illustrates how mathematical optimization can be used to address complex, real-world challenges at the intersection of public health, sustainability, and economic access.