Does your model reflect the needs of the user? Meaningful Data-driven Multi-criteria Decision Modeling: Elicitation of Preference among Multiple Criteria in Food Distribution by Food Banks


Decision making to satisfy the basic human needs of health, food, and education is complex. In 2020, more than 38 million people, including 12 million children, in the U.S. were food insecure. By 2021, 53 million people sought help from food banks and community programs to feed their families. Food banks are nonprofit organizations that collect and distribute food donations to food-insecure populations in their service regions. Food banks are challenged with juggling multiple criteria such as equity, effectiveness, and efficiency when making distribution decisions. Models that assume predetermined weights on multiple criteria may produce inaccurate results as the preference of food banks over these criteria may vary over time, and as a function of supply and demand. In collaboration with our food bank partner in North Carolina, we develop a single-period, weighted multi-criteria optimization model that provides the decision-maker the flexibility to capture their preferences over the three criteria of equity, effectiveness, and efficiency, and explore the resulting trade-offs. We introduce a novel algorithm to elicit the inherent preference of a food bank by analyzing its actions within a single-period. The algorithm does not require direct interaction with the decision-maker. The non-interactive nature of this algorithm is especially significant for humanitarian organizations such as food banks which lack the resources to interact with modelers on a regular basis. We explore the implications of different decision-maker preferences for the criteria on distribution policies.


Julie Simmons Ivy, Ph.D., is a Professor and Fitts Faculty Fellow of Health Systems Engineering in the Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University with extensive background in decision making under conditions of uncertainty using stochastic and statistical modeling. She received her B.S. and Ph.D. in Industrial and Operations Engineering from the University of Michigan. She also received her M.S. in Industrial and Systems Engineering from Georgia Tech. She is an active member of the Institute of Operations Research and Management Science (INFORMS), Dr. Ivy served as the 2007 Chair (President) of the INFORMS Health Applications Society and the 2012 – 13 President for the INFORMS Minority Issues Forum. Recently, Dr. Ivy was elected as a 2022 INFORMS Fellow. Dr. Ivy’s research seeks to model complex interactions and quantitatively capture the impact of different factors, objectives, system dynamics, intervention options and policies on outcomes with the goal of improving decision quality. In particular, Dr. Ivy has extensive background in the application of systems science methods, including the analysis and modeling of large data sets, to hunger relief and health decision making. This research has made an impact on how researchers and practitioners address complex societal issues, such as health disparities, public health preparedness, hunger relief, student performance, and personalized medical decision-making and has been funded by the CDC, NSF, and NIH.