Food banks across the globe share a common goal of alleviating hunger and, in a broader scope, making the world a more humanitarian and equitable place for all. They also face a common challenge: balancing their ability to serve society and the environment while maintaining the economic stability required to perform their intended purpose. This capstone investigates the food banking sector and common tactical challenges, using South Africa's largest food banking network, FoodForward SA (FFSA), as the primary case study and sponsoring organization to apply real-world data and context to the proposed supply chain solutions. The main research goal is to optimize FoodForward SA’s supply chain network with models that separately address cost, emissions, and social impact, then evaluate tradeoffs across those criteria, deriving insights that may be applicable beyond FFSA. Data from FFSA and Stellenbosch University informed criteria aligned with three objective functions: economic (minimizing transport and handling costs), environmental (reducing emissions and food waste), and social (improving equitable access using multidimensional poverty indices). Using three optimization models built with Python and Gurobi to optimize each objective function separately, we analyzed FFSA's distribution network to identify tradeoffs across these solutions across the models. The proposed methodology supports decision-making by evaluating network performance under baseline and scenario analyses. Scenarios include opening and closing warehouses, adjusting demand among beneficiary organization clusters, and changing lower bounds in the social model to assess cost, equity, and efficiency impacts. Results of modeling showed that there are clear tradeoffs for food banks in prioritizing each objective. While opportunity for improvement in each measure is apparent, optimizing for one criterion will affect the performance measures of the other two objectives within each model. Each food bank is responsible for assessing its needs, goals, capabilities, and risk tolerance when toggling its network and service strategies. Still, leaders in the food banking industry may use this approach as a practical framework and applied methodology. By inputting their own food bank data, organizations can evaluate operational performance, assess tradeoffs, and generate insights to drive informed business decisions.
