Food donation management under supply and demand uncertainties in COVID-19: A robust optimization approach

COVID-19 pandemic and the associated lockdown have globally impacted the food-insecure low-income population. A community-based initiative of an NPO - working on malnutrition issues among the below poverty line children in an urban Indian setting - towards adoption of the slum- and shelter-dwelling children, faced challenges during the pandemic in tackling the twofold uncertainties regarding supply capacity reduction and demand surge. This paper extends earlier research on this NPO's pre-COVID-time food assistance program by introducing a robust optimization-based mathematical programming model to determine the donors to engage, donor-to-beneficiary allocations, and the associated optimal donation flows while addressing these uncertainties. The issue of over-conservatism in robust optimization is addressed by parameterizing the decision-maker's uncertainty budget. We present a detailed numerical study on a test problem along with interesting observations elicited by our sensitivity analyses.