Minimizing food waste and costs in hospital environments through a chance-constrained stochastic programming model

Hospitals discard 30% of prepared meals, causing financial losses and contributing 5–10% of the facility's CO₂ emissions. This environmental threat is concerning, given that 20–50% of patients are malnourished upon admission, and inadequate nutrition during hospitalization increases the risk of complications. Despite growing sustainability imperatives, the lack of integrated approaches to reduce hospital food waste that consider patient-specific needs and demand uncertainty remains a challenge. This study proposes a chance-constrained two-stage stochastic programming model to minimize food waste costs, last-minute purchases, and storage costs, under uncertain demand. Validated through a case study in Florida, the model reduces food storage costs by 60%, decreases annual food waste by 2 tons, and lowers the climate impact by 15% in the most probable scenario. The study also reveals a nonlinear relationship in which costs increase faster than the incremental benefits of meeting nutritional requirements. This work supports hospital sustainability by minimizing waste and improving patient care.