Hybrid and distributionally robust optimization approaches for hospital blood bank inventory management under demand uncertainty
针对医院血库在需求不确定、血液易过期和供应有限下的订货与分配问题，提出了混合鲁棒优化和分布鲁棒优化两种模型，实验表明分布鲁棒优化模型总成本比传统随机优化低5%。
Blood product shortages and wastage costs remain persistent challenges for hospital blood banks (HBBs) due to uncertain demand, perishability, and limited supply. Existing approaches often fail to offer robust performance under distributional shifts or partial data availability and tend to yield overly conservative solutions. This research develops a stochastic optimization (SO) framework for blood product ordering and allocation decisions under uncertain urgent and elective demands. Two robust counterparts are proposed: (i) a hybrid robust optimization (HRO) model combining conditional value at risk (CVaR) and P-Robustness, and (ii) a distributionally robust optimization (DRO) model. Their performance is evaluated relative to each other and a conventional SO model. Numerical experiments show that the DRO model achieves the greatest cost savings, with total costs 5% lower than the SO model and 2% lower than the HRO model. The results provide managerial insights and demonstrate the effectiveness of distributional robustness in managing blood inventory under uncertainty.