Data‐driven platelet inventory management under uncertainty in the remaining shelf life of units
针对血小板剩余保质期不确定的问题，利用医院信息系统数据，开发了两种库存订购模型（固定初始年龄模型和鲁棒模型），在加拿大两家医院的数据测试中显著降低了过期率和短缺率。
Platelets are perishable (5–7 day shelf life) blood products required for a variety of clinical treatments. In North America, hospitals typically procure platelet units from a central supplier. As such, the remaining shelf life of the delivered units could be subject to high uncertainty. Our work focuses on developing new models that leverage the increasingly available data from hospital information systems to prescribe ordering decisions in the presence of this uncertainty. Specifically, we consider a periodic review, perishable inventory system with zero lead time and uncertainty in demand and remaining shelf life of orders, operating under an oldest‐unit, first‐out allocation policy. We consider a family of base stock policies and adopt an empirical risk minimization approach to estimate the required inventory at the beginning of each period. The required inventory level for each period is assumed to be a linear function of a set of observed features in that period and the coefficients of the linear model are obtained by minimizing an approximate measure of the in‐sample empirical cost, comprised of a weighted sum of shortage and expiry costs. Our fixed initial age model assumes a constant remaining shelf life for all units. Our robust model assumes that an adversary selects the remaining shelf life of units subject to an uncertainty budget determined through an endogenous uncertainty set. We investigate the out‐of‐sample performance of the proposed models in a case study using data from two Canadian hospitals and in comparison to the hospitals' historical performances as well as other benchmarks. Both models achieve significant improvements over the historical decisions. For instance, the fixed initial age model achieves a 53% and 93% reduction in the expiry rate and an 82% and 99% reduction in the shortage rate for the two hospitals, respectively. Further, it either outperforms or performs as well as the other benchmarks. The robust model achieves better out‐of‐sample generalizability and demonstrates a more “robust” performance under counterfactual remaining age distributions.