Optimal Management of Donor Milk Banks

母乳是一种充满活力的生物活性液体，它为婴儿，特别是早产儿提供了至关重要的营养和医疗优势。母乳含有数百种抗体和其他因素，可以保护婴儿免受危及生命的新生儿感染和疾病的伤害。然而，许多母亲不能为其婴儿提供足够的母乳，原因有很多，包括没有或哺乳不足，后者对早产儿的母亲来说尤其严重。因此，通过非营利性母乳银行收集和分发捐赠的母乳，是那些母亲自己的母乳不是可行选择的婴儿的护理标准。尽管美国每年分配的黄金超过200万盎司，但供不应求。此外，母乳银行涉及许多复杂的操作流程。因此，这一不断扩大的设施网络被广泛认为非常需要支持。该奖项旨在通过优化这一宝贵稀缺资源的加工、储存、分配和优先分配来提供此类支持。这项研究的结果将通过更好的医疗结果、更短的住院时间和挽救生命来造福社会。该研究项目的具体目标是优化来自不同捐赠者的母乳如何混合在一起，如何履行医院订单和门诊处方，以及如何对接受者进行优先排序。这些决策过程将被制定为大规模的多阶段随机方案，并将使用来自多个来源的数据进行校准。混合决策将被建模为一个多阶段随机混合整数规划，这是一类名副其实的问题，很难解决，但呈现出吸引人的结构。患者优先排序决策将被建模为多阶段随机凸规划，这些决策过程的雄心勃勃的组合将被建模为多阶段随机混合整数凸规划。总体而言，这项工作的主要学术价值在于应用程序(即母乳银行操作)的及时性和新颖性，以及在求解模型时将克服的建模和算法挑战。

Human breast milk is a dynamic, bioactive fluid that provides crucial nutritional and medicinal advantages to infants, especially those born prematurely. Breast milk contains hundreds of antibodies and other factors that protect babies from life-threatening neonatal infections and diseases. Many mothers, however, cannot provide adequate breast milk for their infants for a host of reasons including absent or insufficient lactation, the latter of which is especially acute for mothers of premature infants. Therefore, donated breast milk, collected and dispensed via non-profit milk banks, is the standard of care for infants whose mother's own milk is not a viable option. Despite the fact that over two million ounces are dispensed annually in the US, demand far outweighs supply. Moreover, milk banks involve many complex operational processes. Hence, this growing network of facilities is widely regarded as being in great need of support. This award aims to provide such support by optimizing the processing, storage, dispensing and prioritized allocation of this precious scarce resource. The outcomes of the research will benefit society through better medical outcomes, shorter hospital stays and saved lives. The specific objectives of this research project are to optimize how milk from different donors is blended together, how hospital orders and out-patient prescriptions are fulfilled and how recipients are prioritized. These decision-making processes will be formulated as large-scale multistage stochastic programs and will be calibrated using data from multiple sources. The blending decisions will be modeled as a multistage stochastic mixed-integer program, a class of problems that has a well-deserved reputation for being very difficult to solve, but exhibit appealing structure. Patient prioritization decisions will be modeled as a multistage stochastic convex program and an ambitious combination of these decision processes will be modeled as a multistage stochastic mixed-integer convex program. Overall, the main intellectual merits of the work lie in the timeliness and novelty of the application (i.e., milk bank operations) as well as the modeling and algorithmic challenges that will be overcome in solving the models.