We delve into a class of multi-target attainment problems, which commonly arise in practical applications such as operations management, marketing, policy making, and healthcare services. The aim is to efficiently allocate a fixed amount of resources to achieve predetermined target payoffs for multiple tasks. We transform this stochastic problem into a tractable optimization problem that, when optimized, approximately maximizes the probability of attaining all the targets as data accumulates. This transformation is leveraged to devise a batch-based resource allocation rule that demonstrates strong theoretical and numerical performance guarantees.

中文翻译：

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数据驱动的资源分配以实现多目标


我们深入研究了一类多目标实现问题，这些问题通常出现在运营管理、营销、政策制定和医疗保健服务等实际应用中。目的是有效地分配固定数量的资源，以实现多项任务的预定目标回报。我们将这个随机问题转化为一个易于处理的优化问题，优化后，随着数据的积累，近似最大化实现所有目标的概率。利用这种转换来设计基于批次的资源分配规则，该规则展示了强大的理论和数值性能保证。