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Data-driven resource allocation for multi-target attainment
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AI
DOI：10.1016/j.ejor.2024.05.045.png)
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摘要
En 中文
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.
Keyword:
Decision analysis
Target attainment
Resource allocation
Large deviations
Bandit problems