A Classification Method for Ranking and Selection with Covariates
A Classification Method for Ranking and Selection with Covariates
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DOI：
10.1109/wsc57314.2022.10015235
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Gregory Keslin;B. Nelson;M. Plumlee;B. Pagnoncelli;Hamed Rahimian
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文献类型：
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作者：
Gregory Keslin;B. Nelson;M. Plumlee;B. Pagnoncelli;Hamed Rahimian
排序与选择（R&S）程序是一种模拟优化算法，用于在有限的一组备选系统设计或可行解决方案中做出一次性决策，并在统计上保证良好的选择。带有协变量的排序与选择（R&S+C）扩展了这一范式，允许最优选择取决于在需要做出决策之前刚刚获得的情境信息。解决此类问题的主要方法是利用离线模拟来创建元模型，该元模型根据协变量预测每个系统或可行解决方案的性能。本文介绍了一种完全不同的方法，该方法针对协变量的不同值离线解决单个R&S问题，然后将实时决策视为一个分类问题：给定协变量信息，哪个系统是一个好的解决方案？我们的方法利用了高效的R&S程序的可用性，相比元建模范式需要更宽松的假设来提供有力的保证，并且可能更高效。
Ranking & selection (R&S) procedures are simulation-optimization algorithms for making one-time decisions among a finite set of alternative system designs or feasible solutions with a statistical assurance of a good selection. R&S with covariates (R&S+C) extends the paradigm to allow the optimal selection to depend on contextual information that is obtained just prior to the need for a decision. The dominant approach for solving such problems is to employ offline simulation to create metamodels that predict the performance of each system or feasible solution as a function of the covariate. This paper introduces a fundamentally different approach that solves individual R&S problems offline for various values of the covariate, and then treats the real-time decision as a classification problem: given the covariate information, which system is a good solution? Our approach exploits the availability of efficient R&S procedures, requires milder assumptions than the metamodeling paradigm to provide strong guarantees, and can be more efficient.