Efficient simulation budget allocation for contextual ranking and selection with quadratic models
针对情境化排序与选择问题，假设各方案均值性能是连续情境空间的二次函数，提出一种贝叶斯预算分配方法，主动学习问题实例并逐步提升决策质量，数值实验表明在固定预算和固定精度下均优于基准算法。
This paper considers contextual ranking and selection problems where the objective is to identify the best design under every possible context. We assume the mean performance of each alternative design to be a quadratic function across a continuous context space. By judiciously pre-selecting a finite set of contexts for sampling and leveraging this quadratic model structure, we develop an efficient Bayesian budget allocation procedure that actively learns the problem instance and myopically improves decision quality across the context space. We prove the asymptotic consistency of our algorithm. We also conduct extensive numerical experiments using both synthetic functions and industrial examples whereby we show that our procedure can deliver significantly better performance against benchmark algorithms under both fixed-budget and fixed-precision settings.