Efficient simulation budget allocation for contextual ranking and selection with quadratic models

IF 6 2区 管理学 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Dongyang Li , Ek Peng Chew , Haobin Li , Enver Yücesan , Chun-Hung Chen
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引用次数: 0

Abstract

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.
基于二次模型的上下文排序和选择的高效仿真预算分配
本文考虑情境排序和选择问题,其目标是在每种可能的情境下识别最佳设计。我们假设每个备选设计的平均性能是一个跨越连续上下文空间的二次函数。通过明智地预先选择一组有限的上下文进行采样,并利用这种二次模型结构,我们开发了一个有效的贝叶斯预算分配过程,该过程主动学习问题实例,并在短期内提高了整个上下文空间的决策质量。证明了算法的渐近一致性。我们还使用合成函数和工业示例进行了广泛的数值实验,从而表明我们的程序在固定预算和固定精度设置下都可以提供比基准算法更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
European Journal of Operational Research
European Journal of Operational Research 管理科学-运筹学与管理科学
CiteScore
11.90
自引率
9.40%
发文量
786
审稿时长
8.2 months
期刊介绍: The European Journal of Operational Research (EJOR) publishes high quality, original papers that contribute to the methodology of operational research (OR) and to the practice of decision making.
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