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

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Titel: Efficient simulation budget allocation for contextual ranking and selection with quadratic models.
Autoren: Li, Dongyang1 (AUTHOR) dongyang_li@u.nus.edu, Chew, Ek Peng1 (AUTHOR) isecep@nus.edu.sg, Li, Haobin1 (AUTHOR) li_haobin@nus.edu.sg, Yücesan, Enver1,2 (AUTHOR) enver.yucesan@insead.edu, Chen, Chun-Hung3 (AUTHOR) cchen9@gmu.edu
Quelle: European Journal of Operational Research. Feb2026, Vol. 328 Issue 3, p862-876. 15p.
Schlagwörter: CONTEXTUAL analysis, QUADRATIC equations, COMPUTER simulation, COMPUTER performance, ADAPTIVE sampling (Statistics), MATHEMATICAL functions, BUDGET management
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. • Identify the context-dependent optimal designs via offline simulation optimization. • Bayesian framework with quadratic models to infer unknown performance functions. • Myopic sampling procedure to actively learn the problem and improve decision quality. • Applications in personalized medical treatments and agricultural practices. [ABSTRACT FROM AUTHOR]
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Datenbank: Business Source Index
Beschreibung
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. • Identify the context-dependent optimal designs via offline simulation optimization. • Bayesian framework with quadratic models to infer unknown performance functions. • Myopic sampling procedure to actively learn the problem and improve decision quality. • Applications in personalized medical treatments and agricultural practices. [ABSTRACT FROM AUTHOR]
ISSN:03772217
DOI:10.1016/j.ejor.2025.08.042