A Multilevel Simulation Optimization Approach for Quantile Functions

Songhao Wang (), Szu Hui Ng () and William Benjamin Haskell ()
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Songhao Wang: Department of Information Systems and Management Engineering, Southern University of Science and Technology, Shenzhen 518000, China
Szu Hui Ng: Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore 117576
William Benjamin Haskell: Krannert School of Management, Purdue University, West Lafayette, Indiana 47906

INFORMS Journal on Computing, 2022, vol. 34, issue 1, 569-585

Abstract: A quantile is a popular performance measure for a stochastic system to evaluate its variability and risk. To reduce the risk, selecting the actions that minimize the tail quantiles of some loss distributions is typically of interest for decision makers. When the loss distribution is observed via simulations, evaluating and optimizing its quantile can be challenging, especially when the simulations are expensive as it may cost a large number of simulation runs to obtain accurate quantile estimators. In this work, we propose a multilevel metamodel (cokriging)-based algorithm to optimize quantiles more efficiently. Utilizing nondecreasing properties of quantiles, we first search on cheaper and informative lower quantiles, which are more accurate and easier to optimize. The quantile level iteratively increases to the objective level, and the search has a focus on the possible promising regions identified by the previous levels. This enables us to leverage the accurate information from the lower quantiles to find the optimums faster and improve algorithm efficiency.

Keywords: quantile optimization; cokriging model; simulation optimization (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:34:y:2022:i:1:p:569-585

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