Robust Simulation with Likelihood-Ratio Constrained Input Uncertainty

Zhaolin Hu () and L. Jeff Hong ()
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Zhaolin Hu: School of Economics and Management, Tongji University, Shanghai 200092, China
L. Jeff Hong: School of Management and School of Data Science, Fudan University, Shanghai 200433, China

INFORMS Journal on Computing, 2022, vol. 34, issue 4, 2350-2367

Abstract: To use simulation models to study the behaviors of stochastic systems, one needs to specify the distribution of the input random variables. However, specifying this distribution precisely is typically difficult and even impossible in practice. The issue is known as input uncertainty in the simulation literature, and it has been considered and studied extensively in recent years. In this paper, we model the uncertainty by an ambiguity set that is defined based on the likelihood ratio between the true (unknown) distribution and the nominal distribution (i.e., the best estimate), and develop a robust simulation (RS) approach that estimates the worst-case values of performance measures of the random simulation output when the true distribution varies in the ambiguity set. We show that the RS approach is computationally tractable, and the corresponding results reveal important information of the stochastic systems and help decision makers make better decisions.

Keywords: simulation; statistical analysis; programming: stochastic; probability: distributions (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:34:y:2022:i:4:p:2350-2367

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