Optimal budget allocation for stochastic simulation with importance sampling: Exploration vs. replication

Young Myoung Ko and Eunshin Byon

IISE Transactions, 2022, vol. 54, issue 9, 881-893

Abstract: This article investigates a budget allocation problem for optimally running stochastic simulation models with importance sampling in computer experiments. In particular, we consider a two-level (or nested) simulation to estimate the expectation of the simulation output, where the first-level draws random input samples and the second-level obtains the output given the input from the first-level. The two-level simulation faces the trade-off in allocating the computational budgets: exploring more inputs (exploration) or exploiting the stochastic response surface at a sampled point in more detail (replication). We study an appropriate computational budget allocation strategy that strikes a balance between exploration and replication to minimize the variance of the estimator when importance sampling is employed at the first-level simulation. Our analysis suggests that exploration can be beneficial than replication in many practical situations. We also conduct numerical experiments in a wide range of settings and wind turbine case study to investigate the trade-off.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/24725854.2021.1953197 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:uiiexx:v:54:y:2022:i:9:p:881-893

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uiie20

DOI: 10.1080/24725854.2021.1953197

Access Statistics for this article

IISE Transactions is currently edited by Jianjun Shi

More articles in IISE Transactions from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().