EconPapers    
Economics at your fingertips  
 

Simulation cloning with induced negative correlation

Judy S Lee, Sigrún Andradóttir and Richard M Fujimoto

Journal of Simulation, 2017, vol. 11, issue 4, 391-406

Abstract: Simulation cloning involves expediting a simulation by sharing computational results among different sample paths. It resembles the idea of splitting, which is widely researched in rare event simulation, as splitting techniques also produce clones when a sample path reaches a certain state. In this paper, we consider the use of simulation cloning for estimation of performance measures via transient simulation. In addition to the basic cloning approach, we present simulation cloning algorithms with induced negative correlation to reduce variance. We also consider the possibility of cloning the simulation at multiple (two) decision points. Numerical experiments are provided to illustrate the effectiveness of our algorithms. In real-life situations where simulating one replication is expensive and variability is larger in the replicated part of the simulation, simulation cloning can significantly improve the efficiency of the simulation.

Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1057/s41273-016-0028-7 (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:tjsmxx:v:11:y:2017:i:4:p:391-406

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjsm20

DOI: 10.1057/s41273-016-0028-7

Access Statistics for this article

Journal of Simulation is currently edited by Christine Currie

More articles in Journal of Simulation from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:tjsmxx:v:11:y:2017:i:4:p:391-406