An efficient approximate solution for stochastic Lanchester models

Donghyun Kim, Hyungil Moon, Donghyun Park and Hayong Shin ()
Additional contact information
Donghyun Kim: KAIST
Hyungil Moon: KAIST
Donghyun Park: KAIST
Hayong Shin: KAIST

Journal of the Operational Research Society, 2017, vol. 68, issue 11, 1470-1481

Abstract: Abstract Combat modeling is one of the essential topics for military decision making. The Lanchester equation is a classic method for modeling warfare, and many variations have extended its limitations and relaxed its assumptions. As a model becomes more complex, solving it analytically becomes intractable or computationally expensive. Hence, we propose two approximation methods: moment-matching scheme and a supporting method called battle-end approximation. These methods give an approximate solution in a short amount of time, while maintaining a high level of accuracy in simulation results in terms of hypothesis testing and numerical verification. They can be applied to computationally intensive problems, such as optimal resource allocation and analysis with asymmetric power like snipers or stealth aircrafts.

Keywords: Military; stochastic Lanchester model; Gaussian approximation (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1057/s41274-016-0163-6 Abstract (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:pal:jorsoc:v:68:y:2017:i:11:d:10.1057_s41274-016-0163-6

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/41274

DOI: 10.1057/s41274-016-0163-6

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald and Jonathan Crook

More articles in Journal of the Operational Research Society from Palgrave Macmillan, The OR Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().