Targeting, Deployment, and Loss-Tolerance in Lanchester Engagements
Michael P. Atkinson (),
Moshe Kress () and
Niall J. MacKay ()
Additional contact information
Michael P. Atkinson: Operations Research Department, Naval Postgraduate School, Monterey, California 93943
Moshe Kress: Operations Research Department, Naval Postgraduate School, Monterey, California 93943
Niall J. MacKay: Department of Mathematics, University of York, York YO10 5DD, United Kingdom
Operations Research, 2021, vol. 69, issue 1, 71-81
Abstract:
Existing Lanchester combat models focus on two force parameters: numbers (force size) and per-capita effectiveness (attrition rate). Whereas these two parameters are central in projecting a battle’s outcome, there are other important factors that affect the battlefield: (1) targeting capability, that is, the capacity to identify live enemy units and not dissipate fire on nontargets; (2) tactical restrictions preventing full deployment of forces; and (3) morale and tolerance of losses, that is, the capacity to endure casualties. In the spirit of Lanchester theory, we derive, for the first time, force-parity equations for various combinations of these effects and obtain general implications and trade-offs. We show that more units and better weapons (higher attrition rate) are preferred over improved targeting capability and relaxed deployment restrictions unless these are poor. However, when facing aimed fire and unable to deploy more than half of one’s force, it is better to be able to deploy more existing units than to have either additional reserve units or the same increase in attrition effectiveness. Likewise, more relaxed deployment constraints are preferred over enhanced loss-tolerance when initial reserves are greater than the force level at which withdrawal occurs.
Keywords: combat modeling; Lanchester equations (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
https://doi.org/10.1287/opre.2020.2022 (application/pdf)
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:inm:oropre:v:69:y:2021:i:1:p:71-81
Access Statistics for this article
More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().