Publication:
A Data Farming Analysis of a Simulation of Armstrong’s Stochastic Salvo Model

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Authors
Kesler, Gökhan
Lucas, Thomas W.
Sanchez, Paul J.
Subjects
Advisors
Date of Issue
2019
Date
2019
Publisher
IEEE
Language
en_US
Abstract
In 1995, Retired Navy Captain Wayne Hughes formulated a salvo model for assessing the military worth of warship capabilities in the missile age. Hughes’ model is deterministic, and therefore provides no information about the distribution of outcomes that result from inherently stochastic salvo exchanges. To address this, Michael Armstrong created a stochastic salvo model by transforming some of Hughes’ fixed inputs into random variables. Using approximations, Armstrong provided closed-form solutions that obtain probabilistic outcomes. This paper investigates Armstrong’s stochastic salvo model using data farming. By using a sophisticated design of experiments to run a simulation at thousands of carefully selected input combinations, responses such as ship losses are formulated as readily interpretable regression and partition tree metamodels of the inputs. The speed at which the simulation runs suggests that analysts should directly use the simulation rather than resorting to approximate closed-form solutions.
Type
Article
Description
Proceedings of the 2019 Winter Simulation Conference
Series/Report No
Department
Operations Research (OR)
Organization
Naval Postgraduate School
Identifiers
NPS Report Number
Sponsors
Funder
Format
12 p.
Citation
Kesler, Gökhan, Thomas W. Lucas, and Paul J. Sanchez. "A Data Farming Analysis of A Simulation of Armstrong’s Stochastic Salvo Model." 2019 Winter Simulation Conference (WSC). IEEE, 2019.
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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