Abstract

The Weapon Combat Effectiveness (WCE) analytics is very expensive, time-consuming, and dangerous in the real world because we have to create data from the real operations with a lot of people and weapons in the actual environment. The Modeling and Simulation (M&S) of many techniques is used for overcoming these limitations. Although the era of big data has emerged and achieved a great deal of success in a variety of fields, most WCE research using the Defense Modeling and Simulation (DM&S) techniques were studied without the help of big data technologies and techniques. The existing research has not considered various factors affecting WCE. This is because current research has been restricted by only using constructive simulation, a single weapon system, and limited scenarios. Therefore, the WCE analytics using existing methodologies have also incorporated the same limitations, and therefore, cannot help but get biased results. To solve the above problem, this dissertation is to initially review and compose the basic knowledge for the new WCE analytics methodology using big data and DM&S to further serve as the stepping-stone of the future research for the interested researchers. Also, this dissertation presents the new methodology on WCE analytics using big data generated by Live, Virtual, or/and Constructive (LVC) simulations. This methodology can increase the fidelity of WCE analytics results by considering various factors. It can give opportunities for application of weapon acquisition, operations analytics and plan, and objective level development on each training factor for the weapon operators according to the selection of Measures of Effectiveness (MOEs) and Measures of Performance (MOPs), or impact factors, based on the analytics goal.

Notes

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Graduation Date

2018

Semester

Spring

Advisor

Lee, Gene H.

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Industrial Engineering and Management Systems

Degree Program

Industrial Engineering

Format

application/pdf

Identifier

CFE0007025

URL

http://purl.fcla.edu/fcla/etd/CFE0007025

Language

English

Release Date

May 2023

Length of Campus-only Access

5 years

Access Status

Doctoral Dissertation (Open Access)

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