Date of Award

3-2020

Document Type

Thesis

Degree Name

Master of Science in Operations Research

Department

Department of Operational Sciences

First Advisor

Mark A. Gallagher, PhD

Abstract

This research compares simulations to Dynamic Bayesian Networks in analyzing situations. The research applies models that have known output mean and variance. Queueing systems have theoretical values of the steady-state mean and variance for the number of entities in the system. Monte Carlo simulation development is broken down into two separate approaches: discrete-event simulation and time-oriented simulation. The discrete-event simulation uses pseudo-random numbers to schedule and trigger future events (i.e. customer arrivals and services) and is based on the generated objects.The time-oriented simulation utilizes fixed-width time intervals and updates the system state according to a stochastic process for the set of events occurring during each time period. The accuracy of each approach in estimated by a comparison to the theoretical mean, variance, and probability values.

AFIT Designator

AFIT-ENS-MS-20-M-168

DTIC Accession Number

AD1102506

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