Introduction to Discrete Event Simulation and Agent-based Modeling: Voting Systems, Health Care, Military, and Manufacturing

229 Pages·2011·3.53 MB·English

by  Theodore T. Allen (auth.)

Introduction to Discrete Event Simulation and
Agent-based Modeling
Theodore T. Allen, PhD
Introduction to Discrete
Event Simulation and
Agent-based Modeling
Voting Systems, Health Care, Military,
and Manufacturing
123
Dr. TheodoreT. Allen,PhD
Integrated Systems Engineering
The OhioStateUniversity
210BakerSystems
1971NeilAvenue
Columbus, OH43210-1271
USA
e-mail: [email protected]
ISBN 978-0-85729-138-7
DOI 10.1007/978-0-85729-139-4
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Foreword
The main purpose of this text is to provide an up-to-date foundation for applying
discreteeventsimulationandagent-basedmodeling.Itisperhapstruethatnoother
book covers as many topics of interest for providing real-world decision-support
including:
1. Open source simulation programming including Visual Basic (VB) and Net-
Logo which provide inexpensive options for businesses,
2. Agent-based modeling,
3. Variance-reduction techniques such as Latin-Hypercube sampling,
4. Process improvement opportunity identification using the theory of con-
straints, lean production, and other contemporary methods,
5. Output analysis including selection and ranking and design of experiments,
6. Black box simulation and multi-fidelity optimization,
7. Quasi-Monte Carlo,
8. Subjectivityincluding the nature of probabilities and empirical distributions,
9. Input analysis including samples sizes and distributions fitting, and
10. Introduction to well-known software packages (ARENA and SIMIO).
Asaresult,thisbookisarguablymoreup-to-datethanalternativetextsforboth
research and practice. Also included are 100+ solved examples or problems.
Thismaterialhasreceivedgoodratingswhenusedinmyintroductorycoursein
Integrated Systems Engineering at The Ohio State University. That course was
(effectively)asinglesemesterlongandexcludedtheadvancedcontentinChaps.8,
9, and 12 (variation reduction, VB, and agent-based modeling). It is believed that
those chapters make the book relevant to both introductory undergraduate and
graduateclasses.WhilestudentsatOhioStatehavetakenintroductoryprobability
theory prior totaking simulation, the intent is that prerequisites are notneeded.
v

Acknowledgments
I would like to thank my wife Emily Patterson and my sons, Andrew and Henry,
forsupport.Andrewhelpedtogatherdataforthesupermarketproject.Iwouldlike
tothankmyparents,GeorgeandJodie,for additionalsupport.Inparticular,Jodie
edited the entire document. Austin Mount-Campbell provided content for the
ARENA-related work and editorial assistance.
MuerYangwrotemuchoftheVBcodeandplayedthekeyroleinestablishing
rigorous results related to election machine allocation. Douglas Samuelson con-
tributed significantly to Chap. 12. Mikhail (Mike) Bernshteyn is my partner and
respected colleague. Mike’s coding and modeling have driven all of our elections
related consulting work. David Sturrock helped to build and document a 3D-
SIMIO model of the gourmet checkout aisles.
Gordon Clark and Allen Miller have both provided much needed support and
encouragement. Also, I would like to thank Shane Henderson for ideas about
election machine allocation and Mike Fry leading much of our research.
Fritz Scheuren and Steven Hertzberg provided the inspirational leadership that
opened the world of election systems to Mike and me. Matthew Damschroder
providedconsistentlythoughtfulleadershipforourFranklinCounty,Ohioprojects
with further leadership from the director, Michael Stinziano. Karen Cotton also
provided key support. Ohio Secretary of State Jennifer Brunner and Antoinette
Wilson continue to inspire us through their constant search for opportunities to
take decisive, data-driven actions to aid Ohioans.
Tim Leopold and John Gillard supplied relevant information about simulation
in practice including about embedded excel files. Michael Garrambone presented
useful information about the uses of simulation and J. O. Miller clarified several
issues about aggregation. The IIE Student Simulation Competition and Carley
Jurishica and others from Rockwell International contributed. Edward Williams
told me about modified Turing tests. Finally, the undergraduate students at The
Ohio State University including especially Jonathan Carmona inspired the work
and provided helpful feedback while Cathy Xia, Khalil-Bamoradia Kabiri, David
Woods, and Suvrajeet Sen contributed many useful ideas.
vii

Contents
1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Domains and Uses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Questions about Voting Systems. . . . . . . . . . . . . . . . . . . . . 3
1.3 Simulation Phases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Phase 1: Define the System and Team Charter. . . . . . . . . . . 5
1.5 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Probability Theory and Monte Carlo . . . . . . . . . . . . . . . . . . . . . 9
2.1 Random Variables and Expected Values . . . . . . . . . . . . . . . 9
2.2 Confidence Intervals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.1 Confidence Intervals Construction Method. . . . . . . . 12
2.3 Expected Value Formula and Leaps of Faith . . . . . . . . . . . . 15
2.4 Discrete Event Simulation . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.4.1 Linear Congruential Generators . . . . . . . . . . . . . . . 18
2.4.2 Inverse Cumulative Distribution Functions. . . . . . . . 19
2.4.3 Discrete Event Simulation . . . . . . . . . . . . . . . . . . . 21
2.5 Monte Carlo Errors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.6 Monte Carlo Simulation Example. . . . . . . . . . . . . . . . . . . . 24
2.6.1 Problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.6.2 Solution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.7 Voting Systems Example Summary. . . . . . . . . . . . . . . . . . . 25
2.8 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3 Input Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.1 Guidelines for Gathering Data . . . . . . . . . . . . . . . . . . . . . . 29
3.2 Relative Frequency Histograms and SSE. . . . . . . . . . . . . . . 32
3.2.1 Relative Frequency Histograms. . . . . . . . . . . . . . . . 32
3.2.2 Sum of Squares Error . . . . . . . . . . . . . . . . . . . . . . 33
3.3 The Kolmogorov–Smirnov Test . . . . . . . . . . . . . . . . . . . . . 35
3.3.1 Constructing Cumulative Empirical Distributions . . . 35
3.3.2 The Kolmogorov–Smirnov Test . . . . . . . . . . . . . . . 36
ix