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El. knyga: Applied Risk Analysis for Guiding Homeland Security Policy and Decisions [Wiley Online]

Edited by (Northeastern University, Boston, MA), Edited by (Washington State University), Edited by (PNNL)
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Presents various challenges faced by security policy makers and risk analysts, and mathematical approaches that inform homeland security policy development and decision support

Compiled by a group of highly qualified editors, this book provides a clear connection between risk science and homeland security policy making and includes top-notch contributions that uniquely highlight the role of risk analysis for informing homeland security policy decisions. Featuring discussions on various challenges faced in homeland security risk analysis, the book seamlessly divides the subject of risk analysis for homeland security into manageable chapters, which are organized by the concept of risk-informed decisions, methodology for applying risk analysis, and relevant examples and case studies. 

Applied Risk Analysis for Guiding Homeland Security Policy and Decisions offers an enlightening overview of risk analysis methods for homeland security. For instance, it presents readers with an exploration of radiological and nuclear risk assessment, along with analysis of uncertainties in radiological and nuclear pathways. It covers the advances in risk analysis for border security, as well as for cyber security. Other topics covered include: strengthening points of entry; systems modeling for rapid containment and casualty mitigation; and disaster preparedness and critical infrastructure resilience.





Highlights how risk analysis helps in the decision-making process for homeland security policy Presents specific examples that detail how various risk analysis methods provide decision support for homeland security policy makers and risk analysts Describes numerous case studies from academic, government, and industrial perspectives that apply risk analysis methods for addressing challenges within the U.S. Department of Homeland Security (DHS) Offers detailed information regarding each of the five DHS missions: prevent terrorism and enhance security; secure and manage our borders; enforce and administer our immigration laws; safeguard and secure cyberspace; and strengthen national preparedness and resilience Discusses the various approaches and challenges faced in homeland risk analysis and identifies improvements and methodological advances that influenced DHS to adopt an increasingly risk-informed basis for decision-making Written by top educators and professionals who clearly illustrate the link between risk science and homeland security policy making 

Applied Risk Analysis for Guiding Homeland Security Policy and Decisions is an excellent textbook and/or supplement for upper-undergraduate and graduate-level courses related to homeland security risk analysis. It will also be an extremely beneficial resource and reference for homeland security policy analysts, risk analysts, and policymakers from private and public sectors, as well as researchers, academics, and practitioners who utilize security risk analysis methods.
About the Editors xix
List of Contributors
xxi
Preface xxv
Chapter Abstracts xxviii
Part I Managing National Security Risk and Policy Programs
1(2)
1 On the "Influence of Scenarios to Priorities" in Risk and Security Programs
3(1)
Heimir Thorisson
James H. Lambert
1.1 Introduction
3(1)
1.2 Risk Programs
4(2)
1.3 Canonical Questions Guiding Development of Risk Programs
6(2)
1.3.1 Canonical Question I: Scope
6(1)
1.3.2 Canonical Question II: Operational Design
7(1)
1.3.3 Canonical Question III: Evaluation
7(1)
1.4 Scenario-Based Preferences
8(1)
1.5 Methodology
9(3)
1.6 Demonstration of Methods
12(8)
1.7 Discussion and Conclusions
20(5)
Acknowledgments
22(1)
References
22(3)
2 Survey of Risk Analytic Guidelines Across the Government
25(44)
Isaac Maya
Amelia Liu
Lily Zhu
Francine Tran
Robert Creighton
Charles Woo
2.1 Department of Defense (DOD) Overview
25(8)
2.1.1 Joint Risk Analysis Methodology (JRAM) for the Chairman's Risk Assessment (CRA)
26(3)
2.1.2 Mission Assurance (MA): Risk Assessment and Management for DOD Missions
29(2)
2.1.3 Risk Management Guide for DOD Acquisition
31(2)
2.2 Department of Justice (DOJ)
33(3)
2.3 Environmental Protection Agency (EPA) Overview
36(7)
2.3.1 EPA Risk Leadership
36(1)
2.3.2 EPA Risk Assessment Methodology and Guidelines
37(3)
2.3.3 Risk Assessment Case Studies
40(3)
2.3 A Risk Assessment Challenges of EPA
43(1)
2.3.5 Review of EPA Risk Assessment/Risk Management Methodologies
43(1)
2.4 National Aeronautics and Space Administration (NASA): Overview
44(5)
2.4.1 NASA Risk Leadership
44(1)
2.4.2 Critical Steps in NASA Risk Assessment/Risk Management
44(4)
2.4.3 Risk Assessment/Risk Management Challenges of NASA
48(1)
2.4.4 Review of NASA Risk Assessment/Risk Management Methodologies
49(1)
2.5 Nuclear Regulatory Commission (NRC) Overview
49(6)
2.5.1 NRC Leadership
51(1)
2.5.2 Critical Steps in NRC Risk Assessment/Risk Management
52(1)
2.5.3 Risk Assessment/Risk Management Challenges of NRC
53(1)
2.5.4 Review of NRC Risk Assessment/Risk Management Methodologies
54(1)
2.6 International Standards Organization (ISO) Overview
55(3)
2.6.1 ISO Leadership
57(1)
2.6.2 Critical Steps in ISO Risk Assessment/Risk Management
57(1)
2.6.3 Risk Assessment/Risk Management Challenges of ISO
58(1)
2.7 Australia Overview
58(3)
2.7.1 Australia Leadership
59(1)
2.7.2 Critical Steps in Australia Risk Assessment/Risk Management
60(1)
2.7.3 Risk Assessment/Risk Management Challenges of Australia
61(1)
2.8 UK Overview
61(8)
2.8.1 UK Leadership
61(1)
2.8.2 Critical Steps in UK Risk Assessment/Risk Management
62(3)
2.8.3 Risk Assessment/Risk Management Challenges of the United Kingdom
65(1)
Acknowledgments
65(1)
References
65(4)
3 An Overview of Risk Modeling Methods and Approaches for National Security
69(32)
Samrat Chatterjee
Robert T. Brigantic
Angela M. Waterworth
3.1 Introduction
69(1)
3.2 Homeland Security Risk Landscape and Missions
70(3)
3.2.1 Risk Landscape
71(1)
3.2.2 Security Missions
71(1)
3.2.3 Risk Definitions and Interpretations from DHS Risk Lexicon
72(1)
3.3 Background Review
73(15)
3.3.1 1960s to 1990s: Focus on Foundational Concepts
73(2)
3.3.2 The 2000s: Increased Focus on Multi-hazard Risks Including Terrorism
75(3)
3.3.3 2009 to Present: Emerging Emphasis on System Resilience and Complexity
78(10)
3.4 Modeling Approaches for Risk Elements
88(2)
3.4.1 Threat Modeling
88(1)
3.4.2 Vulnerability Modeling
88(1)
3.4.2.1 Survey-Based Methods
88(1)
3.4.2.2 Systems Analysis
89(1)
3.4.2.3 Network-Theoretic Approaches
89(1)
3.4.2.4 Structural Analysis and Reliability Theory
89(1)
3.4.3 Consequence Modeling
89(1)
3.4.3.1 Direct Impacts
89(1)
3.4.3.2 Indirect Impacts
89(1)
3.4.4 Risk-Informed Decision Making
90(1)
3.5 Modeling Perspectives for Further Research
90(4)
3.5.1 Systemic Risk and Resilience Within a Unified Framework
90(1)
3.5.2 Characterizing Cyber and Physical Infrastructure System Behaviors and Hazards
91(1)
3.5.3 Utilizing "Big" Data or Lack of Data for Generating Risk and Resilience Analytics
91(1)
3.5.4 Conceptual Multi-scale, Multi-hazard Modeling Framework
92(2)
3.6 Concluding Remarks
94(7)
Acknowledgments
95(1)
References
95(6)
4 Comparative Risk Rankings in Support of Homeland Security Strategic Plans
101(624)
Russell Lundberg
4.1 Introduction
101(1)
4.2 Conceptual Challenges in Comparative Risk Ranking
102(1)
4.3 Practical Challenges in Comparative Ranking of Homeland Security Risks
103(1)
4.3.1 Choosing a Risk Set
104(1)
4.3.1.1 Lessons from the DMRR on Hazard Set Selection
105(1)
4.3.2 Identifying Attributes to Consider
105(2)
4.3.2.1 Lessons from the DMRR on Attribute Selection
107(2)
4.3.3 Assessing Each Risk Individually
109(2)
4.3.3.1 Lessons from the DMRR on Assessing Individual Homeland Security Risks
111(1)
4.3.4 Combining Individual Risks to Develop a Comparative Risk Ranking
112(2)
4.3.4.1 Lessons from the DMRR on Comparing Homeland Security Risks
114(2)
4.4 Policy Relevance to Strategic-Level Homeland Security Risk Rankings
116(609)
4.4.1 Insights into Homeland Security Risk Rankings
116(2)
4.4.2 Risk vs. Risk Reduction
118(2)
Acknowledgments
120(1)
References
120(5)
5 A Data Science Workflow for Discovering Spatial Patterns Among Terrorist Attacks and Infrastructure
125(1)
Daniel C. Fortin
Thomas Johansen
Samrat Chatterjee
George Muller
Christine Noonan
5.1 Introduction
125(1)
5.2 The Data: Global Terrorism Database
126(1)
5.3 The Tools: Exploring Data Interactively Using a Custom Shiny App
127(3)
5.4 Example: Using the App to Explore ISIL Attacks
130(4)
5.5 The Models: Statistical Models for Terrorist Event Data
134(1)
5.6 More Data: Obtaining Regional Infrastructure Data to Build Statistical Models
135(2)
5.7 A Model: Determining the Significance of Infrastructure on the Likelihood of an Attack
137(1)
5.8 Case Study: Libya
138(1)
5.9 Case Study: Jammu and Kashmir Region of India
139(9)
5.9.1 The Model Revisited: Accounting for Many Regions with No Recorded Attacks
147(1)
5.9.2 Investigating the Effect of Outliers
147(1)
5.9.3 The Insight: What Have We Learned?
147(1)
5.10 Summary
148(1)
References
148(3)
Part II Strengthening Ports of Entry
151(2)
6 Effects of Credibility of Retaliation Threats in Deterring Smuggling of Nuclear Weapons
153(1)
Xiaojun Shan
Jun Zhuang
6.1 Introduction
153(5)
6.2 Extending Prior Game-Based Model
158(1)
6.3 Comparing the Game Trees
158(3)
6.4 The Extended Model
161(1)
6.5 Solution to the Extended Model
162(1)
6.6 Comparing the Solutions in Prior Game-Based Model and This Study
163(1)
6.7 Illustration of the Extended Model Using Real Data
164(1)
6.8 Conclusion and Future Research Work
165(6)
References
167(4)
7 Disutility of Mass Relocation After a Severe Nuclear Accident
171(22)
Vicki M. Bier
Shuji Liu
7.1 Introduction
171(3)
7.2 Raw Data
174(3)
7.3 Trade-Offs Between Cancer Fatalities and Relocation
177(2)
7.4 Risk-Neutral Disutility Model
179(1)
7.5 Risk-Averse Disutility Model
179(3)
7.6 Disutility Model with Interaction Effects
182(3)
7.7 Economic Analysis
185(5)
7.8 Conclusion
190(3)
References
191(2)
8 Scheduling Federal Air Marshals Under Uncertainty
193(28)
Keith W. DeGregory
Rajesh Ganesan
8.1 Introduction
193(3)
8.2 Literature
196(4)
8.2.1 Commercial Aviation Industry
196(2)
8.2.2 Homeland Security and the Federal Air Marshals Service
198(1)
8.2.3 Approximate Dynamic Programming
199(1)
8.3 Air Marshal Resource Allocation Model
200(4)
8.3.1 Risk Model
200(2)
8.3.2 Static Allocation
202(1)
8.3.3 Dynamic Allocation
203(1)
8.4 Stochastic Dynamic Programming Formulation
204(3)
8.4.1 System State
205(1)
8.4.2 Decision Variable
205(1)
8.4.3 Post-decision State
206(1)
8.4.4 Exogenous Information
206(1)
8.4.5 State Transition Function
206(1)
8.4.6 Contribution Function
206(1)
8.4.7 Objective Function
207(1)
8.4.8 Bellman's Optimality Equations
207(1)
8.5 Phases of Stochastic Dynamic Programming
207(3)
8.5.1 Exploration Phase
207(1)
8.5.2 Learning Phase
208(1)
8.5.2.1 Algorithm
208(1)
8.5.2.2 Approximation Methods
208(1)
8.5.2.3 Convergence
209(1)
8.5.3 Learned Phase
210(1)
8.6 Integrated Allocation Model
210(1)
8.7 Results
211(6)
8.7.1 Experiment
211(1)
8.7.2 Results from Stochastic Dynamic Programming Model
211(1)
8.7.3 Sensitivity Analysis
212(2)
8.7.4 Model Output
214(3)
8.8 Conclusion
217(4)
Acknowledgments
218(1)
References
218(3)
Part III Securing Critical Cyber Assets
221(2)
9 Decision Theory for Network Security: Active Sensing for Detection and Prevention of Data Exfiltration
223(1)
Sara M. McCarthy
Arunesh Sinha
Milind Tambe
Pratyusa Manadhatha
9.1 Introduction
223(3)
9.1.1 Problem Domain
224(2)
9.2 Background and Related Work
226(3)
9.2.1 DNS Exfiltration
226(2)
9.2.2 Partially Observable Markov Decision Process (POMDP)
228(1)
9.3 Threat Model
229(3)
9.3.1 The POMDP Model
230(2)
9.4 POMDP Abstraction
232(7)
9.4.1 Abstract Actions
232(2)
9.4.2 Abstract Observations
234(1)
9.4.3 VD-POMDP Factored Representation
234(2)
9.4.4 Policy Execution
236(3)
9.5 VD-POMDP Framework
239(2)
9.6 Evaluation
241(6)
9.6.1 Synthetic Networks
241(1)
9.6.2 DETER Testbed Simulation
241(1)
9.6.3 Runtime
242(2)
9.6.4 Performance
244(2)
9.6.5 Robustness
246(1)
9.7 Game Theoretic Extensions
247(2)
9.7.1 Threat Model
248(1)
9.8 Conclusion and Future Work
249(4)
Acknowledgments
249(1)
References
249(4)
10 Measurement of Cyber Resilience from an Economic Perspective
253(22)
Adam Z. Rose
Noah Miller
10.1 Introduction
253(1)
10.2 Economic Resilience
254(3)
10.2.1 Basic Concepts of Cyber Resilience
254(1)
10.2.2 Basic Concepts of Economic Resilience
254(1)
10.2.3 Economic Resilience Metrics
255(2)
10.3 Cyber System Resilience Tactics
257(10)
10.4 Resilience for Cyber-Related Sectors
267(2)
10.4.1 Resilience in the Manufacturing of Cyber Equipment
267(1)
10.4.2 Resilience in the Electricity Sector
268(1)
10.5 Conclusion
269(6)
References
270(5)
11 Responses to Cyber Near-Misses: A Scale to Measure Individual Differences
275(20)
Jinshu Cui
Heather Rosoff
Richard S. John
11.1 Introduction
275(2)
11.2 Scale Development and Analysis Outline
277(1)
11.3 Method
278(6)
11.3.1 Measures
278(1)
11.3.1.1 Cyber Near-Miss Appraisal Scale (CNMAS)
278(3)
11.3.1.2 Measures of Discriminant Validity
281(1)
11.3.1.3 Measure of Predictive Validity
281(1)
11.3.1.4 Participants and Procedures
281(3)
11.4 Results
284(7)
11.4.1 Dimensionality and Reliability
284(1)
11.4.2 Item Response Analysis
284(3)
11.4.3 Differential Item Functioning (DIF)
287(2)
11.4.4 Effects of Demographic Variables
289(1)
11.4.5 Discriminant Validity
290(1)
11.4.6 Predictive Validity
290(1)
11.5 Discussion
291(4)
Acknowledgments
292(1)
References
292(3)
Part IV Enhancing Disaster Preparedness and Infrastructure Resilience
295(2)
12 An Interactive Web-Based Decision Support System for Mass Dispensing, Emergency Preparedness, and Biosurveillance
297(1)
Eva K. Lee
Ferdinand H. Pietz
Chien-Hung Chen
Yifan Liu
12.1 Introduction
297(2)
12.2 System Architecture and Design
299(2)
12.3 System Modules and Functionalities
301(11)
12.3.1 Interactive User Experience
301(1)
12.3.2 Geographical Boundaries
301(1)
12.3.3 Network of Service, Locations, and Population Flow and Assignment
302(2)
12.3.4 ZIP Code and Population Composition
304(1)
12.3.5 Multimodality Dispensing and Public-Private Partnership
305(3)
12.3.6 POD Layout Design and Resource Allocation
308(1)
12.3.7 Radiological Module
309(1)
12.3.8 Biosurveillance
309(1)
12.3.9 Regional Information Sharing, Reverse Reporting, Tracking and Monitoring, and Resupply
310(1)
12.3.10 Multilevel End-User Access
311(1)
12 A Biodefense, Pandemic Preparedness Planning, and Radiological and Large-Scale Disaster Relief Efforts
312(13)
12.4.1 Biodefense Mass Dispensing Regional Planning
312(3)
12.4.2 Real-Life Disaster Response Effort
315(1)
12.4.2.1 RealOpt-Haiti©
315(1)
12.4.2.2 RealOpt-Regional and RealOpt-CRC for Fukushima Daiichi Nuclear Disaster
316(2)
12.4.2.3 RealOpt-ASSURE©
318(1)
12.5 Challenges and Conclusions
319(6)
Acknowledgments
321(1)
References
321(4)
13 Measuring Critical Infrastructure Risk, Protection, and Resilience in an All-Hazards Environment
325(32)
Julia A. Phillips
Frederic Petit
13.1 Introduction to Critical Infrastructure Risk Assessment
325(1)
13.2 Motivation for Critical Infrastructure Risk Assessments
326(1)
13.2.1 Unrest pre-September 2001
326(1)
13.2.2 Post-911 Critical Infrastructure Protection and Resilience
326(1)
13.3 Decision Analysis Methodologies for Creating Critical Infrastructure Risk Indicators
327(4)
13.3.1 Decision Analysis
328(1)
13.3.2 Illustrative Calculations for an Index: Buying a Car
328(3)
13.4 An Application of Critical Infrastructure Protection, Consequence, and Resilience Assessment
331(19)
13.4.1 Protection and Vulnerability
334(1)
13.4.1.1 Physical Security
335(1)
13.4.1.2 Security Management
335(1)
13.4.1.3 Security Force
335(2)
13.4.1.4 Information Sharing
337(1)
13.4.1.5 Security Activity Background
338(1)
13.4.2 Resilience
339(2)
13.4.2.1 Preparedness
341(1)
13.4.2.2 Mitigation Measures
341(1)
13.4.2.3 Response Capabilities
342(1)
13.4.2.4 Recovery Mechanisms
343(1)
13.4.3 Consequences
343(2)
13.4.3.1 Human Consequences
345(1)
13.4.3.2 Economic Consequences
346(1)
13.4.3.3 Government Mission/Public Health/Psychological Consequences
346(1)
13.4.3.4 Cascading Impact Consequences
347(2)
13.4.4 Risk Indices Comparison
349(1)
13.5 Infrastructure Interdependencies
350(2)
13.6 What's Next for Critical Infrastructure Risk Assessments
352(5)
References
354(3)
14 Risk Analysis Methods in Resilience Modeling: An Overview of Critical Infrastructure Applications
357(24)
Hiba Baroud
14.1 Introduction
357(1)
14.2 Background
358(3)
14.2.1 Risk Analysis
358(1)
14.2.2 Resilience
359(1)
14.2.3 Critical Infrastructure Systems
360(1)
14.3 Modeling the Resilience of Critical Infrastructure Systems
361(7)
14.3.1 Resilience Models
361(1)
14.3.1.1 Manufacturing
361(1)
14.3.1.2 Communications
362(1)
14.3.1.3 Dams, Levees, and Waterways
363(1)
14.3.1.4 Defense
363(1)
14.3.1.5 Emergency Services
363(1)
14.3.1.6 Energy
363(1)
14.3.1.7 Transportation
364(1)
14.3.1.8 Water/Wastewater
364(1)
14.3.2 Discussion
365(1)
14.3.2.1 Economic Impact
365(2)
14.3.2.2 Social Impact
367(1)
14.3.2.3 Interdependencies
367(1)
14.4 Assessing Risk in Resilience Models
368(2)
14.4.1 Probabilistic Methods
368(1)
14.4.2 Uncertainty Modeling
369(1)
14.4.3 Simulation-Based Approaches
369(1)
14.4.4 Data-Driven Analytics
370(1)
14.5 Opportunities and Challenges
370(2)
14.5.1 Opportunities
370(1)
14.5.2 Challenges
371(1)
14.6 Concluding Remarks
372(9)
References
373(8)
15 Optimal Resource Allocation Model to Prevent, Prepare, and Respond to Multiple Disruptions, with Application to the Deepwater Horizon Oil Spill and Hurricane Katrina
381(24)
Cameron A. MacKenzie
Amro Al Kazimi
15.1 Introduction
381(2)
15.2 Model Development
383(3)
15.2.1 Resource Allocation Model
383(2)
15.2.2 Extension to Uncertain Parameters
385(1)
15.3 Application: Deepwater Horizon and Hurricane Katrina
386(11)
15.3.1 Parameter Estimation
386(1)
15.3.1.1 Oil Spill Parameters
387(1)
15.3.1.2 Hurricane Parameters
388(3)
15.3.2 Base Case Results
391(3)
15.3.3 Sensitivity Analysis on Economic Impacts
394(1)
15.3.4 Model with Uncertain Effectiveness
395(2)
15.4 Conclusions
397(8)
References
398(7)
16 Inoperability Input-Output Modeling of Electric Power Disruptions
405(22)
Joost R. Santos
Sheree Ann Pagsuyoin
Christian Yip
16.1 Introduction
405(2)
16.2 Risk Analysis of Natural and Man-Caused Electric Power Disruptions
407(1)
16.3 Risk Management Insights for Disruptive Events
408(3)
16.4 Modeling the Ripple Effects for Disruptive Events
411(1)
16.5 Inoperability Input-Output Model
412(4)
16.5.1 Model Parameters
412(1)
16.5.2 Sector Inoperability
413(1)
16.5.3 Interdependency Matrix
413(1)
16.5.4 Demand Perturbation
414(1)
16.5.5 Economic Resilience
414(1)
16.5.6 Economic Loss
415(1)
16.6 Sample Electric Power Disruptions Scenario Analysis for the United States
416(5)
16.7 Summary and Conclusions
421(6)
References
422(5)
17 Quantitative Assessment of Transportation Network Vulnerability with Dynamic Traffic Simulation Methods
427(16)
Venkateswaran Shekar
Lance Fiondella
17.1 Introduction
427(2)
17.2 Dynamic Transportation Network Vulnerability Assessment
429(2)
17.3 Sources of Input for Dynamic Transportation Network Vulnerability Assessment
431(1)
17.4 Illustrations
432(7)
17.4.1 Example I: Simple Network
432(5)
17.4.2 Example II: University of Massachusetts Dartmouth Evacuation
437(2)
17.5 Conclusion and Future Research
439(4)
References
440(3)
18 Infrastructure Monitoring for Health and Security
443(24)
Prodyot K. Basu
18.1 Introduction
443(4)
18.2 Data Acquisition
447(1)
18.3 Sensors
447(12)
18.3.1 Underlying Principles of Some of the Popular Sensors Listed in Table 18.1
451(1)
18.3.1.1 Fiber Optics
451(1)
18.3.1.2 Vibrating Wire
451(5)
18.3.1.3 Piezoelectric Sensors
456(1)
18.3.1.4 Piezoresistive Sensors
456(1)
18.3.1.5 Laser Vibrometer
456(1)
18.3.1.6 Acoustic Emission Sensing
457(1)
18.3.1.7 GPSandGNSS
458(1)
18.3.2 Selection of a Sensor
459(1)
18.4 Capturing and Transmitting Signals
459(2)
18.5 Energy Harvesting
461(1)
18.6 Robotic IHM
462(2)
18.7 Cyber-Physical Systems
464(1)
18.8 Conclusions
464(3)
References
465(2)
19 Exploring Metaheuristic Approaches for Solving the Traveling Salesman Problem Applied to Emergency Planning and Response
467(20)
Ramakrishna Tipireddy
Javier Rubio-Herrero
Samrat Chatterjee
Satish Chikkagoudar
19.1 The Traveling Salesman Problem
467(1)
19.1.1 Definition
467(1)
19.1.2 Computational Complexity
467(1)
19.1.3 Solution Algorithms
468(1)
19.1.4 Emergency Response Application
468(1)
19.2 Emergency Planning and Response as a Traveling Salesman Problem
468(1)
19.3 Metaheuristic Approaches
469(13)
19.3.1 Simulated Annealing
470(1)
19.3.1.1 Overview
470(1)
19.3.1.2 Pseudocode
471(2)
19.3.1.3 Case Study Results
473(1)
19.3.2 Tabu Search
473(1)
19.3.2.1 Overview
473(1)
19.3.2.2 Pseudocode
474(2)
19.3.2.3 Case Study Results
476(1)
19.3.3 Genetic Algorithms
476(1)
19.3.3.1 Overview
476(2)
19.3.3.2 Pseudocode
478(1)
19.3.3.3 Case Study Results
479(1)
19.3.4 Ant Colony Optimization
479(1)
19.3.4.1 Overview
479(1)
19.3.4.2 Stochastic Solution Construction
480(1)
19.3.4.3 Pheromone Update
480(1)
19.3.4.4 Pseudocode
481(1)
19.3.4.5 Case Study Results
481(1)
19.4 Discussion
482(1)
19.5 Concluding Remarks
482(5)
References
484(3)
Index 487
SAMRAT CHATTERJEE, PhD, is Senior Operations Research/Data Scientist and the Decision Modeling and Optimization Team Lead in the Computing and Analytics Division within the National Security Directorate at Pacific Northwest National Laboratory (PNNL). He is also Affiliate Professor of Civil and Environmental Engineering with Northeastern University in Boston.

ROBERT T. BRIGANTIC, PhD, is Chief Operations Research Scientist and the Statistical Modeling and Experimental Design Team Lead in the Computing and Analytics Division within the National Security Directorate at PNNL. He is also Adjunct Professor of Operations Research with the Carson College of Business at the Washington State University.

ANGELA M. WATERWORTH, MS, is Senior Operations Research/Data Scientist in the Computing and Analytics Division within the National Security Directorate at PNNL.
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