Stochastic Modeling and Optimization Methods for Critical Infrastructure Protection, Volume 1

1. Auflage 2025
ISBN: 978-1-83669-027-6
Verlag: John Wiley & Sons

Buch, Englisch, 288 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 581 g

Stochastic Modeling and Optimization Methods for Critical Infrastructure Protection is a thorough exploration of mathematical models and tools that are designed to strengthen critical infrastructures against threats – both natural and adversarial. Divided into two volumes, this first volume examines stochastic modeling across key economic sectors and their interconnections, while the second volume focuses on advanced mathematical methods for enhancing infrastructure protection.

The book covers a range of themes, including risk assessment techniques that account for systemic interdependencies within modern technospheres, the dynamics of uncertainty, instability and system vulnerabilities. The book also presents other topics such as cryptographic information protection and Shannon's theory of secret systems, alongside solutions arising from optimization, game theory and machine learning approaches.

Featuring research from international collaborations, this book covers both theory and applications, offering vital insights for advanced risk management curricula. It is intended not only for researchers, but also educators and professionals in infrastructure protection and stochastic optimization.

Autoren/Hrsg.

Gaivoronski, Alexei A

Knopov, Pavel S.

Norkin, Vladimir I.

Zaslavskyi, Volodymyr A.

Fachgebiete

• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde Werkstoffkunde, Materialwissenschaft: Forschungsmethoden
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Technische Wissenschaften Bauingenieurwesen Bauingenieurwesen
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure

Inhaltsverzeichnis

Preface xi
Alexei A. GAIVORONSKI, Pavel S. KNOPOV, Vladimir I. NORKIN and Volodymyr A. ZASLAVSKYI

Part 1. Model-Based Risk Management in Critical Economic Sectors 1

Introduction to Part 1 3
Alexei A. GAIVORONSKI and Pavel S. KNOPOV

Chapter 1. Integrated Solutions and Distributed Models' Linkage Procedures for Food--Energy--Water--Environmental Nexus Security Modeling and Management 5
Tatiana Y. ERMOLIEVA, Anatoly G. ZAGORODNY, Viacheslav L. BOGDANOV, Petr HAVLIK, Elena ROVENSKAJA, Nadejda KOMENDANTOVA and Pavel S. KNOPOV

1.1. Introduction 6

1.2. Linking distributed optimization models under joint resource constraints 8

1.3. Linking energy and agricultural models for FEW nexus 15

1.4. Conclusion 20

1.5. Acknowledgments 20

1.6. References 21

Chapter 2. Integrated Modeling for Managing Catastrophic Risks: Vulnerability Analysis and Systemic Risks Management 25
Nadejda KOMENDANTOVA, Tatiana Y. ERMOLIEVA, Taher ZOBEIDI, Iuliana ARMAS, Dragos TOMA-DANILA and Marcel HÜRLIMANN

2.1. Introduction 26

2.2. Management of endogenous systemic risks: safety indicators and robust measures 27

2.3. Types of vulnerability and vulnerability analysis approaches 36

2.4. Statistical and ML approaches to analyze future vulnerabilities 41

2.5. Application of the ML regression model to future vulnerability scenarios testing 47

2.6. Conclusion 49

2.7. Acknowledgments 51

2.8. References 51

Chapter 3. Robust Statistical Estimation and Two-Stage Stochastic Optimization: Quantile Regression EPIC Meta-Model of Soil Organic Carbon for Robust Decision Making with GLOBIOM 59
Tatiana Y. ERMOLIEVA, Petr HAVLIK, Andrey LESSA-DERCI-AUGUSTYNCZIK, Stefan FRANK, Andre DEPPERMANN, Andrè (Mahdi) NAKHAVALI, Juraj BALKOVIC, Rastislav SKALSKY and Nadejda KOMENDANTOVA

3.1. Introduction 60

3.2. Concept of robustness in statistical and general decision-making problems 63

3.3. Quantile-based machine learning regression model for tracking the dynamics and uncertainties of soil organic carbon in agricultural soils using multisource data 68

3.4. Conclusion 75

3.5. Acknowledgments 75

3.6. References 76

Chapter 4. A Multi-Stage Multi-Horizon Stochastic Equilibrium Model of Multi-Fuel Energy Markets 79
Zhonghua SU, Alexei A. GAIVORONSKI and Asgeir TOMASGARD

4.1. Introduction 79

4.2. Model formulation and model-specific background 82

4.3. Case study 101

4.4. Results analysis 106

4.5. Conclusion and outlook for future work 116

4.6. Appendix 117

4.7. References 127

Chapter 5. Modeling for Diversification and Optimization of the Electricity Generation Capacities in the Energy Sector 131
Volodymyr A. ZASLAVSKYI and Maya PASICHNA

5.1. Introduction 131

5.2. Data collection 140

5.3. Conclusion 141

5.4. References 143

Part 2. Reliability Theory for Ensuring Safety of Critical Infrastructure Facilities 147

Introduction to Part 2 149
Pavel S. KNOPOV and Volodymyr A. ZASLAVSKYI

Chapter 6. Methods of Risk Assessment in Environmentally Hazardous Industries 151
Pavel S. KNOPOV, Aleksandr N. GOLODNIKOV, Volodumir A. PEPELYAEV and Liliia B. VOVK

6.1. Introduction 151

6.2. Analysis of publications 157

6.3. Evaluation methods with a limited amount of statistical information 164

6.4. Conclusion 178

6.5. Acknowledgments 179

6.6. References 179

Chapter 7. Application of the Type-Variety Principle in the Formation of a Complex of Non-Destructive Testing Technologies for Critical Infrastructure 183
Volodymyr A. ZASLAVSKYI

7.1. Introduction 183

7.2. Optimization algorithm for a heterogeneous set of non-destructive testing methods for defect detection in complex systems 185

7.3. Conclusion 204

7.4. References 204

Chapter 8. The Type-Variety Principle, Mathematical Models and Algorithms for the Optimization of the Reliability of Series-Parallel and Parallel-Series Systems, the Elements of Which Allow Two Types of Failures 207
Volodymyr A. ZASLAVSKYI and Oleg FRANCHUK

8.1. Introduction 208

8.2. Models and algorithms for optimizing the reliability of series--parallel systems the elements of which allow two types of failures. 208

8.3. Optimal type-variety redundancy of parallel--serial systems, the element of which allows two types of failures 218

8.4. Conclusion 230

8.5. References 230

Chapter 9. Mathematical Models for the Study of Critical Infrastructure Vulnerability 233
Konstantin ATOYEV

9.1. Introduction 233

9.2. The general approach to CI risk assessment 234

9.3. The model for assessment of CI vulnerability 236

9.4. Assessing the consequences of CI disruptions 239

9.5. Dependence on small changes in model parameters 244

9.6. Assessment of CI risks for separate economic sectors 247

9.7. Conclusion 250

9.8. Acknowledgments 250

9.9. References 251

List of Authors 255

Index 259

Summary of Volume 2 263

Über Autor(innen)

Alexei A. Gaivoronski is Professor at the Norwegian University of Science and Technology, Norway. His research focuses on risk theory, and its applications in finance, energy, telecommunications and stochastic optimization.

Pavel S. Knopov is Head of Department at V.M. Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine. His research focuses on statistical decision theory, stochastic optimal control and stochastic optimization.

Vladimir I. Norkin is Leading Researcher at V.M. Glushkov Institute of Cybernetics, Ukraine. His research focuses on operations research.

Volodymyr A. Zaslavskyi is Professor at the Taras Shevchenko National University of Kyiv, Ukraine. His research focuses on systems analysis, risk and the reliability of critical systems.