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

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.

• Preface xiAlexei A. GAIVORONSKI, Pavel S. KNOPOV, Vladimir I. NORKIN and Volodymyr A. ZASLAVSKYIPart 1. Model-Based Risk Management in Critical Economic Sectors 1Introduction to Part 1 3Alexei A. GAIVORONSKI and Pavel S. KNOPOVChapter 1. Integrated Solutions and Distributed Models' Linkage Procedures for Food--Energy--Water--Environmental Nexus Security Modeling and Management 5Tatiana Y. ERMOLIEVA, Anatoly G. ZAGORODNY, Viacheslav L. BOGDANOV, Petr HAVLIK, Elena ROVENSKAJA, Nadejda KOMENDANTOVA and Pavel S. KNOPOV1.1. Introduction 61.2. Linking distributed optimization models under joint resource constraints 81.3. Linking energy and agricultural models for FEW nexus 151.4. Conclusion 201.5. Acknowledgments 201.6. References 21Chapter 2. Integrated Modeling for Managing Catastrophic Risks: Vulnerability Analysis and Systemic Risks Management 25Nadejda KOMENDANTOVA, Tatiana Y. ERMOLIEVA, Taher ZOBEIDI, Iuliana ARMAS, Dragos TOMA-DANILA and Marcel HÜRLIMANN2.1. Introduction 262.2. Management of endogenous systemic risks: safety indicators and robust measures 272.3. Types of vulnerability and vulnerability analysis approaches 362.4. Statistical and ML approaches to analyze future vulnerabilities 412.5. Application of the ML regression model to future vulnerability scenarios testing 472.6. Conclusion 492.7. Acknowledgments 512.8. References 51Chapter 3. Robust Statistical Estimation and Two-Stage Stochastic Optimization: Quantile Regression EPIC Meta-Model of Soil Organic Carbon for Robust Decision Making with GLOBIOM 59Tatiana Y. ERMOLIEVA, Petr HAVLIK, Andrey LESSA-DERCI-AUGUSTYNCZIK, Stefan FRANK, Andre DEPPERMANN, Andrè (Mahdi) NAKHAVALI, Juraj BALKOVIC, Rastislav SKALSKY and Nadejda KOMENDANTOVA3.1. Introduction 603.2. Concept of robustness in statistical and general decision-making problems 633.3. Quantile-based machine learning regression model for tracking the dynamics and uncertainties of soil organic carbon in agricultural soils using multisource data 683.4. Conclusion 753.5. Acknowledgments 753.6. References 76Chapter 4. A Multi-Stage Multi-Horizon Stochastic Equilibrium Model of Multi-Fuel Energy Markets 79Zhonghua SU, Alexei A. GAIVORONSKI and Asgeir TOMASGARD4.1. Introduction 794.2. Model formulation and model-specific background 824.3. Case study 1014.4. Results analysis 1064.5. Conclusion and outlook for future work 1164.6. Appendix 1174.7. References 127Chapter 5. Modeling for Diversification and Optimization of the Electricity Generation Capacities in the Energy Sector 131Volodymyr A. ZASLAVSKYI and Maya PASICHNA5.1. Introduction 1315.2. Data collection 1405.3. Conclusion 1415.4. References 143Part 2. Reliability Theory for Ensuring Safety of Critical Infrastructure Facilities 147Introduction to Part 2 149Pavel S. KNOPOV and Volodymyr A. ZASLAVSKYIChapter 6. Methods of Risk Assessment in Environmentally Hazardous Industries 151Pavel S. KNOPOV, Aleksandr N. GOLODNIKOV, Volodumir A. PEPELYAEV and Liliia B. VOVK6.1. Introduction 1516.2. Analysis of publications 1576.3. Evaluation methods with a limited amount of statistical information 1646.4. Conclusion 1786.5. Acknowledgments 1796.6. References 179Chapter 7. Application of the Type-Variety Principle in the Formation of a Complex of Non-Destructive Testing Technologies for Critical Infrastructure 183Volodymyr A. ZASLAVSKYI7.1. Introduction 1837.2. Optimization algorithm for a heterogeneous set of non-destructive testing methods for defect detection in complex systems 1857.3. Conclusion 2047.4. References 204Chapter 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 207Volodymyr A. ZASLAVSKYI and Oleg FRANCHUK8.1. Introduction 2088.2. Models and algorithms for optimizing the reliability of series--parallel systems the elements of which allow two types of failures. 2088.3. Optimal type-variety redundancy of parallel--serial systems, the element of which allows two types of failures 2188.4. Conclusion 2308.5. References 230Chapter 9. Mathematical Models for the Study of Critical Infrastructure Vulnerability 233Konstantin ATOYEV9.1. Introduction 2339.2. The general approach to CI risk assessment 2349.3. The model for assessment of CI vulnerability 2369.4. Assessing the consequences of CI disruptions 2399.5. Dependence on small changes in model parameters 2449.6. Assessment of CI risks for separate economic sectors 2479.7. Conclusion 2509.8. Acknowledgments 2509.9. References 251List of Authors 255Index 259Summary of Volume 2 263