Advanced risk analysis in engineering enterprise systems

Since the emerging discipline of engineering enterprise systems extends traditional systems engineering to develop webs of systems and systems-of-systems, the engineering management and management science communities need new approaches for analyzing and managing risk in engineering enterprise systems. Advanced Risk Analysis in Engineering Enterprise Systems presents innovative methods to address these needs.

With a focus on engineering management, the book explains how to represent, model, and measure risk in large-scale, complex systems that are engineered to function in enterprise-wide environments. Along with an analytical framework and computational model, the authors introduce new protocols: the risk co-relationship (RCR) index and the functional dependency network analysis (FDNA) approach. These protocols capture dependency risks and risk co-relationships that may exist in an enterprise.

Moving on to extreme and rare event risks, the text discusses how uncertainties in system behavior are intensified in highly networked, globally connected environments. It also describes how the risk of extreme latencies in delivering time-critical data, applications, or services can have catastrophic consequences and explains how to avoid these events.

With more and more communication, transportation, and financial systems connected across domains and interfaced with an infinite number of users, information repositories, applications, and services, there has never been a greater need for analyzing risk in engineering enterprise systems. This book gives you advanced methods for tackling risk problems at the enterprise level.

Author Notes

C. Ariel Pinto is an Associate Professor in the Department of Engineering Management and Systems Engineering at Old Dominion University, where he co-founded the Emergent Risk Initiative. He earned a Ph.D. in systems engineering from the University of Virginia. Dr. Pinto's research interests encompass the areas of risk management in engineered systems, including project risk management, risk valuation, risk communication, analysis of extreme-and-rare events, and decision making under uncertainty.

Paul R. Garvey is Chief Scientist and a Director for the Center for Acquisition and Systems Analysis, a division of The MITRE Corporation. He earned an A.B. and M.Sc. in pure and applied mathematics from Boston College and Northeastern University, respectively, and a Ph.D. in engineering management from Old Dominion University, where he was awarded the doctoral dissertation medal from the faculty of the College of Engineering. He is the author of the CRC Press books Analytical Methods for Risk Management and Probability Methods for Cost Uncertainty Analysis. Dr. Garvey's research interests include the theory and application of risk-decision analytic methods to operations research problems in the system sciences domains.

Preface	p. xv
Acknowledgments	p. xix
Authors	p. xxi
1 Engineering Risk Management	p. 1
1.1 Introduction	p. 1
1.1.1 Boston's Central Artery/Tunnel Project	p. 2
1.2 Objectives and Practices	p. 6
1.3 New Challenges	p. 12
Questions and Exercises	p. 13
2 Perspectives on Theories of Systems and Risk	p. 15
2.1 Introduction	p. 15
2.2 General Systems Theory	p. 15
2.2.1 Complex Systems, Systems-of-Systems, and Enterprise Systems	p. 20
2.3 Risk and Decision Theory	p. 24
2.4 Engineering Risk Management	p. 36
Questions and Exercises	p. 39
3 Foundations of Risk and Decision Theory	p. 41
3.1 Introduction	p. 41
3.2 Elements of Probability Theory	p. 41
3.3 The Value Function	p. 63
3.4 Risk and Utility Functions	p. 81
3.4.1 vNM Utility Theory	p. 81
3.4.2 Utility Functions	p. 85
3.5 Multiattribute Utility-The Power Additive Utility Function	p. 97
3.5.1 The Power-Additive Utility Function	p. 97
3.5.2 Applying the Power-Additive Utility Function	p. 98
3.6 Applications to Engineering Risk Management	p. 101
3.6.1 Value Theory to Measure Risk	p. 102
3.6.2 Utility Theory to Compare Designs	p. 114
Questions and Exercises	p. 119
4 A Risk Analysis Framework in Engineering Enterprise Systems	p. 125
4.1 Introduction	p. 125
4.2 Perspectives on Engineering Enterprise Systems	p. 125
4.3 A Framework for Measuring Enterprise Capability Risk	p. 129
4.4 A Risk Analysis Algebra	p. 133
4.5 Information Needs for Portfolio Risk Analysis	p. 149
4.6 The "Cutting Edge"	p. 150
Questions and Exercises	p. 151
5 An Index to Measure Risk Corelationships	p. 157
5.1 Introduction	p. 157
5.2 RCR Postulates, Definitions, and Theory	p. 158
5.3 Computing the RCR Index	p. 164
5.4 Applying the RCR Index: A Resource Allocation Example	p. 171
5.5 Summary	p. 174
Questions and Exercises	p. 174
6 Functional Dependency Network Analysis	p. 177
6.1 Introduction	p. 177
6.2 FDNA Fundamentals	p. 178
6.3 Weakest Link Formulations	p. 186
6.4 FDNA (¿, ß) Weakest Link Rule	p. 191
6.5 Network Operability and Tolerance Analyses	p. 215
6.5.1 Critical Node Analysis and Degradation Index	p. 222
6.5.2 Degradation Tolerance Level	p. 227
6.6 Special Topics	p. 237
6.6.1 Operability Function Regulation	p. 237
6.6.2 Constituent Nodes	p. 239
6.6.3 Addressing Cycle Dependencies	p. 245
6.7 Summary	p. 247
Questions and Exercises	p. 249
7 A Decision-Theoretic Algorithm for Ranking Risk Criticality	p. 257
7.1 Introduction	p. 257
7.2 A Prioritization Algorithm	p. 257
7.2.1 Linear Additive Model	p. 258
7.2.2 Compromise Models	p. 259
7.2.3 Criteria Weights	p. 262
7.2.4 Illustration	p. 265
Questions and Exercises	p. 269
8 A Model for Measuring Risk in Engineering Enterprise Systems	p. 271
8.1 A Unifying Risk Analytic Framework and Process	p. 271
8.1.1 A Traditional Process with Nontraditional Methods	p. 271
8.1.2 A Model Formulation for Measuring Risk in Engineering Enterprise Systems	p. 272
8.2 Summary	p. 279
Questions and Exercises	p. 279
9 Random Processes and Queuing Theory	p. 281
9.1 Introduction	p. 281
9.2 Deterministic Process	p. 282
9.2.1 Mathematical Determinism	p. 283
9.2.2 Philosophical Determinism	p. 284
9.3 Random Process	p. 284
9.3.1 Concept of Uncertainity	p. 286
9.3.2 Uncertainty, Randomness, and Probability	p. 287
9.3.3 Causality and Uncertainty	p. 289
9.3.4 Necessary and Sufficient Causes	p. 291
9.3.5 Causalities and Risk Scenario Identification	p. 291
9.3.6 Probabilistic Causation	p. 293
9.4 Markov Process	p. 298
9.4.1 Birth and Death Process	p. 300
9.5 Queuing Theory	p. 300
9.5.1 Characteristic of Queuing Systems	p. 302
9.5.2 Poisson Process and Distribution	p. 303
9.5.3 Exponential Distribution	p. 304
9.6 Basic Queuing Models	p. 304
9.6.1 Single-Server Model	p. 304
9.6.2 Probability of an Empty Queuing System	p. 306
9.6.3 Probability That There are Exactly N Entities Inside the Queuing Systems	p. 307
9.6.4 Mean Number of Entities in the Queuing System	p. 308
9.6.5 Mean Number of Waiting Entities	p. 308
9.6.6 Average Latency Time of Entities	p. 308
9.6.7 Average Time of an Entity Waiting to Be Served	p. 309
9.7 Applications to Engineering Systems	p. 310
9.8 Summary	p. 315
Questions and Exercises	p. 316
10 Extreme Event Theory	p. 323
10.1 Introduction to Extreme and Rare Events	p. 323
10.2 Extreme and Rare Events and Engineering Systems	p. 324
10.3 Traditional Data Analysis	p. 325
10.4 Extreme Value Analysis	p. 327
10.5 Extreme Event Probability Distributions	p. 329
10.5.1 Independent Single-Order Statistic	p. 331
10.6 Limit Distributions	p. 334
10.7 Determining Domain of Attraction Using Inverse Function	p. 336
10.8 Determining Domain of Attraction Using Graphical Method	p. 341
10.8.1 Steps in Visual Analysis of Empirical Data	p. 341
10.8.2 Estimating Parameters of GEVD	p. 345
10.9 Complex Systems and Extreme and Rare Events	p. 347
10.9.1 Extreme and Rare Events in a Complex System	p. 348
10.9.2 Complexity and Causality	p. 349
10.9.3 Complexity and Correlation	p. 349
10.9.4 Final Words on Causation	p. 350
10.10 Summary	p. 351
Questions and Exercises	p. 351
11 Prioritization Systems in Highly Networked Environments	p. 357
11.1 Introduction	p. 357
11.2 Priority Systems	p. 357
11.2.1 PS Notation	p. 358
11.3 Types of Priority Systems	p. 363
11.3.1 Static Priority Systems	p. 363
11.3.2 Dynamic Priority Systems	p. 365
11.3.3 State-Dependent DPS	p. 365
11.3.4 Time-Dependent DPS	p. 371
11.4 Summary	p. 375
Questions and Exercises	p. 375
Questions	p. 376
12 Risks of Extreme Events in Complex Queuing Systems	p. 379
12.1 Introduction	p. 379
12.2 Risk of Extreme Latency	p. 379
12.2.1 Methodology for Measurement of Risk	p. 381
12.3 Conditions for Unbounded Latency	p. 386
12.3.1 Saturated PS	p. 388
12.4 Conditions for Bounded Latency	p. 389
12.4.1 Bounded Latency Times in Saturated Static PS	p. 389
12.4.2 Bounded Latency Times in a Saturated SDPS	p. 392
12.4.3 Combinations of Gumbel Types	p. 394
12.5 Derived Performance Measures	p. 395
12.5.1 Tolerance Level for Risk	p. 395
12.5.2 Degree of Deficit	p. 397
12.5.3 Relative Risks	p. 398
12.5.4 Differentation Tolerance Level	p. 400
12.5.5 Cost Functions	p. 401
12.6 Optimization of PS	p. 403
12.6.1 Cost Function Minimization	p. 404
12.6.2 Bounds on Waiting Line	p. 404
12.6.3 Pessimistic and Optimistic Decisions in Extremes	p. 406
12.7 Summary	p. 410
Questions and Exercises	p. 411
Appendix Bernoulli Utility and the St. Petersburg Paradox	p. 415
A.1.1 The St. Petersburg Paradox	p. 415
A.1.2 Use Expected Utility, Not Expected Value	p. 417
Questions and Exercises	p. 419
References	p. 421
Index	p. 429

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