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El. knyga: Advanced Risk Analysis in Engineering Enterprise Systems

4.50/5 (4 ratings by Goodreads)
(Old Dominion University, Norfolk, Virginia, USA), (The MITRE Corporation, Bedford, Massachusetts, USA)
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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.

Recenzijos

"The book develops several topics in risk analysis, including models and measurement of engineering risks, capability portfolio risk analysis and management, functional dependency network analysis, and extreme-event theory. Several chapters present some tools from applied probability and statistics A reader with main interest in statistics may find in these chapters several ideas about the use of specific statistical tools in applied engineering. Questions and exercises are provided in each chapter to help the reader understand the main topics." Fabrizio Durante, International Statistical Review, 2014

"excellent references appropriate for risk engineers or quality professionals wishing to gain a comprehensive understanding of the engineering or mathematics behind advanced risk analysis. I would certainly recommend this book to anyone working in high-risk, complex environments, such as nuclear, aerospace or explosives." Paul Naysmith, Quality World

"The book is a decidedly unique and rigorous treatment of selected topics in engineering systems risk analysis and management. The narrative is notably modern and clear. The mathematical formalism is comprehensive and advanced while remaining accessible for those involved in engineering complex systems. This is foremost a book of exciting and innovative ideas for the field, exceeding what might easily have been a rote assembly of worn methods or re-introduction of the works of others. It will be of long-standing appeal to practitioners engaged in the analysis of risk in engineering enterprise systems. The book will also appeal to scholars and researchers who will benefit from the advanced and fresh thinking it offers readers. The book will improve the systems engineering communitys ability to address enterprise design risk assessment and management across a systems lifecycle." Professor James Lambert, Associate Director, Center for Risk Management of Engineering Systems, University of Virginia

Preface xv
Acknowledgments xix
Authors xxi
1 Engineering Risk Management
1(14)
1.1 Introduction
1(5)
1.1.1 Boston's Central Artery/Tunnel Project
2(4)
1.2 Objectives and Practices
6(6)
1.3 New Challenges
12(3)
Questions and Exercises
13(2)
2 Perspectives on Theories of Systems and Risk
15(26)
2.1 Introduction
15(1)
2.2 General Systems Theory
15(9)
2.2.1 Complex Systems, Systems-of-Systems, and Enterprise Systems
20(4)
2.3 Risk and Decision Theory
24(12)
2.4 Engineering Risk Management
36(5)
Questions and Exercises
39(2)
3 Foundations of Risk and Decision Theory
41(84)
3.1 Introduction
41(1)
3.2 Elements of Probability Theory
41(22)
3.3 The Value Function
63(18)
3.4 Risk and Utility Functions
81(16)
3.4.1 vNM Utility Theory
81(4)
3.4.2 Utility Functions
85(12)
3.5 Multiattribute Utility---The Power Additive Utility Function
97(4)
3.5.1 The Power-Additive Utility Function
97(1)
3.5.2 Applying the Power-Additive Utility Function
98(3)
3.6 Applications to Engineering Risk Management
101(24)
3.6.1 Value Theory to Measure Risk
102(12)
3.6.2 Utility Theory to Compare Designs
114(5)
Questions and Exercises
119(6)
4 A Risk Analysis Framework in Engineering Enterprise Systems
125(32)
4.1 Introduction
125(1)
4.2 Perspectives on Engineering Enterprise Systems
125(4)
4.3 A Framework for Measuring Enterprise Capability Risk
129(4)
4.4 A Risk Analysis Algebra
133(16)
4.5 Information Needs for Portfolio Risk Analysis
149(1)
4.6 The "Cutting Edge"
150(7)
Questions and Exercises
151(6)
5 An Index to Measure Risk Corelationships
157(20)
5.1 Introduction
157(1)
5.2 RCR Postulates, Definitions, and Theory
158(6)
5.3 Computing the RCR Index
164(7)
5.4 Applying the RCR Index: A Resource Allocation Example
171(3)
5.5 Summary
174(3)
Questions and Exercises
174(3)
6 Functional Dependency Network Analysis
177(80)
6.1 Introduction
177(1)
6.2 FDNA Fundamentals
178(8)
6.3 Weakest Link Formulations
186(5)
6.4 FDNA (α, β) Weakest Link Rule
191(24)
6.5 Network Operability and Tolerance Analyses
215(22)
6.5.1 Critical Node Analysis and Degradation Index
222(5)
6.5.2 Degradation Tolerance Level
227(10)
6.6 Special Topics
237(10)
6.6.1 Operability Function Regulation
237(2)
6.6.2 Constituent Nodes
239(6)
6.6.3 Addressing Cycle Dependencies
245(2)
6.7 Summary
247(10)
Questions and Exercises
249(8)
7 A Decision-Theoretic Algorithm for Ranking Risk Criticality
257(14)
7.1 Introduction
257(1)
7.2 A Prioritization Algorithm
257(14)
7.2.1 Linear Additive Model
258(1)
7.2.2 Compromise Models
259(3)
7.2.3 Criteria Weights
262(3)
7.2.4 Illustration
265(4)
Questions and Exercises
269(2)
8 A Model for Measuring Risk in Engineering Enterprise Systems
271(10)
8.1 A Unifying Risk Analytic Framework and Process
271(8)
8.1.1 A Traditional Process with Nontraditional Methods
271(1)
8.1.2 A Model Formulation for Measuring Risk in Engineering Enterprise Systems
272(7)
8.2 Summary
279(2)
Questions and Exercises
279(2)
9 Random Processes and Queuing Theory
281(42)
9.1 Introduction
281(1)
9.2 Deterministic Process
282(2)
9.2.1 Mathematical Determinism
283(1)
9.2.2 Philosophical Determinism
284(1)
9.3 Random Process
284(14)
9.3.1 Concept of Uncertainty
286(1)
9.3.2 Uncertainty, Randomness, and Probability
287(2)
9.3.3 Causality and Uncertainty
289(2)
9.3.4 Necessary and Sufficient Causes
291(1)
9.3.5 Causalities and Risk Scenario Identification
291(2)
9.3.6 Probabilistic Causation
293(5)
9.4 Markov Process
298(2)
9.4.1 Birth and Death Process
300(1)
9.5 Queuing Theory
300(4)
9.5.1 Characteristic of Queuing Systems
302(1)
9.5.2 Poisson Process and Distribution
303(1)
9.5.3 Exponential Distribution
304(1)
9.6 Basic Queuing Models
304(6)
9.6.1 Single-Server Model
304(2)
9.6.2 Probability of an Empty Queuing System
306(1)
9.6.3 Probability That There Are Exactly N Entities Inside the Queuing System
307(1)
9.6.4 Mean Number of Entities in the Queuing System
308(1)
9.6.5 Mean Number of Waiting Entities
308(1)
9.6.6 Average Latency Time of Entities
308(1)
9.6.7 Average Time of an Entity Waiting to Be Served
309(1)
9.7 Applications to Engineering Systems
310(5)
9.8 Summary
315(8)
Questions and Exercises
316(7)
10 Extreme Event Theory
323(34)
10.1 Introduction to Extreme and Rare Events
323(1)
10.2 Extreme and Rare Events and Engineering Systems
324(1)
10.3 Traditional Data Analysis
325(2)
10.4 Extreme Value Analysis
327(2)
10.5 Extreme Event Probability Distributions
329(5)
10.5.1 Independent Single-Order Statistic
331(3)
10.6 Limit Distributions
334(2)
10.7 Determining Domain of Attraction Using Inverse Function
336(5)
10.8 Determining Domain of Attraction Using Graphical Method
341(6)
10.8.1 Steps in Visual Analysis of Empirical Data
341(4)
10.8.2 Estimating Parameters of GEVD
345(2)
10.9 Complex Systems and Extreme and Rare Events
347(4)
10.9.1 Extreme and Rare Events in a Complex System
348(1)
10.9.2 Complexity and Causality
349(1)
10.9.3 Complexity and Correlation
349(1)
10.9.4 Final Words on Causation
350(1)
10.10 Summary
351(6)
Questions and Exercises
351(6)
11 Prioritization Systems in Highly Networked Environments
357(22)
11.1 Introduction
357(1)
11.2 Priority Systems
357(6)
11.2.1 PS Notation
358(5)
11.3 Types of Priority Systems
363(12)
11.3.1 Static Priority Systems
363(2)
11.3.2 Dynamic Priority Systems
365(1)
11.3.3 State-Dependent DPS
365(6)
11.3.4 Time-Dependent DPS
371(4)
11.4 Summary
375(4)
Questions and Exercises
375(1)
Questions
376(3)
12 Risks of Extreme Events in Complex Queuing Systems
379(36)
12.1 Introduction
379(1)
12.2 Risk of Extreme Latency
379(7)
12.2.1 Methodology for Measurement of Risk
381(5)
12.3 Conditions for Unbounded Latency
386(3)
12.3.1 Saturated PS
388(1)
12.4 Conditions for Bounded Latency
389(6)
12.4.1 Bounded Latency Times in Saturated Static PS
389(3)
12.4.2 Bounded Latency Times in a Saturated SDPS
392(2)
12.4.3 Combinations of Gumbel Types
394(1)
12.5 Derived Performance Measures
395(8)
12.5.1 Tolerance Level for Risk
395(2)
12.5.2 Degree of Deficit
397(1)
12.5.3 Relative Risks
398(2)
12.5.4 Differentiation Tolerance Level
400(1)
12.5.5 Cost Functions
401(2)
12.6 Optimization of PS
403(7)
12.6.1 Cost Function Minimization
404(1)
12.6.2 Bounds on Waiting Line
404(2)
12.6.3 Pessimistic and Optimistic Decisions in Extremes
406(4)
12.7 Summary
410(5)
Questions and Exercises
411(4)
Appendix Bernoulli Utility and the St. Petersburg Paradox
415(6)
A.1.1 The St. Petersburg Paradox
415(2)
A.1.2 Use Expected Utility, Not Expected Value
417(4)
Questions and Exercises
419(2)
References 421(8)
Index 429
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. Pintos 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. Garveys research interests include the theory and application of risk-decision analytic methods to operations research problems in the system sciences domains.
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