Metric Optimization-Based Design of Systems Subject to Hostile Disruptions

In many applications, engineering systems are required to operate acceptably well in hostile environments. In the past, survivability engineering has addressed this requirement using heuristic rule-based design approaches followed by analysis to determine if survivability constraints have been satisfied. The treatment of survivability as a constraint rather than an independent design objective hinders the ability of system engineers to trade off survivability with other design objectives, such as cost and performance. Herein, the survivability problem is posed in terms of maximizing expected performance and minimizing the risk of unacceptable performance. Design metrics that allow optimal selection of systems on the basis of these survivability dimensions are presented. The metrics are part of a systematic approach to system engineering in which survivability concerns are quantified and individual systems and entire classes of systems can be compared objectively. These metrics are a necessary step toward an integrated design process wherein tradeoffs between all design objectives can be identified. This methodology is demonstrated on the design of a notional electric warship integrated engineering plant (IEP) that is subject to hostile disruptions posed by antiship missiles. By use of this method, the performance of the IEP is shown to be improved.

[1]  Greg A. Jamieson,et al.  Object Worlds in Work Domain Analysis: A Model of Naval Damage Control , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[2]  H. Markowitz,et al.  Mean-Variance versus Direct Utility Maximization , 1984 .

[3]  Daniel E. Hastings,et al.  Defining Survivability for Engineering Systems , 2007 .

[4]  Philippe Artzner,et al.  Coherent Measures of Risk , 1999 .

[5]  Daniel E. Hastings,et al.  A Framework for Incorporating "ilities" in Tradespace Studies , 2007 .

[6]  John B. Kidd,et al.  Decisions with Multiple Objectives—Preferences and Value Tradeoffs , 1977 .

[7]  Scott D. Sudhoff,et al.  Performance Metrics for Electric Warship Integrated Engineering Plant Battle Damage Response , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[8]  Robert E. Ball,et al.  The Fundamentals of Aircraft Combat Survivability: Analysis and Design, 2nd Edition , 2003 .

[9]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[10]  Phhilippe Jorion Value at Risk: The New Benchmark for Managing Financial Risk , 2000 .

[11]  S.D. Sudhoff,et al.  Naval combat survivability testbeds for investigation of issues in shipboard power electronics based power and propulsion systems , 2002, IEEE Power Engineering Society Summer Meeting,.

[12]  Alan Brown,et al.  Risk Metric for Multi‐Objective Design of Naval Ships , 2004 .

[13]  Daniel E. Hastings,et al.  Systems Architecting for Survivability: Limitations of Existing Methods for Aerospace Systems , 2008 .

[14]  Charles N. Calvano,et al.  Systems engineering in an age of complexity , 2004 .

[15]  Ciwei Gao,et al.  Risk Assessment of Malicious Attacks Against Power Systems , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[16]  Scott D. Sudhoff,et al.  Evolutionary Algorithms for Minimax Problems in Robust Design , 2009, IEEE Transactions on Evolutionary Computation.

[17]  D.H. Rhodes,et al.  Empirical Validation of Design Principles for Survivable System Architecture , 2008, 2008 2nd Annual IEEE Systems Conference.

[18]  Aaron M. Cramer,et al.  Modeling and Simulation of an Electric Warship Integrated Engineering Plant , 2006 .

[19]  T. M. Hopkins,et al.  Operationally Oriented Vulnerability Requirements in the Ship Design Process , 1998 .

[20]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[21]  R. L. Keeney,et al.  Decisions with Multiple Objectives: Preferences and Value Trade-Offs , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[22]  P. Kumaraswamy A generalized probability density function for double-bounded random processes , 1980 .

[23]  Richard K. Belew,et al.  New Methods for Competitive Coevolution , 1997, Evolutionary Computation.

[24]  D.H. Rhodes,et al.  Design Principles for Survivable System Architecture , 2007, 2007 1st Annual IEEE Systems Conference.

[25]  Alan Brown,et al.  Multiple‐Objective Optimization in Naval Ship Design , 2003 .

[26]  Robert E. Ball,et al.  The fundamentals of aircraft combat survivability analysis and design , 1985 .

[27]  Vishal Mahulkar,et al.  System-of-Systems Modeling and Simulation of a Ship Environment With Wireless and Intelligent Maintenance Technologies , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[28]  Dimitri N. Mavris,et al.  Methodology for Assessing Survivability Tradeoffs in the Preliminary Design Process , 2000 .

[29]  Daniel E. Hastings,et al.  Metrics for Evaluating Survivability in Dynamic Multi-Attribute Tradespace Exploration , 2008 .