MBSE and SysML

Model based systems engineering (MBSE) extends traditional systems engineering methods in order to efficiently create systems that are agile in the face of dynamically changing environmental conditions and requirements. MBSE is a collection of processes, methods and tools that enable capability-based system design and development. In MBSE, the activities supporting the system engineering process are accomplished iteratively through development of increasingly detailed models. These models are utilized as operational prototypes to support the search for alternatives, design refinement and validation, system integration and verification, and system operation.

SysML is a visual modeling language that allows industry standard communication among systems lifecycle development activities. It is based on UML 2.0 and provides additional set of modeling diagrams for hardware, software, data, procedures and other system components.

RIMES focuses on abstract modeling of the user domain as well as modeling methodologies, model libraries and ontology creation for analysis of complex systems including system-of-systems. RIMES incorporates SysML and/or UML into their model-based systems analysis and methodologies with the objective of building integrated models that capture cognitive, information and social aspects of complex systems. Some of the modeling methodologies of interest to RIMES are multi-agent models, intelligent cognitive architectures, Colored Petri Nets, and discrete event models. RIMES also focuses on the development of systems engineering processes through model based analysis. Process areas of interest include process as different from product, process efficiency, cycle-shortening, integrated process sensing & control, evolution, and drift correction. MBSE area supports other RIMES research areas including architecture analysis, service oriented architecture applications, risk and reliability modeling.

  • Complex System Modeling, Testing and Re-Engineering with Complementary Methods for System Dynamics,Bharath Dantu and Eric D. Smith, Complex System Modeling, Testing and Re-Engineering with Complementary Methods for System Dynamics, Test & Evaluation of Systems of Systems Conference, International Test & Evaluation Association (ITEA), El Paso, TX, 2012.

  • Assessment of DoDAF as an Architectural Framework Evolving toward Syntactic and Semantic Completeness,Francisco Chagolla and Eric D. Smith, Assessment of DoDAF as an Architectural Framework Evolving toward Syntactic and Semantic Completeness, Test & Evaluation of Systems of Systems Conference, International Test & Evaluation Association (ITEA), El Paso, TX, 2012.

  • Enterprise Transformation through Aspects and Levels: Zachman Bayesian Approach,Ramakanth Gona and Eric D. Smith, Enterprise Transformation through Aspects and Levels: Zachman Bayesian Approach, Complex Adaptive Systems Conference, Chicago, IL, 2011.

  • Zachman Framework Consolidation of Quality Measures Across the Testing & Evaluation Enterprise,Ramakanth Gona and Eric D. Smith, Zachman Framework Consolidation of Quality Measures Across the Testing & Evaluation Enterprise, Live Virtual Constructive (LVC) Conference, International Test & Evaluation Association (ITEA), El Paso, TX, 2011.

  • Architectural Framework & Modeling Innovations for Attribute Based Testing & Evaluation,Eric D. Smith, Ricardo Pineda and Justin Kieser, Architectural Framework & Modeling Innovations for Attribute Based Testing & Evaluation, Live Virtual Constructive (LVC) Conference, International Test & Evaluation Association (ITEA), El Paso, TX, 2010.

  • Dagli C. and Kilicay-Ergin N., 2009, “Chapter 4: System of Systems Architecting” in System of Systems: Innovations for the 21st Century, ed. Jamshidi M., Wiley & Sons Inc.

  • Complementarity in Systems Architecting, Eric D. Smith, Conference on Systems Engineering Research (CSER), 2008, Los Angeles, CA.

  • Valid Models Require Defined Levels, A. Terry Bahill, Ferenc Szidarovszky, Rick Botta, Eric D. Smith, International Journal of General Systems, 37(5), 2008.

  • What Are Levels? A. Terry Bahill, Ferenc Szidarovszky, Rick Botta, Eric D. Smith, Proceedings of the International Council on Systems and Industrial, Manufacturing & Systems Engineering (INCOSE) 15th International Symposium, 2005, Rochester, NY.

  • Quantum Mechanical Principles of Emergence, Eric D. Smith and Neale R. Smith, Artificial Neural Networks in Engineering (ANNIE), 2007, St. Louis, MO.

Trade-off Studies

Tradeoff studies are broadly recognized and mandated as the method for simultaneously considering multiple alternatives with many criteria, and as such are recommended in the Capability Maturity Model Integration (CMMI) Decision Analysis and Resolution (DAR) process. Tradeoff studies can be studied extensively through mathematics, or, in contrast, by value engineering. Tradeoff studies, which involve human numerical judgment, calibration, and data updating, are often approached with under confidence by analysts and are often distrusted by decision makers. The decision-making fields of Judgment and Decision Making, Cognitive Science and Experimental Economics have built up a large body of research on human biases and errors in considering numerical and criteria-based choices. Relationships between experiments in these fields and the elements of tradeoff studies show that tradeoff studies are susceptible to human mental mistakes

RIMES focuses on development of objective methods and techniques to eliminate the presence, or ameliorate the effects of mental mistakes on tradeoff studies.

  • Complementary Decompositions for Systems within Systems, Hugo Almaraz, Ricardo Pineda and Eric D. Smith, Complementary Decompositions for Systems within Systems, Live Virtual Constructive (LVC) Conference, International Test & Evaluation Association (ITEA), El Paso, TX, 2011.

  • Clarity, Variety, Allocation, Synergy and Emergence in the Formulation of Tradeoff Studies, Eric D. Smith and Ricardo Pineda, Clarity, Variety, Allocation, Synergy and Emergence in the Formulation of Tradeoff Studies, Conference on Systems Engineering Research (CSER), Stevens Institute of Technology and the University of Southern California, Hoboken, NJ, 2010.

  • Sensitivity Analysis: A Powerful System Validation Technique, Eric D. Smith, Ferenc Szidarovszky, William J. Karnavas, and Terry Bahill, The Open Cybernetics & Systemics Journal, Bentham Open, Sensitivity Analysis: A Powerful System Validation Technique, 2008, Vol. 2, 39-56.

  • Ameliorating Mistakes in Tradeoff Studies, Eric D. Smith, Young Jun Son, Massimo Piattelli-Palmarini and A. Terry Bahill, Systems Engineering, 10(3), 222-240, 2007.

  • Cognitive Biases Affecting the Acceptance of Tradeoff Studies, Eric D. Smith, Massimo Piattelli-Palmarini, and A. Terry Bahill, in Decision Modeling and Behavior in Uncertain and Complex Environments, J. C. Smith and T. Kugler editors, Springer, Cambridge, MA, 2007.

  • Tradeoff Studies and Cognitive Biases, Eric D. Smith, A. Terry Bahill, Proceedings of the International Council on Systems and Industrial, Manufacturing & Systems Engineering (INCOSE) 16th International Symposium, 2006, Orlando, FL.

  • Attribute Substitution in Systems Engineering, Eric D. Smith, A. Terry Bahill, Proceedings of the 2007 Industrial, Manufacturing & Systems Engineering Research Conference, G. Bayraksan, W. Lin, Y. Son, and R. Wysk, eds.

  • Taboada, H. & Coit, D. (2006). Data Mining Techniques to Facilitate the Analysis of the Pareto-Optimal Set for Multiple Objective Problems. In Proceedings of the Industrial, Manufacturing & Systems Engineering Research Conference (IERC), Orlando, Florida, May 2006.

  • Taboada, H. & Coit, D. (2008). Multiple Objective Scheduling Problems: Determination of Pruned Pareto Sets. IIE Transactions, 40(5):552-564.

Reliability

Reliability analysis in systems engineering involves the utilization of many engineering techniques to predict and quantify the probability that the system or components will perform its intended function during a specified period of time under stated conditions. Reliability-based design encompasses processes, tools and practices for designing reliability into systems or products.

RIMES focuses on identifying and or developing formal approaches for reliability analysis in the system-of-systems context (SoS). A significant challenge of reliability-based design within a SoS is determining the appropriate mix of components to meet a set of needs or provide a set of services. To address this problem, RIMES is interested in the development of methods for the optimal SoS allocation of resources/components. Moreover, when analyzing complex systems, not surprisingly, several objectives must be considered. Because the constituent systems are capable of independent operation, they may collaborate or compete. Therefore, RIMES focuses on developing new and practical approaches for solving and analyzing SoS optimization problems considering multiple objective functions, and the development of analytical frameworks for incorporating both data and expert judgments for projecting SoS reliability. RIMES takes into consideration reliability-based design under uncertainty and the development of methods to address SoS resiliency and vulnerability reduction.

Another area related to reliability analysis is replacement analysis which encompasses system upgrade factors. RIMES is interested in the development of algorithms to systematically and optimally upgrade SoS considering aging components and uncertainty including decisions about when to replace existing assets, when to add new assets, and when to upgrade assets with improved technologies.

  • Quantitatively Augmented QFD-HOQ, Vivek K. Jikar, Elizabeth A. Cudney, Eric D. Smith, Kenneth M. Ragsdell, Kioumars Paryani, SAE Asia Pacific Automotive Engineering Conference, 2007, Hollywood, CA, USA.

  • Taboada, H., Baheranwala, F., Coit, D. & Wattanapongsakorn, N. (2007). Practical Solutions for Multi-Objective Optimization: An Application to System Reliability Design Problems. Reliability Engineering & System Safety, 92(3):314-322. Ranked 1st in Top 25 Hottest Articles by ScienceDirect.com

  • Taboada, H. & Coit, D. (2007). Data Clustering of Solutions for Multiple Objective System Reliability Optimization Problems. Quality Technology & Quantitative Management Journal, 4(2):35-54.

  • Taboada, H., Espiritu, J. & Coit, D. (2008). MOMS-GA: A Multiobjective Multi-State Genetic Algorithm for System Reliability Optimization Design Problems. IEEE Transactions on Reliability, 57(1):182-191.

  • Taboada, H., Espiritu, J. & Coit, D. (2008). Design Allocation of Multi-State Series-Parallel Systems for Power Systems Planning: A Multiple Objective Evolutionary Approach. Journal of Risk and Reliability (in print).

  • Espiritu, J. & Coit, W. (2006). Customized Component Reliability Importance Measures for Electricity Transmission Systems. In Proceedings of the Industrial, Manufacturing & Systems Engineering Research Conference (IERC). Orlando, FLA.

  • Espiritu, J., Coit, W., Prakash, U. & Ramirez-Marquez, J. (2005). Reliability Modeling of Electricity Transmission Systems: An Extension of Traditional Reliability Methods. In Proceedings of the Industrial, Manufacturing & Systems Engineering Research Conference (IERC). Atlanta, GA.

  • Taboada, H., Baheranwala, F., Coit, D. & Wattanapongsakorn, N. (2005). Practical Solutions of Multi-objective System Reliability Design Problems using Genetic Algorithms. In Proceedings of the Fourth International Conference on Quality & Reliability (ICQR4), Beijing, China, August 2005.

  • Taboada, H. & Coit, D. (2007). Multiple Objective Design Allocation Problems: Development of New Evolutionary Algorithms. In Proceedings of the European Safety && Reliability Conference (ESREL), Stavanger, Norway, June 2007.

  • Taboada, H. & Coit, D. (2007). Recent Developed Evolutionary Algorithms for the Multi-Objective Optimization of Design Allocation Problems. In Proceedings of the 5th International Conference on Quality & Reliability (ICQR5), Chiang Mai, Thailand. November, 2007.

  • Taboada, H. & Coit, D. (2008). Development of a New Multiple Objective Prioritized Genetic Algorithm. In Proceedings of the Industrial, Manufacturing & Systems Engineering Research Conference (IERC), Vancouver, British Columbia, Canada. May, 2008.

  • Taboada, H. & Coit, D. (2008). Development of a Multiple Objective Genetic Algorithm for Solving Reliability Design Allocation Problems. In Proceedings of the Industrial, Manufacturing & Systems Engineering Research Conference (IERC), Vancouver, British Columbia, Canada. May, 2008.

Risk-analysis

Risk management is recognized by industry as the best way to consistently quantify risks, as part of a repeatable and quantifiable risk management process. However, risk management involves human subjective: numerical judgment, calibration, rounding, censoring, and data updating. Thus, risk management is often approached with under confidence and distrusted by decision makers.

Risk management is recognized by industry as the best way to consistently quantify risks, as part of a repeatable and quantifiable risk management process. However, risk management involves human subjective: numerical judgment, calibration, rounding, censoring, and data updating. Thus, risk management is often approached with under confidence and distrusted by decision makers.

  • Risk Analysis and Mitigation Tendencies as Interpreted by Cognitive Science, Eric D. Smith, Risk Analysis and Mitigation Tendencies as Interpreted by Cognitive Science, Test & Evaluation of Systems of Systems Conference, International Test & Evaluation Association (ITEA), El Paso, TX, 2012.

  • Risk Analysis, Eric D. Smith and A. Terry Bahill, Proceedings of the International Council on Systems and Industrial, Manufacturing & Systems Engineering (INCOSE) 17th International Symposium, 2007, San Diego, CA.

  • Cognitive Biases in the Risk Matrix, Eric D. Smith, William Siefert and David Drain, Systems Engineering, the journal of INCOSE, 12(4), 2009 (scheduled).

  • Coit, D., Espiritu, J., Ramirez-Marquez, J. & Prakash, U. (2004) Impact of Parameter Uncertainty on Asset Criticality and System Reliability. Funded project by industry.

  • Espiritu, J., Coit, D. & Prakash, U. (2007). Component Criticality Importance Measures for the Power Industry. Electric Power systems Research, 77(5-6). Electric Power Systems Research, 77(5-6).

  • Taboada, H., Espiritu, J. & Coit, D. (2008). Design allocation of multi-state series-parallel systems for power systems planning: A multiple objective evolutionary approach. Journal of Risk and Reliability (in print).

Systems Architecture

Architecture in systems engineering involves the application of art and science in the conceptual design of systems, considering the arrangements of components, and the relations among components. Balanced architectures with good performance measure values and holistic attributes are sought. There is a diversity of architecture frameworks for enabling architecture developments. The Department of Defense Architectural Framework (DoDAF) and the Zachman Framework, among others, are descriptive frameworks which capture different views of the system architecture. The systems architecting process becomes complicated as systems are becoming increasingly networked. There is a need for evolvable architectures that will satisfy changing needs and requirements.

RIMES focuses on development of architectural frameworks and methodologies to help design, test and evaluate evolvable system architectures. Top-level strategic design at the beginning of the life-cycle is stressed. Other aspects, such as essential decisions, rationales and behaviors are considered in the creation of good architectural representations and systems. Service oriented architectures where functionalities are grouped around business processes are becoming widely deployed as means of constructing evolvable systems architectures. RIMES focuses on application of services oriented architectures to system-of-systems context.

  • Complex System Modeling, Testing and Re-Engineering with Complementary Methods for System Dynamics,Bharath Dantu and Eric D. Smith, Complex System Modeling, Testing and Re-Engineering with Complementary Methods for System Dynamics, Test & Evaluation of Systems of Systems Conference, International Test & Evaluation Association (ITEA), El Paso, TX, 2012.

  • Assessment of DoDAF as an Architectural Framework Evolving toward Syntactic and Semantic Completeness,Francisco Chagolla and Eric D. Smith, Assessment of DoDAF as an Architectural Framework Evolving toward Syntactic and Semantic Completeness, Test & Evaluation of Systems of Systems Conference, International Test & Evaluation Association (ITEA), El Paso, TX, 2012.

  • Enterprise Transformation through Aspects and Levels: Zachman Bayesian Approach,Ramakanth Gona and Eric D. Smith, Enterprise Transformation through Aspects and Levels: Zachman Bayesian Approach, Complex Adaptive Systems Conference, Chicago, IL, 2011.

  • Zachman Framework Consolidation of Quality Measures Across the Testing & Evaluation Enterprise,Ramakanth Gona and Eric D. Smith, Zachman Framework Consolidation of Quality Measures Across the Testing & Evaluation Enterprise, Live Virtual Constructive (LVC) Conference, International Test & Evaluation Association (ITEA), El Paso, TX, 2011.

  • Architectural Framework & Modeling Innovations for Attribute Based Testing & Evaluation,Eric D. Smith, Ricardo Pineda and Justin Kieser, Architectural Framework & Modeling Innovations for Attribute Based Testing & Evaluation, Live Virtual Constructive (LVC) Conference, International Test & Evaluation Association (ITEA), El Paso, TX, 2010.

  • Dagli C. and Kilicay-Ergin N., 2009, “Chapter 4: System of Systems Architecting” in System of Systems: Innovations for the 21st Century, ed. Jamshidi M., Wiley & Sons Inc.

  • Complementarity in Systems Architecting, Eric D. Smith, Conference on Systems Engineering Research (CSER), 2008, Los Angeles, CA.

  • Valid Models Require Defined Levels, A. Terry Bahill, Ferenc Szidarovszky, Rick Botta, Eric D. Smith, International Journal of General Systems, 37(5), 2008.

  • What Are Levels? A. Terry Bahill, Ferenc Szidarovszky, Rick Botta, Eric D. Smith, Proceedings of the International Council on Systems and Industrial, Manufacturing & Systems Engineering (INCOSE) 15th International Symposium, 2005, Rochester, NY.

  • Quantum Mechanical Principles of Emergence, Eric D. Smith and Neale R. Smith, Artificial Neural Networks in Engineering (ANNIE), 2007, St. Louis, MO.