Advanced Methods


Mission Statement

The ASDL Advanced Methods Division aims to educate highly qualified professionals and to advance the state-of-the-art of systems engineering and related methodologies to address current and future challenges.


The Advanced Methods Division leads research to develop state-of-the-art design and systems engineering methodologies. These methods aim to leverage cutting-edge technologies to push the envelope and advance the ability to aid engineering design, analysis, trade-studies, and decision making at all levels, from the system of systems level to the technology and component level. This division researches and formulates methodologies, which are developed from a fundamental research level to full implementation on a broad range of real-world applications for sponsored research. In addition, the Advanced Systems Engineering Division maintains a database of existing methods and tools both internal and external to ASDL. Close collaboration with the other divisions is a driving philosophy in all research activities. This facilitates the ability to identify cross-division needs and broadens the implementation of new methods and techniques.


Dr. Elena Garcia, Division Chief

Dr. Elena Garcia
Strategic Planning, Operations Research, and Technology Analysis Branch Lead
Project Manager for Lockheed Martin Logistics and Air Force Research Lab Cargo UAS
Supporting RE on Joint Forces Command

Dr. Elena Garcia
System of Systems Engineering Branch Lead
Project Manager for ARCHITECT
Leading the ARCHITECT and ROSETTA initiatives
Supporting RE on Joint Forces Command, GT MENTOR, and UAS in the NAS


Strategic Planning, Operations Research, and Technology Analysis

  • Logistics

Logistics and a related field, operations research, focuses more on "how" rather than "what" assets are used, their sequence of actions, scheduling, queuing, interactions with other entities and tracks the behavior of the network, supply chain, or distribution system.

  • Discrete-Event Simulation

Discrete-Event Simulation (DES) is one method to create and execute complex models in order to analyze the behavior of a system or process. Instead of more traditional uniform iteration time steps, DES evaluates the various states and responses through a sequence of events, which can accelerate execution time and analyses.


Multi-Attribute and Multi-Objective Decision Making is typically used for product or design selection and the synthesis of design objectives, respectively. Both describe different perspectives on Multi-Criteria Decision Making where trades are performed across the large number of dimensions to identify or select the "best" design or solution.

  • Probabilistic Design

As opposed to deterministic design, probabilistic design assumes that inputs and responses are uncertain and can be represented with distributions. This uncertainty due to noise, external factors or lack of knowledge about the design is quantified using probabilistic methods such as Monte Carlo simulations.

  • Risk and Uncertainty

Understanding the range that design variables can assume (uncertainty) and the impact of those various combinations of values (risk) is a fundamental engineering activity. Developing methods to evaluate the likelihood of certain events and reduce their negative effects on the design or outcomes is a key objective in uncertainty and risk analysis.

  • Human Decision Making

Human decision making addresses the biases, prejudices and preferences of humans when making decisions. From an engineering perspective, it seeks to quantify and predict how humans will respond in concept selection activities or design amidst various external conditions or stimuli.

  • Technology Assessment, Selection and Forecasting

Identifying and assessing the impact of new technologies is an essential step before investment in any portfolio of technologies. Various methods have been developed and are currently researched to improve technology forecasting and selection to aid decision makers.

  • Quality Engineering

Quality engineering is field devoted to conceptual design and analysis where little or no knowledge of the design space is available. Often subject matter experts are employed to gather information and data about the concepts where historical data is lacking or non-existent. A commonly used method using quality engineering principles is Quality Function Deployment (QFD).

  • Economic Analysis

As resources become rare and design expectations or requirements increase, the affordability and cost-benefit analyses are crucial to identify feasibility for any technology development program or design effort.

  • Game Theory

Game Theory can be applied to a variety of problems where the actions or decisions of one individual or decision maker can influence the decisions of others. As a result, the responses or payoffs to each stakeholder in a particular game or situations are highly interdependent, but proper structuring of multi-agent games can assist with analyzing expected outcomes.

  • Real options

In order to hedge or protect against uncertainty, such as fluctuations in future fuel prices, organizations can use options as part of their particular business strategy. In a similar fashion, real options can be applied to technology development to quantify the impact and feasibility of future technology and identify designs with potential to maximize value.

  • Safety and Reliability Analysis

Safety and Reliability analysis focuses on minimizes the hazards to human life due to component or system failures and from insufficient planning, operations or processes. Efforts to improve designs such as increasing mean-time-to-failures, or to identify and mitigate the hazards of critical failure points can increase the reliability of a given system.

  • Cost management

With economic constraints becoming more stringent and requirements more demanding, appropriate scheduling and fiscal planning throughout the entire life cycle becomes an essential activity for any successful technology development program.

System of Systems Engineering



System of Systems Modeling and Simulation
This research area includes researching the application of different modeling and simulation and mathematical modeling techniques to system of systems problems, as well as understanding how fundamental modeling concepts need to be adapted to be applicable to systems of systems modeling.

  • Techniques
    • Agent-based Modeling/Constructive Simulation
    • Discrete Event Modeling
    • System Dynamics Modeling
    • Applied Graph Theory
    • Markov Chains
  • Fundamental Concepts
    • Computational Challenges
    • Stochastic Modeling and Surrogate Modeling
    • Dynamical Modeling and Surrogate Modeling
    • Managing Uncertainty in the System of Systems

Architecture-based Engineering
This research area aims to advance architecture-based engineering concepts. Research will include application of techniques from software and system architecting to the system of systems problem, as well as taking existing system of systems architecting techniques to the next level. The application of enterprise architecting to the business processes of the system of systems will also be an area of research.

  • Model-based Systems Engineering
  • System of Systems Architecting
  • Enterprise Architecting

Understanding and Modeling Complexity
The role of complexity in system of systems is a hot research topic in many fields, and this research is particularly applicable to system of systems.

The Human Role in the System of Systems Because humans play a large factor in the overall performance and the uncertainty in a SoS, understanding and modeling the human element can be a driving factor that needs to be included in the design and analysis process. There is a strong need for research in this area, and the SoSE branch is exploring ways to capture the impact of humans as SoS elements.

Systems Design, Modeling, and Visualization

Design is a process that maps a set of requirements to a set of functions, leading to the exploration of a vast space of possibilities and eventually to the formulation of a series of decisions that contribute to the final description of a solution. This problem-solving process during which data, information, and knowledge are exchanged to foster the formulation of appropriate conclusions and actions is characterized by several contextual (lack of physical data, sensitivity to requirements, dimensionality, uncertainty, data overload, etc.) and human challenges (communication, collaboration, etc.).

The Systems Design, Modeling, and Visualization branch aims at fostering the development and implementation of new and creative ideas through cross-fertilization to support the formulation, development and implementation of new advanced design methods able to address the industry present and future challenges.

The main research areas of the Systems Design, Modeling, and Visualization branch include methods pertaining to:

Design Problem Definition, and Concept Space Definition and Exploration

  • Requirements exploration, prioritization and analysis (QFD, GOTChA, affinity diagram, etc.)
  • Analysis of alternatives (combinatorial methods, morphological analysis, etc.)
  • Concept space exploration (genetic algorithms, etc.)

Modeling and Simulation

  • Physics-based modeling and analysis
  • Surrogate modeling (response surface methodology, artificial neural network, Kriging, Gaussian models, etc.)
  • Robust design
  • Probabilistic design
  • Discrete event simulation
  • System dynamics

Design Space Exploration

  • Design Space exploration techniques: fast probability integration, inverse design (filtered Monte Carlo), Monte Carlo simulation
  • Visualization-enabled design space exploration: visualization methods for multidimensional data sets, data fusion, data farming

System Optimization

  • Optimization algorithms, Multidisciplinary Design, Analysis (MDA) and Optimization (MDO)

Data and Visual Analytics

  • Data and text mining, data farming, data fusion, visual analytics, etc.

In particular, the Systems Design, Modeling, and Visualization branch will leverage the rapid advancement of information, infrastructural and communication technologies to:

  • Support the seamless integration of design systems, methods and tools,
  • Support collaborative design efforts within the design process,
  • Facilitate data and knowledge creation and sharing across multiple disciplines and geographical locations,
  • Facilitate information storage, versioning, retrieval and mining,
  • Support the development of environments that integrate and leverage computational, interaction and visualization techniques to support analytical reasoning and data fusion, and eventually reduce the designer’s cognitive burden,
  • Synthesize enabling techniques into a suite of application-based methods,
  • Develop an integrated knowledge-based engineering and management framework

The function of the Systems Design, Modeling, and Visualization branch within the Aerospace Systems Design Laboratory (ASDL) consists in:

  • Supporting design efforts across the different divisions of ASDL,
  • Educating and empowering the next generation of Aerospace system engineers and technologists for immediate deployment in academia, industry, and government,
  • Providing leadership and innovation in Advanced System Engineering


Publications and Reports

Book Chapters

  • D. N. Mavris and O. J. Pinon, A Systems Engineering Approach to Aircraft Design, in Encyclopedia of Aerospace Engineering, eds R. Blockley and W. Shyy, John Wiley: Chichester, DOI:10.1002/9780470686652.eae597, Published 15th June 2012
  • Griendling, Kelly. Chapter 11: DoDAF and MODAF Artifacts in Architecture and Principles of Systems Engineering Book Authored by Dr. Dimitri Mavris and Dr. Charles Dickerson, Auerbach Publications, 2010

Journal Papers

  • O. J. Pinon, E. Garcia and D. N. Mavris, A Strategic Assessment of Airport Technology Investments, Submitted to Transportation Research Part C: Emerging Technologies
  • O. J. Pinon, D. N. Mavris and E. Garcia, Harmonizing European and American Aviation Modernization Efforts through Visual Analytics, Journal of Aircraft, Vol. 48, No.5., September-October 2011
  • Justin, Cedric; Briceno, Simon; Mavris, Dimitri; and Villeneuve, Frederic; "A Competitive Market Approach to Gas Turbine Technology Portfolio Selection" ASME Turbo Expo 2011; Vancouver, Canada; June, 2011
  • Cheung, Julie; Scanlan, James; Wong, James; Forrester, Jennifer; Eres, Hakki; Collopy, Paul; Hollingsworth, Peter; Wiseall, Steve; and Briceno, Simon; "Application of Value-Driven Design to Commercial Aero-Engine Systems" Submitted to the AIAA Journal of Aircraft, 2010
  • Mavris, D. N. and Briceno, S. I., "Implementation of a physics based decision making framework for evaluation of the multidisciplinary aircraft uncertainty", World Aviation Congress; Montreal, Canada; 2003, SAE Transactions.

Conference Papers

  • S. I. Briceno, O. J. Pinon, B. Laughlin and D. N. Mavris, Addressing Integration Challenges in the Design of Complex Aerospace Systems, to be presented at the NAFEMS World Congress (NWC), Salzburg, Austria, June 9-12, 2013
  • O. J. Pinon, E. Garcia and D. N. Mavris, Evaluating Flexibility in Airport Capacity-Enhancing Technology Investments, 28th Congress of International Council of the Aeronautical Sciences (ICAS), Brisbane, Australia, September 23-28, 2012
  • D. N. Mavris and O. J. Pinon, Chapter 1: An Overview of Design Challenges and Methods in Aerospace Engineering, Invited Paper in Complex Systems Design & Management, Omar Hammami, Daniel Krob and Jean-Luc Voirin (Eds), Springer, January 2012
  • Ellis, Michael R.; Mize, Christopher L.; Venkatram, Saumya; Pinon, Olivia J.; Briceno, Simon I; and Mavris, Dimitri. Development of a Public Registry for the Future Evaluation of NextGen Technology Transfer Benefits, Submitted to the 11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, Virginia Beach, Virginia, September 20-22, 2011
  • Pinon, Olivia J.; Mavris, Dimitri and Garcia, Elena. A System Dynamics Approach to the Evaluation of Airport Technology Portfolios, Submitted to the 11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, Virginia Beach, Virginia, September 20-22, 2011
  • Justin, Cedric Y.; Briceno, Simon I; and Mavris, Dimitri. Strategic Analysis of Competitive Markets: A Case Study for the Narrowbody Market, Submitted to the 11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, Virginia Beach, Virginia, September 20-22, 2011
  • Mavris, Dimitri and Griendling, Kelly. The Relational Oriented Systems Engineering Technology Tradeoff (ROSETTA) Environment, Invited Paper, Submitted to 6th Annual System of Systems Engineering Conference. June, 2011. Albuquerque, New Mexico.
  • Mavris, Dimitri; Pinon, Olivia J. and Garcia, Elena. Modélisation et Visualization pour l’Aide à la Décision, Invited Paper, Submitted to Les Entretiens de Toulouse – Rencontres Aérospatiales, Toulouse, France, May 3-4, 2011
  • Griendling, Kelly; Duncan, Scott and Mavris, Dimitri. Combining Policy with Technology Research and Development to Enable Wind Energy Proliferation in the United States, 20th International Conference for Management of Technology. April, 2011. Miami Beach, FL
  • Domercant, Jean and Mavris Dimitri. Measuring the Architectural Complexity of Military Systems-of-Systems, 2011 IEEE Aerospace Conference, Big Sky, MT
  • Griendling, Kelly and Mavris, Dimitri. Development of a DoDAF-based Executable Architecting Approach to Analyze System-of-Systems Alternatives, 2011 IEEE Aerospace Conference, Big Sky, MT
  • Salmon, John; Iwata, Curtis and Mavris, Dimitri. “Comparative Assessment and Decision Support System for Strategic Military Airlift Capability”, MODSIM World 2010 Conference & Expo, Virginia Beach, VA.
  • Ellis, Michael R.; Pinon, Olivia J.; Briceno, Simon I; Mavris, Dimitri and Gawdiak, Yuri. Identification and Evaluation of Technology Transfer Benefits: Definition of a Registry in Support of the Strategic Development of NextGen Architectures, AIAA 2010-9248, 10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, Fort Worth, Texas, September 13-15, 2010
  • Mavris, Dimitri; Pinon, Olivia J. and Fullmer, David. Systems Design and Modeling: A Visual Analytics Approach, Invited Paper, 27th Congress of International Council of the Aeronautical Sciences (ICAS), Nice, France, September 19-24, 2010
  • Pinon, Olivia J.; Mavris, Dimitri and Garcia, Elena. Development of an Options-Based Approach to the Selection of Adaptable and Airport Capacity-Enhancing Technology Portfolios, 27th Congress of International Council of the Aeronautical Sciences (ICAS), Nice, France, September 19-24, 2010
  • Griendling, Kelly and Mavris, Dimitri. An Architecture-based Approach to Identifying System-of-Systems Alternatives, 5th Annual IEEE SoSE Conference, Loughborough, UK, June 22-24 2010
  • Bagdatli, Burak; Griendling, Kelly and Mavris, Dimitri. A Method for Examining the Impact of Interoperability on Mission Performance in a System-of Systems, 2010 IEEE Aerospace Conference, Big Sky, MT
  • Griendling, Kelly and Mavris, Dimitri. A Process for System of Systems Architecting, 2010 AIAA Aerospace Sciences Meeting, Orlando, FL
  • Pinon, Olivia J.; Mavris, Dimitri and Garcia, Elena. A Visual Analytics Approach to the Qualitative Comparison of the SESAR and NextGen Efforts, AIAA 2009-6902, 9th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, Hilton Head, South Carolina, September 2009
  • Salmon, John; Soban, Danielle and Mavris, Dimitri. “Development of a Visual Analytics Tool Suite to Support Strategic Decision Making”, AIAA-2009-0625, 47th AIAA ASM 2009 Conference, Orlando, FL
  • Pinon, Olivia J.; Mavris, Dimitri and Garcia, Elena. A Methodological Approach for Airport Technology Evaluation and Selection, AIAA-2008-8965, 26th Congress of International Council of the Aeronautical Sciences (ICAS), Anchorage, Alaska, September 14-19, 2008
  • Griendling, Kelly; Balestrini, Santiago and Mavris, Dimitri. DoDAF-based System Architecture Selection using a Comprehensive Modeling Process and Multi-Criteria Decision Making, AIAA MDAO Conference, Victoria, Canada, Sept 10-12, 2008
  • Griendling, Kelly. A Framework to Aid in Decision-Making for DoDAF-based Acquisition and Design Georgia Institute of Technology AE 8900 Special Topics Report White Paper, Presented to Dr. Dimitri Mavris, School of Aerospace Engineering, April 28, 2008
  • Briceno, S. I. and Mavris, D. N., ‘Applications of Game Theory in a Systems Design Approach to Strategic Engine Selection’, 25th International Congress of the Aeronautical Sciences; Hamburg, Germany; 5th Sept. 2006.
  • Mavris, D. N. and Briceno, S. I., ‘Strategic decision-making: Applications of game theory in a systems approach to commercial engine selection’, AIAA 5th Aviation, Technology, Integration, and Operations Conference (ATIO); Arlington, VA; USA; 26-28 Sept. 2005 pp. 1–10.
  • Mavris, D. N. and Briceno, S. I., ‘Implementation of a physics based decision making framework for evaluation of the multidisciplinary aircraft uncertainty’, World Aviation Congress; Montreal, Canada; 2003.
  • Dimitri N. Mavris, Simon I. Briceno, Ismael Fernandez, ‘Development of a Strategic Business Decision-Making Environment for Commercial Jet Engine Selection’, 41st Aerospace Sciences Meeting and Exhibit; Reno, NV; 6-9 Jan. 2003.
  • Simon I. Briceno and Dimitri N. Mavris,, ‘Quiet Supersonic Jet Engine Performance Tradeoff Analysis Using a Response Surface Methodology Approach’, SAE World Aviation Congress; Phoenix, NV; Nov. 2002.
  • Simon I. Briceno, Michael A. Buonanno, Ismael Fernandez and Dimitri N. Mavris,, ‘A Parametric Exploration of Supersonic Business Jet Concepts Utilizing Response Surfaces’, AIAA’s Aircraft Technology, Integration, and Operations; Los Angeles, CA; 1-3 Oct. 2002.