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Mavris, Dimitri N.

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Author: DeLaurentis, Daniel A. × Author: Hale, Mark A. ×

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    Capturing Corporate Philosophy: The Future of IT
    (Georgia Institute of Technology, 2000-02) Hale, Mark A. ; Daberkow, Debora Daniela ; DeLaurentis, Daniel A. ; Mavris, Dimitri N. ; Schrage, Daniel P. ; Craig, James I.
    Context is proposed as a mechanism for organizing Information Technology practices in the future through its role in interpretation. An enterprise organization model based on decision-flow is presented here that is applicable to a variety of domains. It contains elements that mark the information content with respect to a full consideration of its environment. These elements are, in order of increasing superiority, data, information, knowledge, judgement, and philosophy. There are four marked stages where contextual derivation occurs among these elements, including definition, refinement, improvement, and realization. Discovery occurs during the derivation of context and it is at this time that higher-level processes influence subordinate processes. For this reason, it is believed that corporate philosophy can be infused explicitly throughout enterprise practices. The resulting organizational model can be used by an enterprise to strategically allocate resources and maintain competitive advantage.
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    Elements of an Emerging Virtual Stochastic life Cycle Design Environment
    (Georgia Institute of Technology, 1999-10) Mavris, Dimitri N. ; DeLaurentis, Daniel A. ; Hale, Mark A. ; Tai, Jimmy C. M.
    The challenge of designing next-generation systems that meet goals for system effectiveness, environmental compatibility, and cost has grown to the point that traditional design methodologies are becoming ineffective. Increases in the analysis complexity required, the number of objectives and constraints to be evaluated, and the multitude of uncertainties in today? design problems are primary drivers of this situation. A new environment for design has been formulated to treat this situation. It is viewed as a testbed, in which new techniques in such areas as design-oriented/physics-based analysis, uncertainty modeling, technology forecasting, system synthesis, and decision-making can be posed as hypotheses. Several recent advances in elements of this multidisciplinary environment, termed the Virtual Stochastic Life Cycle Design Environment, are summarized in this paper.
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    A Stochastic Approach to Multi-disciplinary Aircraft Analysis and Design
    (Georgia Institute of Technology, 1998-01) Mavris, Dimitri N. ; DeLaurentis, Daniel A. ; Bandte, Oliver ; Hale, Mark A.
    Within the context of multi-disciplinary aircraft analysis and design, a new approach has been formulated and described which allows for the rapid technical feasibility and economic viability assessment of multi- attribute, multi-constrained designs. The approach, referred to here as Virtual Stochastic Life Cycle Design, facilitates the multi-disciplinary consideration of a system, accounting for life-cycle issues in a stochastic fashion. The life-cycle consideration is deemed essential in order to evaluate the emerging, all encompassing system objective of affordability. The stochastic treatment is employed to account for the knowledge variation/uncertainty that occurs in time through the various phases of design. Variability found in the treatment of assumptions, ambiguous requirements, code fidelity (imprecision), economic uncertainty, and technological risk are all examples of categories of uncertainty that the proposed probabilistic approach can assess. For cases where the problem is over-constrained and a feasible solution is not possible, the proposed method facilitates the identification and provides guidance in the determination of potential barriers which will have to be overcome via the infusion of new technologies. The specific task of examining system feasibility and viability is encapsulated and outlined in a series of easy to follow steps. Finally, the method concludes with a brief description and discussion of proposed decision making techniques to achieve optimal designs with reduced variability. This decision making is achieved through a combined utility theory and Robust Design Simulation approach.