Abstract

A major challenge to the successful planning and evolution of an acknowledged System of Systems (SoS) is the current lack of understanding of the impact that the presence or absence of a set of constituent systems has on the overall SoS capability. Since the candidate elements of a SoS are fully functioning, stand-alone Systems in their own right, they have goals and objectives of their own to satisfy, some of which may compete with those of the overarching SoS. These system-level concerns drive decisions to participate (or not) in the SoS. Individual systems typically must be requested to join the SoS construct, and persuaded to interface and cooperate with other Systems to create the “new” capability of the proposed SoS. Current SoS evolution strategies lack a means for modeling the impact of decisions concerning participation or non-participation of any given set of systems on the overall capability of the SoS construct. Without this capability, it is difficult to optimize the SoS design.

The goal of this research is to model the evolution of the architecture of an acknowledged SoS that accounts for the ability and willingness of constituent systems to support the SoS capability development. Since DoD Systems of Systems (SoS) development efforts do not typically follow the normal program acquisition process described in DoDI 5000.02, the Wave Model proposed by Dahmann and Rebovich is used as the basis for this research on SoS capability evolution. The Wave Process Model provides a framework for an agent-based modeling methodology, which is used to abstract the nonutopian behavioral aspects of the constituent systems and their interactions with the SoS. In particular, the research focuses on the impact of individual system behavior on the SoS capability and architecture evolution processes.

A generic agent-based model (ABM) skeleton structure is developed to provide an Acknowledged SoS manager a decision making tool in negotiating of SOS architectures during the wave model cycles. The model provides an environment to plug in multiple SoS meta-architecture generation multiple criteria optimization models based on both gradient and non-gradient descent optimization procedures. Three types of individual system optimization models represent different behaviors of systems agents, namely; selfish, opportunistic and cooperative, are developed as plug in models. ABM has a plug in capability to incorporate domain-specific negotiation modes and a fuzzy associative memory (FAM) to evaluate candidate architectures for simulating SoS creation and evolution. The model evaluates the capability of the evolving SoS architecture with respect to four attributes: performance, affordability, flexibility and robustness.

In the second phase of the project, the team will continue with the development of an evolutionary strategies-based multi-objective mathematical model for creating an initial SoS meta architecture to start the negotiation at each wave. A basic generic structure will be defined for the fuzzy assessor math model that will be used to evaluate SoS meta architectures and domain dependent parameters pertaining to system of systems analysis and architecting through Agent Based Modeling. The work will be conducted in consideration of the national priorities, funding and threat assessment being provided by the environment developed for delivery at end of December 2013.

Department(s)

Engineering Management and Systems Engineering

Comments

This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Systems Engineering Research Center (SERC) under Contract H98230-08-D-0171. SERC is a federally funded University Affiliated Research Center managed by Stevens Institute of Technology

Report Number

SERC-2013-TR-021-2, SERC-2013-TR-021-3

Document Type

Technical Report

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2013 Stevens Institute of Technology, Systems Engineering Research Center, All rights reserved.

Publication Date

29 Mar 2013

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