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Role of Decision Analysis in MBSE

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Handbook of Model-Based Systems Engineering

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Abstract

This chapter examines the use of model-based systems engineering (MBSE) tools to perform trade-off analysis of alternative systems decisions throughout the system life cycle. Specially, we seek integrated models that automate the simultaneous evaluation of the performance, effectiveness, stakeholder value, and cost of multiple alternative system designs. We used Web of Science to perform a structured literature search to identify papers that describe the use of MBSE tools to support automated analysis of alternatives and trade-off analyses. While we found no papers that use the terms decision science and MBSE. We did find papers that used decision analysis and MBSE. We also found very few papers that claimed to use MBSE to provide analysis of design alternatives or tradespace exploration. Based on the literature search insights, we identified and described the required and desired capabilities to perform automated trade-off analyses of performance, effectiveness, stakeholder value, and cost for multiple system design alternatives using integrated models. We provide an illustrative case study of an unmanned aerial vehicle (UAV) design trade-off analysis that uses a model-based engineering tool, ModelCenter® Integrate, to develop an integrated modeling tool in order to simultaneously evaluate the performance, effectiveness, and stakeholder value of UAV designs. We performed eight iterations of tool development with increasing fidelity models. Iteration 1 identifies 13 Pareto optimal alternatives. However, by the end of iteration 8, only four of these designs remain feasible, though not all remain as Pareto optimal. By delaying major design decisions and using higher-fidelity models, we are able to prevent the selection of a suboptimal and potentially infeasible design. By integrating decision analysis and life cycle cost models with physics models and simulations, we are able to take advantage of the benefits of model-based engineering practices to support system decision-making.

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Author information

Authors and Affiliations

  1. Department of Industrial Engineering, University of Arkansas, Fayetteville, AR, USA

    Gregory S. Parnell & Edward A. Pohl

  2. System Design and Analytics Laboratory, Department of Industrial Engineering, University of Arkansas, Fayetteville, AR, USA

    Nicholas J. Shallcross, Eric A. Specking & Matt Phillips

Authors
  1. Gregory S. Parnell
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  2. Nicholas J. Shallcross
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  3. Eric A. Specking
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  4. Edward A. Pohl
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  5. Matt Phillips
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Corresponding author

Correspondence to Gregory S. Parnell .

Editor information

Editors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    Azad M. Madni

  2. Lockheed Martin (United States), Bethesda, MD, USA

    Norman Augustine

  3. University of Southern California, Los Angelies, CA, USA

    Michael Sievers

Section Editor information

  1. Systems Architecting and Engineering, University of Southern California, Los Angeles, CA, USA

    Azad M. Madni

  2. University of Southern California, Los Angeles, CA, USA

    Michael W. Sievers

Appendix: List of Acronyms and Their Meaning

Appendix: List of Acronyms and Their Meaning

AC: Air-conditioning

BDD: Block Definition Diagram

CSMOP: Constraint Satisfaction Multicriteria Optimization Problem

GUI: Graphical User Interface

HPC: High-performance computing

ID: Influence diagram

IMPRINT: Improved Performance Research Integration Tool

LCC: Life cycle cost

MATLAB: Matrix Laboratory

MBSE: Model-based systems engineering

MODA: Multiple objective decision analysis

OOSEM: Object-Oriented System Engineering Method

RAM: Reliability, Availability, and Maintainability

SBD: Set-based design

SML: System Modeling Language

SysML: Systems Modeling Language

UAV: Unmanned aerial vehicle

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Parnell, G.S., Shallcross, N.J., Specking, E.A., Pohl, E.A., Phillips, M. (2022). Role of Decision Analysis in MBSE. In: Madni, A.M., Augustine, N., Sievers, M. (eds) Handbook of Model-Based Systems Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-27486-3_14-1

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  • DOI: https://doi.org/10.1007/978-3-030-27486-3_14-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27486-3

  • Online ISBN: 978-3-030-27486-3

  • eBook Packages: Springer Reference Intelligent Technologies and RoboticsReference Module Computer Science and Engineering

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