A Highly-Optimized Tolerance (HOT)-Inspired Model of the Large Scale Systems Engineering Process

Abstract

Large-scale systems engineering efforts involving multiple stakeholders often have been problematic, and there has been recent interest in understanding how to improve the systems engineering process. This paper presents an approach to modeling the systems engineering process, with possible extensions to systems investment and systems operations, inspired by the highly optimized tolerance (HOT) framework for understanding complexity in designed systems. HOT is complementary to agent-based modeling (ABM) in the sense that it emphasizes the centrally planned aspect of designed systems with tradeoffs and uncertainty, rather than distributed decision making based on local knowledge and goals. To begin the exploration of models of the systems engineering process, a temporal model is presented with stakeholder interactions modeled as random events. Following the HOT approach, planning behavior is framed as stochastic optimization, which is reduced to a open-loop control problem. The initial results suggest promise for the HOT-inspired framework in helping to understand how to improve the systems engineering process, but more exploratory work is needed, including work on relating actual systems engineering experience to the models

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
AD1132216

Entities

People

  • Leonard A. Wojcik

Organizations

  • MITRE Corporation

Tags

Communities of Interest

  • Air Platforms
  • Biomedical
  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Adaptive Systems
  • Air Traffic
  • Air Transportation
  • Aircrafts
  • Avionics
  • Command And Control
  • Complex Systems
  • Corporations
  • Data Links
  • Differential Equations
  • Engineering
  • Equations
  • Game Theory
  • Infrastructure
  • Military Operations
  • Models
  • Network Architecture
  • New England
  • Probability
  • Probability Distributions
  • Random Variables
  • Systems Engineering
  • Transportation
  • Uncertainty
  • United States
  • Websites

Fields of Study

  • Computer science
  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Software Engineering.
  • Theoretical Analysis.