everything.study
16.888

Multidisciplinary Design Optimization

3-1-8 units · Graduate

Systems modeling for design and optimization. Selection of design variables, objective functions and constraints. Overview of principles, methods and tools in multidisciplinary design optimization (MDO). Subsystem identification, development and interface design. Design of experiments (DOE). Review of linear (LP) and non-linear (NLP) constrained optimization formulations. Scalar versus vector optimization problems. Karush-Kuhn-Tucker (KKT) conditions of optimality, Lagrange multipliers, adjoints, gradient search methods, sensitivity analysis, geometric programming, simulated annealing, genetic algorithms and particle swarm optimization. Constraint satisfaction problems and isoperformance. Non-dominance and Pareto frontiers. Surrogate models and multifidelity optimization strategies. System design for value. Students execute a term project in small teams related to their area of interest.

Spring 2010 · Multidisciplinary System Design Optimization

Topics

  • Introduction to multidisciplinary system design optimization
  • Problem formulation
  • Modeling and simulation
  • Problem decomposition
  • Design space exploration
  • Visualization
  • Numerical optimization I
  • Numerical optimization II
  • Sensitivity analysis
  • Simulated annealing
  • Genetic algorithms I
  • Genetic algorithms II
  • Goal programming – isoperformance
  • Multi-objective optimization I
  • Multi-objective optimization II
  • Post-optimality analysis
  • Approximation methods
  • Robust design
  • Design for value
  • MDO at Boeing
  • Design for flexibility
  • Roots and future of MDO
  • Project presentations
  • Project presentations (cont.)
  • Course summary

Readings

  • Due to the fact that multidisciplinary system design optimization is a relatively young field, we know of no single textbook that would entirely capture all the material of this course and be suitable as a classroom text. However, we will use the following recommended textbook to support parts of the course:
    Listed reading
  • Papalambros, Panos Y., and Douglass J. Wilde. Principles of Optimal Design: Modeling and Computation . 2nd ed. Cambridge, UK: Cambridge University Press, 2000. ISBN: 9780521627276.
    Listed reading
  • Vanderplaats, Garret N. Numerical Optimization Techniques for Engineering Design . 3rd ed. Colorado Springs, CO: Vanderplaats Research and Development Inc., 2001. ISBN: 9780944956014.
    Listed reading
  • Steuer, Ralph E. Multiple Criteria Optimization: Theory, Computation and Application . New York, NY: John Wiley and Sons, Inc., 1986. ISBN: 9780471888468.
    Listed reading
  • Goldberg, David E. Genetic Algorithms in Search, Optimization and Machine Learning . Reading, MA: Addison-Wesley, 1989. ISBN: 9780201157673.
    Listed reading
  • Natalia M., Alexandrov, and M. Y., Hussaini, eds. Multidisciplinary Design Optimization: State of the Art. (Proceedings in Applied Mathematics Series; No. 80) . Philadelphia, PA: Society for Industrial & Applied Math, 1997. ISBN: 9780898713596.
    Listed reading
  • Owens, Lawrence J., Alvin J. Walsh, and Michael J. Fogel. Artificial Intelligence Through Simulated Evolution . New York, NY: John Wiley and Sons, Inc., 1966.
    Listed reading
  • Statnikov, Roman B., and Joseph B. Matusov. Multicriteria Optimization and Engineering . New York, NY: Springer-Verlag, 1995. ISBN: 9780412992315.
    Listed reading

Prerequisites & course information

Same subject as EM.428[J] , IDS.338[J] Prereq: 18.085 or permission of instructor Acad Year 2027-2028: G (Fall) Acad Year 2028-2029: Not offered 3-1-8 units

Sources