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Equality Constraints in Multiobjective Robust Design Optimization: Decision Making Problem

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  • S. Rangavajhala

    (Rensselaer Polytechnic Institute)

  • A. A. Mullur

    (Rensselaer Polytechnic Institute)

  • A. Messac

    (Rensselaer Polytechnic Institute)

Abstract

Robust design optimization (RDO) problems can generally be formulated by incorporating uncertainty into the corresponding deterministic problems. In this context, a careful formulation of deterministic equality constraints into the robust domain is necessary to avoid infeasible designs under uncertain conditions. The challenge of formulating equality constraints is compounded in multiobjective RDO problems. Modeling the tradeoffs between the mean of the performance and the variation of the performance for each design objective in a multiobjective RDO problem is itself a complex task. A judicious formulation of equality constraints adds to this complexity because additional tradeoffs are introduced between constraint satisfaction under uncertainty and multiobjective performance. Equality constraints under uncertainty in multiobjective problems can therefore pose a complicated decision making problem. In this paper, we provide a new problem formulation that can be used as an effective multiobjective decision making tool, with emphasis on equality constraints. We present two numerical examples to illustrate our theoretical developments.

Suggested Citation

  • S. Rangavajhala & A. A. Mullur & A. Messac, 2009. "Equality Constraints in Multiobjective Robust Design Optimization: Decision Making Problem," Journal of Optimization Theory and Applications, Springer, vol. 140(2), pages 315-337, February.
  • Handle: RePEc:spr:joptap:v:140:y:2009:i:2:d:10.1007_s10957-008-9442-y
    DOI: 10.1007/s10957-008-9442-y
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    References listed on IDEAS

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    1. ,, 2004. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 20(2), pages 427-429, April.
    2. Caballero, Rafael & Cerda, Emilio & del Mar Munoz, Maria & Rey, Lourdes, 2004. "Stochastic approach versus multiobjective approach for obtaining efficient solutions in stochastic multiobjective programming problems," European Journal of Operational Research, Elsevier, vol. 158(3), pages 633-648, November.
    3. R. Caballero & E. Cerdá & M. M. Muñoz & L. Rey & I. M. Stancu-Minasian, 2001. "Efficient Solution Concepts and Their Relations in Stochastic Multiobjective Programming," Journal of Optimization Theory and Applications, Springer, vol. 110(1), pages 53-74, July.
    4. ,, 2004. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 20(1), pages 223-229, February.
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    Cited by:

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