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Mathematical programming and the optimization of computer simulations

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Engineering Optimization

Part of the book series: Mathematical Programming Studies ((MATHPROGRAMM,volume 11))

Abstract

Mathematical programming techniques can be combined with response surface experimental design methods to optimize simulated systems. A computer simulation model has controllable input variables x i , i=1,…, n and yields responses η j , j=1,…, m. A simulation trial at a particular set of values x k i , i=1,…n produces an estimate y ki for the system response η j . This paper describes several formulations of the so-called “simulation/optimization” problem, including constrained optimization and multiple-objective optimization. It also describes several procedures for obtaining a solution to this problem, including a direct search technique, a first-order response surface method, and a second-order response surface approach. Each of these techniques combines simulation, response surface methodology, and mathematical programming.

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Author information

Authors and Affiliations

  1. The Pennsylvania State University, University Park, PA, USA

    William E. Biles

  2. Air Products and Chemicals, Inc., Allentown, PA, USA

    James J. Swain

Authors
  1. William E. Biles
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  2. James J. Swain
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Editor information

M. Avriel R. S. Dembo

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© 1979 The mathematical programming society

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Biles, W.E., Swain, J.J. (1979). Mathematical programming and the optimization of computer simulations. In: Avriel, M., Dembo, R.S. (eds) Engineering Optimization. Mathematical Programming Studies, vol 11. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0120864

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  • DOI: https://doi.org/10.1007/BFb0120864

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00799-6

  • Online ISBN: 978-3-642-00800-9

  • eBook Packages: Springer Book Archive

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