Simulation based optimization of stochastic systems with integer design variables by sequential multipoint linear approximation

  • Authors:

  • S. J. Abspoel;L. F. P. Etman;J. Vervoort;R. A. Van Rooij;A. J. G. Schoofs;J. E. Rooda

  • Affiliations:

  • Systems, Dynamics, and Control Engineering, Department of Mechanical Engineering, Eindhoven University of Technology, Wh. 4.105, P.O. Box 513, NL-5600 MB Eindhoven, The Netherlands e-mail: l.f.p.e ...;Systems, Dynamics, and Control Engineering, Department of Mechanical Engineering, Eindhoven University of Technology, Wh. 4.105, P.O. Box 513, NL-5600 MB Eindhoven, The Netherlands e-mail: l.f.p.e ...;Systems, Dynamics, and Control Engineering, Department of Mechanical Engineering, Eindhoven University of Technology, Wh. 4.105, P.O. Box 513, NL-5600 MB Eindhoven, The Netherlands e-mail: l.f.p.e ...;Systems, Dynamics, and Control Engineering, Department of Mechanical Engineering, Eindhoven University of Technology, Wh. 4.105, P.O. Box 513, NL-5600 MB Eindhoven, The Netherlands e-mail: l.f.p.e ...;Systems, Dynamics, and Control Engineering, Department of Mechanical Engineering, Eindhoven University of Technology, Wh. 4.105, P.O. Box 513, NL-5600 MB Eindhoven, The Netherlands e-mail: l.f.p.e ...;Systems, Dynamics, and Control Engineering, Department of Mechanical Engineering, Eindhoven University of Technology, Wh. 4.105, P.O. Box 513, NL-5600 MB Eindhoven, The Netherlands e-mail: l.f.p.e ...

  • Venue:
  • Structural and Multidisciplinary Optimization
  • Year:
  • 2001

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Abstract

Optimization problems are considered for which objective function and constraints are defined as expected values of stochastic functions that can only be evaluated at integer design variable levels via a computationally expensive computer simulation. Design sensitivities are assumed not to be available. An optimization approach is proposed based on a sequence of linear approximate optimization subproblems. Within each search subregion a linear approximate optimization subproblem is built using response surface model building. To this end, N simulation experiments are carried out in the search subregion according to a D-optimal experimental design. The linear approximate optimization problem is solved by integer linear programming using corrected constraint bounds to account for any uncertainty due to the stochasticity. Each approximate optimum is evaluated on the basis of M simulation replications with respect to objective function change and feasibility of the design. The performance of the optimization approach and the influence of parameters N and M is illustrated via two analytical test problems. A third example shows the application to a production flow line simulation model.