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A Stochastic Kriging Metamodel for Constrained Simulation Optimization Based on a k-Optimal Design

Abbaszadeh Peivasti, Hadi | 2014

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 45992 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Mahlooji, Hashem
  7. Abstract:
  8. In recent years, optimization via simulation for the systemswhose objective function has stochastic characteristic and doesn’t explicitly exist in closed form, has attracted considerable interest.Simulation of this kind of systems at times may be veryexpensive. In this research, the constraint simulation optimization problem is considered for solving problems with stochastic features based on metamodels. For this purpose, stochastic Kriging is used as a metamodel. In this method, first, a few feasible points in the solution space are identified by thek-optimal design of experiment and then the simulation runs are performed. In the next step, a metamodel is fitted to all the stochastic constraints and the objective function exclusively. Then, the Cross-Validation method is used for validating the metamodel. After the validation process, The metamodel is used for estimating the new points in the solutionspace. Finally, the optimum value of the system is obtained by using the Metaheuristic methods. The performance of the suggested method is evaluated via three examples taken from the literature, and the solutions are comprised with the solutions of the competing algorithms as well as the Ordinary Kriging method
  9. Keywords:
  10. Simulation Optimization ; Metamodel ; Experiments Design ; Stochastic Kriging Metamodel ; Cross-Validation

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