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An artificial neural network meta-model for constrained simulation optimization

Mohammad Nezhad, A ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1057/jors.2013.73
  3. Abstract:
  4. This paper presents artificial neural network (ANN) meta-models for expensive continuous simulation optimization (SO) with stochastic constraints. These meta-models are used within a sequential experimental design to approximate the objective function and the stochastic constraints. To capture the non-linear nature of the ANN, the SO problem is iteratively approximated via non-linear programming problems whose (near) optimal solutions obtain estimates of the global optima. Following the optimization step, a cutting plane-relaxation scheme is invoked to drop uninformative estimates of the global optima from the experimental design. This approximation is iterated until a terminating condition is met. To study the robustness and efficiency of the proposed algorithm, a realistic inventory model is used; the results are compared with those of the OptQuest optimization package. These numerical results indicate that the proposed meta-model-based algorithm performs quite competitively while requiring slightly fewer simulation observations
  5. Keywords:
  6. Expensive simulation optimization ; Meta-model-based algorithm ; Iterative methods ; Neural networks ; Nonlinear programming ; Stochastic systems ; ANN ; Constrained simulations ; Nonlinear programming problem ; Optimization packages ; Sequential experimental design ; Simulation optimization ; SNOPT solver ; Stochastic constraints ; Mathematical models
  7. Source: Journal of the Operational Research Society ; Vol. 65, issue. 8 , August , 2014 , pp. 1232-1244 ; ISSN: 01605682
  8. URL: http://www.palgrave-journals.com/jors/journal/v65/n8/full/jors201373a.html