This contribution summarizes a methodology for simulation optimization assuming some simulation inputs are uncertain. This methodology integrates Taguchi’s worldview (distinguishing between decision and environmental inputs), metamodeling (either Response Surface Methodology or Kriging), and mathematical programming. Instead of Taguchi’s statistical designs, this contribution uses Latin Hypercube Sampling for the environmental inputs. Mathematical programming is used to estimate the decision inputs that minimize the mean output, subject to a threshold for the standard deviation of the simulation output. Changing that threshold gives the estimated Pareto frontier. Confidence regions for the Pareto-optimal solution based on that frontier can be estimated through bootstrapping. This methodology is illustrated through Economic Order Quantity (EOQ) simulations.

Robust Simulation-Optimization Methodology / Kleijnen, J. P. C.; G., Dellino; Meloni, C. - (2009), pp. 48-52. ((Intervento presentato al convegno INFORMS Simulation Society Research Workshop Simulation: At the Interface of Modeling and Analysis tenutosi a Coventry, UK.

Robust Simulation-Optimization Methodology

Meloni C
2009

Abstract

This contribution summarizes a methodology for simulation optimization assuming some simulation inputs are uncertain. This methodology integrates Taguchi’s worldview (distinguishing between decision and environmental inputs), metamodeling (either Response Surface Methodology or Kriging), and mathematical programming. Instead of Taguchi’s statistical designs, this contribution uses Latin Hypercube Sampling for the environmental inputs. Mathematical programming is used to estimate the decision inputs that minimize the mean output, subject to a threshold for the standard deviation of the simulation output. Changing that threshold gives the estimated Pareto frontier. Confidence regions for the Pareto-optimal solution based on that frontier can be estimated through bootstrapping. This methodology is illustrated through Economic Order Quantity (EOQ) simulations.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1583409
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