Optimization of simulated systems is the goal of many techniques, but most of them assume known environments. Recently, “robust” methodologies accounting for uncertain environments have been developed. Robust optimization tackles problems affected by uncertainty, providing solutions that are in some sense insensitive to perturbations in the model parameters. Several alternative methods have been proposed for achieving robustness in simulation-based optimization problems, adopting different experimental designs and/or metamodeling techniques. This chapter reviews the current state of the art on robust optimization approaches based on simulated systems. First, we summarize robust Mathematical Programming. Then we discuss Taguchi’s approach introduced in the 1970s. Finally, we consider methods to tackle robustness using metamodels, and Kriging in particular. The proposed methodology uses Taguchi’s view of the uncertain world, but replaces his statistical techniques by Kriging. We illustrate the resulting methodology through basic inventory models.

Metamodel-Based Robust Simulation-Optimization: An Overview / Dellino, Gabriella; Kleijnen, Jack P. C.; Meloni, Carlo (OPERATIONS RESEARCH, COMPUTER SCIENCE). - In: Uncertainty Management in Simulation-Optimization of Complex Systems: Algorithms and Applications / [a cura di] Gabriella Dellino; Carlo Meloni. - STAMPA. - Boston, MA : Springer, 2015. - ISBN 978-1-4899-7546-1. - pp. 27-54 [10.1007/978-1-4899-7547-8_2]

Metamodel-Based Robust Simulation-Optimization: An Overview

Dellino, Gabriella;Meloni, Carlo
2015-01-01

Abstract

Optimization of simulated systems is the goal of many techniques, but most of them assume known environments. Recently, “robust” methodologies accounting for uncertain environments have been developed. Robust optimization tackles problems affected by uncertainty, providing solutions that are in some sense insensitive to perturbations in the model parameters. Several alternative methods have been proposed for achieving robustness in simulation-based optimization problems, adopting different experimental designs and/or metamodeling techniques. This chapter reviews the current state of the art on robust optimization approaches based on simulated systems. First, we summarize robust Mathematical Programming. Then we discuss Taguchi’s approach introduced in the 1970s. Finally, we consider methods to tackle robustness using metamodels, and Kriging in particular. The proposed methodology uses Taguchi’s view of the uncertain world, but replaces his statistical techniques by Kriging. We illustrate the resulting methodology through basic inventory models.
2015
Uncertainty Management in Simulation-Optimization of Complex Systems: Algorithms and Applications
978-1-4899-7546-1
Springer
Metamodel-Based Robust Simulation-Optimization: An Overview / Dellino, Gabriella; Kleijnen, Jack P. C.; Meloni, Carlo (OPERATIONS RESEARCH, COMPUTER SCIENCE). - In: Uncertainty Management in Simulation-Optimization of Complex Systems: Algorithms and Applications / [a cura di] Gabriella Dellino; Carlo Meloni. - STAMPA. - Boston, MA : Springer, 2015. - ISBN 978-1-4899-7546-1. - pp. 27-54 [10.1007/978-1-4899-7547-8_2]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/57865
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