A Global Robust Optimization Using Kriging Based Approximation Model

Lee, Kwon-Hee; Park, Gyung-Jin

doi:10.1299/jsmec.49.779

Cited by 72 publications

(54 citation statements)

References 19 publications

(19 reference statements)

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“…Lee and Park [40] present a methodology-based on Kriging metamodelsto tackle robust optimization in deterministic simulation-based systems. They use simulated annealing to solve the optimization problem.…”

Section: Extensions To Taguchi's Methodsmentioning

confidence: 99%

Metamodel-Based Robust Simulation-Optimization: An Overview

Dellino

Kleijnen

Meloni

2015

Uncertainty Management in Simulation-Optimization of Complex Systems

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The simulation-optimization process aims to identify the setting of input parameters leading to optimal system performance, evaluated through a simulation model of the system itself. The factors involved in the simulation model are often noisy and cannot be controlled or varied during the decision process, due to measurement errors or other implementation issues; moreover, some factors are determined by the environment, rather than by managers or decision makers. Therefore, the presumed optimal solution may turn out to be sub-optimal or even infeasible. Robust optimization tackles problems affected by uncertainty, providing solutions that are in some sense insensitive to perturbations in the model parameters.G. Dellino ( ) Istituto per le Applicazioni del Calcolo "Mauro Picone",

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Section: Extensions To Taguchi's Methodsmentioning

confidence: 99%

Metamodel-Based Robust Simulation-Optimization: An Overview

Dellino

Kleijnen

Meloni

2015

Uncertainty Management in Simulation-Optimization of Complex Systems

View full text Add to dashboard Cite

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“…Simpson [13] applied the method to the design of the space shuttle, and compared it with the calculation accuracy and efficiency of the response surface. Lee [14] used the Kriging surrogate model to optimize the design of the cylindrical member crashing problem. Gao [15] used the Kriging model in order to reduce the warping of injection molding process components in order to optimize the design.…”

Section: Construction Of the Kriging Surrogate Modelmentioning

confidence: 99%

Optimization Design of Coupling Beam Metal Damper in Shear Wall Structures

Zhang

et al. 2017

Applied Sciences

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Abstract:The coupling beam damper is a fundamental energy dissipation component in coupling shear wall structures that directly influences the performance of the shear wall. Here, we proposed a two-fold design method that can give better energy dissipation performance and hysteretic behavior to coupling beam dampers. First, we devised four in-plane yielding coupling beam dampers that have different opening types but the same amount of total materials. Then the geometry parameters of each opening type were optimized to yield the maximum hysteretic energy. The search for the optimal parameter set was realized by implementing the Kriging surrogate model which iterates randomly selected input shape parameters and the corresponding hysteretic energy calculated by the infinite element method. By comparing the maximum hysteretic energy in all four opening types, one type that had the highest hysteresis energy was selected as the optimized design. This optimized damper has the advantages of having a simple geometry and a high dissipation energy performance. The proposed method also provided a new framework for the design of in-plane coupling beam dampers.

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“…Inspired by Lee and Park (2006), we fit a single Kriging metamodel to a relatively small number, say, n, of combinations of the decision variables d and the environmental variables e. Next, we use this metamodel to compute the Kriging predictions for the simulation output w for N n combinations of d and e accounting for the distribution of e.…”

Section: Two Kriging Approaches To Robustmentioning

confidence: 99%

Robust Optimization in Simulation: Taguchi and Krige Combined

Dellino

Kleijnen

Meloni

2012

INFORMS Journal on Computing

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O ptimization of simulated systems is the goal of many methods, but most methods assume known environments. We, however, develop a "robust" methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain world but replaces his statistical techniques by design and analysis of simulation experiments based on Kriging (Gaussian process model); moreover, we use bootstrapping to quantify the variability in the estimated Kriging metamodels. In addition, we combine Kriging with nonlinear programming, and we estimate the Pareto frontier. We illustrate the resulting methodology through economic order quantity (EOQ) inventory models. Our results suggest that robust optimization requires order quantities that differ from the classic EOQ. We also compare our results with results we previously obtained using response surface methodology instead of Kriging.

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A Global Robust Optimization Using Kriging Based Approximation Model

Cited by 72 publications

References 19 publications

Metamodel-Based Robust Simulation-Optimization: An Overview

Metamodel-Based Robust Simulation-Optimization: An Overview

Optimization Design of Coupling Beam Metal Damper in Shear Wall Structures

Robust Optimization in Simulation: Taguchi and Krige Combined

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