A quantile-based approach to system selection

Batur, Demet; Choobineh, F.

doi:10.1016/j.ejor.2009.05.039

Cited by 35 publications

(25 citation statements)

References 23 publications

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“…Instead of minimizing the mean under a variance constraint, we may minimize a specific quantile of the simulation output (see [4,5,37]), or minimize the Conditional Value at Risk (CVaR) (see [17,19,28] and [50]); the mean-variance trade-off is also criticized by [65]. Besides CVaR, other risk measures are surveyed by [58], including the "expected shortfall at level p", which is popular in the actuarial literature.…”

Section: Conclusion and Future Researchmentioning

confidence: 98%

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: Conclusion and Future Researchmentioning

confidence: 98%

Metamodel-Based Robust Simulation-Optimization: An Overview

Dellino

Kleijnen

Meloni

2015

Uncertainty Management in Simulation-Optimization of Complex Systems

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“…Other behaviors, such as downside risk and upside risk, are ignored. According to Batur and Choobineh (2010), the downside risk involves "the probability of obtaining outcomes smaller than a target value, while the upside risk involves the probability of obtaining outcomes larger than a target value". For many practical problems, however, the downside or upside risks can be more important than means.…”

Section: A N U S C R I P T 1 Introductionmentioning

confidence: 99%

A direct search method for unconstrained quantile-based simulation optimization

Chang

2015

European Journal of Operational Research

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“…Instead of minimizing the mean under a variance constraint, we may minimize a specific quantile of the simulation output (see Batur and Choobineh 2010, Bekki et al 2009, Kleijnen et al 2011 or minimize the conditional value at risk (CVaR) (see Chen et al 2009, Dehlendorff et al 2010, García-González et al 2007, Natarajan et al 2009); the mean-variance tradeoff is also criticized by Yin et al (2009). Besides CVaR, other risk measures are surveyed by Sordo (2009), including the "expected shortfall at level p," which is popular in the actuarial literature.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Robust Optimization in Simulation: Taguchi and Krige Combined

2009

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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 quantile-based approach to system selection

Cited by 35 publications

References 23 publications

Metamodel-Based Robust Simulation-Optimization: An Overview

Metamodel-Based Robust Simulation-Optimization: An Overview

A direct search method for unconstrained quantile-based simulation optimization

Robust Optimization in Simulation: Taguchi and Krige Combined

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