Showing 1 - 10 of 10
Optimization 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...
Persistent link: https://www.econbiz.de/10013155383
Optimization of simulated systems is tackled by many methods, but most methods assume known environments. This article, however, develops a 'robust' methodology for uncertain environments. This methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by...
Persistent link: https://www.econbiz.de/10012723330
Parametric production frontier functions are frequently used in stochastic frontier models, but there do not seem to be any empirical test statistics for its plausibility. To bridge the gap in the literature, we develop two test statistics based on local smoothing and an empirical process,...
Persistent link: https://www.econbiz.de/10012944869
Parametric production frontier functions are frequently used in stochastic frontier models, but there do not seem to be any empirical test statistics for its plausibility. To bridge the gap in the literature, we develop two test statistics based on local smoothing and an empirical process,...
Persistent link: https://www.econbiz.de/10011739112
This article uses a sequentialized experimental design to select simulation input combinations for global optimization, based on Kriging (also called Gaussian process or spatial correlation modeling); this Kriging is used to analyze the input/output data of the simulation model (computer code)....
Persistent link: https://www.econbiz.de/10014185812
This paper studies simulation-based optimization with multiple outputs. It assumes that the simulation model has one random objective function and must satisfy given constraints on the other random outputs. It presents a statistical procedure for testing whether a specific input combination...
Persistent link: https://www.econbiz.de/10014049484
This paper derives a novel procedure for testing the Karush-Kuhn-Tucker (KKT) first-order optimality conditions in models with multiple random responses.Such models arise in simulation-based optimization with multivariate outputs. This paper focuses on expensive simulations, which have small...
Persistent link: https://www.econbiz.de/10014062609
Kriging is a popular method for estimating the global optimum of a simulated system. Kriging approximates the input/output function of the simulation model. Kriging also estimates the variances of the predictions of outputs for input combinations not yet simulated. These predictions and their...
Persistent link: https://www.econbiz.de/10014038647
Distribution-free bootstrapping of the replicated responses of a given discreteevent simulation model gives bootstrapped Kriging (Gaussian process) metamodels; we require these metamodels to be either convex or monotonic. To illustrate monotonic Kriging, we use an M/M/1 queueing simulation with...
Persistent link: https://www.econbiz.de/10014166285
This paper uses a sequentialized experimental design to select simulation input combinations for global optimization, based on Kriging (also called Gaussian process or spatial correlation modeling); this Kriging is used to analyze the input/output data of the simulation model (computer code)....
Persistent link: https://www.econbiz.de/10013141684