IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T14:12:14ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2012-11-30T08:34:15Z2012-11-30T08:34:15Zhttp://eprints.imtlucca.it/id/eprint/1441This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/14412012-11-30T08:34:15ZSimulation-optimization under uncertainty through metamodeling and bootstrappingMost methods in simulation-optimization assume known environments, whereas this research accounts for uncertain environments combining Taguchi's world view with either regression or Kriging (Gaussian Process) metamodels (response surfaces). These metamodels are combined with Non-Linear Mathematical Programming (NLMP) to find a robust optimal solution. Varying the constraint values in the NLMP model gives an estimated Pareto frontier. To account for the variability of the estimated Pareto frontier, this research uses bootstrapping which gives confidence regions for the robust optimal solution. This methodology is illustrated through the Economic Order Quantity (EOQ) inventory-management model, accounting for the uncertainties in the demand rate and the cost coefficients.Gabriella Dellinogabriella.dellino@imtlucca.itJack P.C. KleijnenCarlo Meloni2012-11-29T16:39:01Z2012-11-29T16:39:01Zhttp://eprints.imtlucca.it/id/eprint/1436This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/14362012-11-29T16:39:01ZRobust optimization in simulation: Taguchi and Krige combinedOptimization 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. Gabriella Dellinogabriella.dellino@imtlucca.itJack P.C. KleijnenCarlo Meloni2011-08-01T13:49:28Z2011-08-08T08:40:22Zhttp://eprints.imtlucca.it/id/eprint/757This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/7572011-08-01T13:49:28ZRobust simulation-optimization methodologyThis 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.Jack P.C. KleijnenGabriella Dellinogabriella.dellino@imtlucca.itCarlo Meloni2011-08-01T13:42:54Z2011-10-07T08:21:48Zhttp://eprints.imtlucca.it/id/eprint/756This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/7562011-08-01T13:42:54ZRobust simulation-optimization using metamodelsOptimization of simulated systems is the goal of many methods, but most methods assume known environments. In this paper we present a methodology that does account for uncertain environments. Our methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by either Response Surface Methodology or Kriging metamodeling. We illustrate the resulting methodology through the well-known Economic Order Quantity (EOQ) modelGabriella Dellinogabriella.dellino@imtlucca.itJack P.C. KleijnenCarlo Meloni2011-08-01T12:52:38Z2011-08-04T07:30:21Zhttp://eprints.imtlucca.it/id/eprint/753This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/7532011-08-01T12:52:38ZParametric and distribution-free bootstrapping in robust simulation-optimizationMost methods in simulation-optimization assume known environments, whereas this research accounts for uncertain environments combining Taguchi's world view with either regression or Kriging (also called Gaussian Process) metamodels (emulators, response surfaces, surrogates). These metamodels are combined with Non-Linear Mathematical Programming (NLMP) to find robust solutions. Varying the constraint values in this NLMP gives an estimated Pareto frontier. To account for the variability of this estimated Pareto frontier, this contribution considers different bootstrap methods to obtain confidence regions for a given solution. This methodology is illustrated through some case studies selected from the literature.Gabriella Dellinogabriella.dellino@imtlucca.itJack P.C. KleijnenCarlo Meloni2011-08-01T10:23:29Z2011-08-04T07:30:21Zhttp://eprints.imtlucca.it/id/eprint/746This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/7462011-08-01T10:23:29ZRobust optimization in simulation: Taguchi and response surface methodologyOptimization 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 Response Surface Methodology (RSM). George Box originated RSM, and Douglas Montgomery recently extended RSM to robust optimization of real (non-simulated) systems. We combine Taguchi's view with RSM for simulated systems. We illustrate the resulting methodology through classic Economic Order Quantity (EOQ) inventory models, which demonstrate that robust optimization may require order quantities that differ from the classic EOQ.Gabriella Dellinogabriella.dellino@imtlucca.itJack P.C. KleijnenCarlo Meloni