Thumbnail Image
Item

Parametric and distribution-free bootstrapping in robust simulation-optimization

Dellino,G.
; 
Kleijnen,Jack P.C.
; 
Meloni,C.
Abstract
Most 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.
Description
Date
2010
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Collections
Tilburg University Research Output Collection
Show all metadata
Files
Thumbnail Image
WSC_2010_Dellino_Kleijnen_Meloni.pdf
Adobe PDF, 306.87 KB
Research Projects
Organizational Units
Journal Issue
Keywords
Citation
Dellino, G, Kleijnen, J P C & Meloni, C 2010, Parametric and distribution-free bootstrapping in robust simulation-optimization. in B Johansson, S Jain, J Montoya-Torres, J Hugan & E Yucesan (eds), Proceedings of the 2010 Winter Simulation Conference. IEEE, Piscataway, NJ, pp. 1283-1294.
URI
https://hdl.handle.net/20.500.14602/15875
License
info:eu-repo/semantics/restrictedAccess
Embedded videos