eprintid: 757
rev_number: 10
eprint_status: archive
userid: 17
dir: disk0/00/00/07/57
datestamp: 2011-08-01 13:49:28
lastmod: 2011-08-08 08:40:22
status_changed: 2011-08-01 13:49:28
type: conference_item
metadata_visibility: show
item_issues_count: 0
creators_name: Kleijnen, Jack P.C.
creators_name: Dellino, Gabriella
creators_name: Meloni, Carlo
creators_id: 
creators_id: gabriella.dellino@imtlucca.it
creators_id: 
title: Robust simulation-optimization methodology
ispublished: pub
subjects: HA
subjects: QA
divisions: EIC
full_text_status: none
pres_type: paper
abstract: This 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.
date: 2009
event_title: INFORMS Simulation Society Research Workshop
event_type: workshop
refereed: TRUE
official_url: http://www.informs-sim.org/2009informs-simworkshop/paper50-54.pdf
citation:   Kleijnen, Jack P.C. and Dellino, Gabriella and Meloni, Carlo  Robust simulation-optimization methodology.  In: INFORMS Simulation Society Research Workshop         (2009)