Continuous optimization via simulation using Golden Region search

dc.contributor.advisor Sigurdur Olafsson
dc.contributor.author Kabirian, Alireza
dc.contributor.department Industrial and Manufacturing Systems Engineering
dc.date 2018-08-11T07:37:54.000
dc.date.accessioned 2020-06-30T02:29:26Z
dc.date.available 2020-06-30T02:29:26Z
dc.date.copyright Thu Jan 01 00:00:00 UTC 2009
dc.date.embargo 2013-06-05
dc.date.issued 2009-01-01
dc.description.abstract <p>Simulation Optimization (SO) is the use of mathematical optimization techniques in which the objective function (and/or constraints) could only be numerically evaluated through simulation. Many of the proposed SO methods in the literature are rooted in or originally developed for deterministic optimization problems with available objective function. We argue that since evaluating the objective function in SO requires a simulation run which is more computationally costly than evaluating an available closed form function, SO methods should be more conservative and careful in proposing new candidate solutions for objective function evaluation. Based on this principle, a new SO approach called Golden Region (GR) search is developed for continuous problems. GR divides the feasible region into a number of (sub) regions and selects one region in each iteration for further search based on the quality and distribution of simulated points in the feasible region and the result of scanning the response surface through a metamodel. The experiments show the GR method is efficient compared to three well-established approaches in the literature. We also prove the convergence in probability to global optimum for a large class of random search methods in general and GR in particular.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/10572/
dc.identifier.articleid 1662
dc.identifier.contextkey 2806831
dc.identifier.doi https://doi.org/10.31274/etd-180810-156
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/10572
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/24778
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/10572/Kabirian_iastate_0097E_10410.pdf|||Fri Jan 14 18:23:39 UTC 2022
dc.subject.disciplines Industrial Engineering
dc.subject.keywords Continuous Optimization
dc.subject.keywords Discrete-Event Simulation
dc.subject.keywords Golden Region Search
dc.subject.keywords Probabilistic Search
dc.subject.keywords Ranking and Selection
dc.subject.keywords Simulation Optimization
dc.title Continuous optimization via simulation using Golden Region search
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication 51d8b1a0-5b93-4ee8-990a-a0e04d3501b1
thesis.degree.level dissertation
thesis.degree.name Doctor of Philosophy
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