Full metadata record
DC FieldValueLanguage
dc.contributor.authorLin, Shin-Yeuen_US
dc.contributor.authorHorng, Shih-Chengen_US
dc.date.accessioned2014-12-08T15:25:00Z-
dc.date.available2014-12-08T15:25:00Z-
dc.date.issued2006en_US
dc.identifier.isbn0-88986-559-0en_US
dc.identifier.urihttp://hdl.handle.net/11536/17388-
dc.description.abstractIn this paper, we propose an ordinal optimization approach to solve for a good enough solution of the stochastic simulation optimization problem with huge decision-variable space. We apply the proposed ordinal optimization algorithm to G/G/1/K polling systems to solve for a good enough number-limited service discipline to minimize the weighting average waiting time. We have compared our results with those obtained by the existing service disciplines and found that our approach outperforms the existing ones. We have also used the genetic algorithm and simulated annealing method to solve the same stochastic simulation optimization problem, and the results show that our approach is much more superior in the aspects of computational efficiency and the quality of obtained solution.en_US
dc.language.isoen_USen_US
dc.subjectordinal optimizationen_US
dc.subjectstochastic simulation optimizationen_US
dc.subjectneural networken_US
dc.subjectgenetic algorithmen_US
dc.subjectpolling systemen_US
dc.subjectaverage waiting timeen_US
dc.titleOrdinal optimization approach to stochastic simulation optimization problems and applicationsen_US
dc.typeProceedings Paperen_US
dc.identifier.journalProceedings of the 15th IASTED International Conference on Applied Simulation and Modellingen_US
dc.citation.spage274en_US
dc.citation.epage279en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000239880200046-
Appears in Collections:Conferences Paper