Estimating performance indexes of a baggage handling system using metamodels Khosravi, Abbas, Nahavandi, Saeid and Creighton, Doug 2009, Estimating performance indexes of a baggage handling system using metamodels, in ICIT 2009 : Future technology in service of regional industry : Proceedings of the IEEE Industrial Tecnology 2009 international conference, IEEE, Piscataway, N. J., pp. 1-6, doi: 10.1109/ICIT.2009.4939626. Attached Files Name Description MIMEType Size Downloads nahavandi-estimatingperformance-2009.pdf Published version application/pdf 335.15KB 796 Title Estimating performance indexes of a baggage handling system using metamodels Author(s) Khosravi, Abbas orcid.org/0000-0001-6927-0744 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Creighton, Doug orcid.org/0000-0002-9217-1231 Conference name IEEE Industrial Technology. International Conference (2009 : Gippsland, Victoria) Conference location Gippsland, Victoria Conference dates 10 - 13 Feb. 2009 Title of proceedings ICIT 2009 : Future technology in service of regional industry : Proceedings of the IEEE Industrial Tecnology 2009 international conference Editor(s) [Unknown] Publication date 2009 Conference series IEEE Industrial Technology International Conference Start page 1 End page 6 Total pages 6 Publisher IEEE Place of publication Piscataway, N. J. Summary In this study, we develop some deterministic metamodels to quickly and precisely predict the future of a technically complex system. The underlying system is essentially a stochastic, discrete event simulation model of a big baggage handling system. The highly detailed simulation model of this is used for conducting some experiments and logging data which are then used for training artificial neural network metamodels. Demonstrated results show that the developed metamodels are well able to predict different performance measures related to the travel time of bags within this system. In contrast to the simulation models which are computationally expensive and expertise extensive to be developed, run, and maintained, the artificial neural network metamodels could serve as real time decision aiding tools which are considerably fast, precise, simple to use, and reliable. Notes ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ISBN 9781424435050 Language eng DOI 10.1109/ICIT.2009.4939626 Field of Research 080602 Computer-Human Interaction Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1 Full written paper - refereed Copyright notice ©2009, IEEE Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30029267 Document type: Conference Paper Collections: Institute for Intelligent Systems Research and Innovation (IISRI) Open Access Collection GTP Research Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Fri, 11 Jun 2010, 20:45:31 EST Fri, 11 Jun 2010, 20:49:10 EST Mon, 14 Jun 2010, 08:27:09 EST Mon, 14 Jun 2010, 11:30:36 EST Mon, 14 Jun 2010, 11:32:57 EST Wed, 08 Sep 2010, 10:56:44 EST Fri, 16 Mar 2012, 18:09:21 EST Wed, 02 Jul 2014, 11:22:56 EST Wed, 02 Jul 2014, 15:03:59 EST Mon, 21 Jul 2014, 10:31:13 EST Fri, 17 Oct 2014, 10:25:21 EST Wed, 21 Oct 2015, 17:04:00 EST Tue, 17 Nov 2015, 14:16:16 EST Tue, 06 Sep 2016, 15:39:29 EST Tue, 05 Dec 2017, 09:16:19 EST Fri, 07 Sep 2018, 14:59:54 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 8 times in Scopus Search Google Scholar Access Statistics: 1103 Abstract Views, 815 File Downloads  -  Detailed Statistics Created: Fri, 11 Jun 2010, 20:45:30 EST by facadmin

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.