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Conference Paper: Multi-objective genetic algorithms for scheduling mateiral handling equipment at automated air cargo terminals

TitleMulti-objective genetic algorithms for scheduling mateiral handling equipment at automated air cargo terminals
Authors
Issue Date2004
PublisherIEEE.
Citation
IEEE Conference on Cybernetics and Intelligent Systems, Singapore, 1-3 December 2004, v. 2, p. 718-723 How to Cite?
AbstractIn order to improve thc productivitics of a typical cargo handling system, it is important to reduce the waiting time of stacker crancs (SCs) and the total traveling time of automated guided vehicles (AGVs) through efficient scheduling of SCs and ACVs, which are cooperating tightly to perform cargo handling operations in an optimal way. In this paper, we devclop and investigate the application of the multi-objective genetic algorithm (MOCA) to solve such schcduling problem with the objectives of minimizing the ACV total traveling time and thc total delay time of the SC. The results of the experimcnts demonstrated that MOGA produces better solution than the single objective genetic algorithms.
Persistent Identifierhttp://hdl.handle.net/10722/46557

 

DC FieldValueLanguage
dc.contributor.authorLau, HYKen_HK
dc.contributor.authorZhao, Yen_HK
dc.date.accessioned2007-10-30T06:52:53Z-
dc.date.available2007-10-30T06:52:53Z-
dc.date.issued2004en_HK
dc.identifier.citationIEEE Conference on Cybernetics and Intelligent Systems, Singapore, 1-3 December 2004, v. 2, p. 718-723en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46557-
dc.description.abstractIn order to improve thc productivitics of a typical cargo handling system, it is important to reduce the waiting time of stacker crancs (SCs) and the total traveling time of automated guided vehicles (AGVs) through efficient scheduling of SCs and ACVs, which are cooperating tightly to perform cargo handling operations in an optimal way. In this paper, we devclop and investigate the application of the multi-objective genetic algorithm (MOCA) to solve such schcduling problem with the objectives of minimizing the ACV total traveling time and thc total delay time of the SC. The results of the experimcnts demonstrated that MOGA produces better solution than the single objective genetic algorithms.en_HK
dc.format.extent1011756 bytes-
dc.format.extent2836 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Conference on Cybernetics and Intelligent Systems-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2004 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.en_HK
dc.titleMulti-objective genetic algorithms for scheduling mateiral handling equipment at automated air cargo terminalsen_HK
dc.typeConference_Paperen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICCIS.2004.1460676en_HK
dc.identifier.hkuros107261-

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