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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/28
DC FieldValueLanguage
dc.contributor.authorNguyen Hong Vien_US
dc.contributor.authorNguyen Duc Thinhen_US
dc.date.accessioned2020-03-12T16:53:23Z-
dc.date.available2020-03-12T16:53:23Z-
dc.date.issued2019-
dc.identifier.citationhttp://www.ijmmm.org/index.php?m=content&c=index&a=show&catid=62&id=516 en_US
dc.identifier.issn1793-8198-
dc.identifier.urihttp://epub.vgu.edu.vn/handle/dlibvgu/28-
dc.description.abstractQuay crane scheduling problem (QCSP) is the problem of the allocation of quay cranes to handle the unloading and loading of containers at seaport container terminals and defining the service sequence of vessel bays of each quay crane. The treatment of crane interference constraints and the increased in vessel size make the problem difficult to solve. Due to the growing interest in applied research for this problem, many researchers have used different algorithms and methods to obtain some solutions. This paper will propose a modified genetic algorithm combined with priority rules to deal with it. The advantage of the proposed algorithm comes from the fact that crane interference constraints, non-simultaneous constraints and precedence constraints can all be handled in simple ways and is to provide practical solutions. The effectiveness and reliability of the approach are tested using several benchmark instances proposed by Meisel and Bierwirth (2011). A comparison with the current best-known solutions reveals that the proposed method is capable of finding the optimal or near-optimal solution in reasonable computing time.en_US
dc.language.isoenen_US
dc.publisherSingapore : IACSIT Pressen_US
dc.relation.ispartofInternational Journal of Materials, Mechanics, and Manufacturingen_US
dc.relation.ispartofseriesVol. 7(1);435 - 440-
dc.subjectcrane schedulingen_US
dc.subjectgenetic algorithmen_US
dc.subjectoptimizationen_US
dc.subjectpriority rulesen_US
dc.titleMarine quay crane scheduling using a combined modified genetic algorithm and priority rules approachen_US
dc.typeArticleen_US
dc.identifier.doi10.18178/ijmmm.2019.7.1.422-
item.languageiso639-1other-
item.grantfulltextnone-
item.fulltextNo Fulltext-
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