Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/23599
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRoy, Debjit-
dc.contributor.authorKoster, Rene De-
dc.date.accessioned2021-02-16T05:14:01Z-
dc.date.available2021-02-16T05:14:01Z-
dc.date.issued2015-04-
dc.identifier.citationRoy, Debjit. & Koster, Rene De. (2015). Stochastic modelling of unloading and loading operations at a container terminal using automated lifting vehicles. SSRN.en_US
dc.identifier.urihttp://hdl.handle.net/11718/23599-
dc.description.abstractWith growing worldwide trade, container terminals have grown in number and size. Many new terminals are now automated to increase operational efficiency. The key focus is on improving seaside processes, where a distinction can be made between single quay crane operations (all quay cranes are either loading or unloading containers) and overlapping quay crane operations (some quay cranes are loading while others are unloading containers). From existing studies, it is not clear if the design insights obtained from analyzing single operations, such as optimal stack layout, are consistent with the insights obtained from analyzing overlapping operations. In this paper, we develop new integrated stochastic models for analyzing the performance of overlapping loading and unloading operations that capture the complex stochastic interactions among quayside, vehicle, and stackside processes. Using these integrated models, we are able to show that that there are stack layout configurations that are robust for both single (either loading or unloading) and for overlapping (both loading and unloading) operations.en_US
dc.language.isoenen_US
dc.publisherSSRNen_US
dc.subjectlogisticsen_US
dc.subjectqueuingen_US
dc.subjecttransportationen_US
dc.subjectuncertainty modellingen_US
dc.titleStochastic modelling of unloading and loading operations at a container terminal using automated lifting vehiclesen_US
dc.typeWorking Paperen_US
Appears in Collections:Working Papers

Files in This Item:
File Description SizeFormat 
SSRN-id2599593.pdfSSRN-id25995931.63 MBAdobe PDFView/Open


Items in IIMA Institutional Repository are protected by copyright, with all rights reserved, unless otherwise indicated.