DC FieldValueLanguage
dc.contributorDepartment of Logistics and Maritime Studiesen_US
dc.creatorJia, Sen_US
dc.creatorLi, CLen_US
dc.creatorXu, Zen_US
dc.date.accessioned2021-05-13T08:31:45Z-
dc.date.available2021-05-13T08:31:45Z-
dc.identifier.issn0191-2615en_US
dc.identifier.urihttp://hdl.handle.net/10397/89850-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.subjectBerth allocationen_US
dc.subjectCongestion mitigationen_US
dc.subjectPort operationsen_US
dc.subjectService time uncertaintyen_US
dc.subjectSimulation optimizationen_US
dc.titleA simulation optimization method for deep-sea vessel berth planning and feeder arrival scheduling at a container porten_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage174en_US
dc.identifier.epage196en_US
dc.identifier.volume142en_US
dc.identifier.doi10.1016/j.trb.2020.10.007en_US
dcterms.abstractVessels served by a container port can usually be classified into two types: deep-sea vessels and feeders. While the arrival times and service times of deep-sea vessels are known to the port operator when berth plans are being devised, the service times of feeders are usually uncertain due to lack of data interchange between the port operator and the feeder operators. The uncertainty of feeder service times can incur long waiting lines and severe port congestion if the service plans for deep-sea vessels and feeders are poorly devised. This paper studies the problem of how to allocate berths to deep-sea vessels and schedule arrivals of feeders for congestion mitigation at a container port where the number of feeders to be served is significantly larger than the number of deep-sea vessels, and where the service times of feeders are uncertain. We develop a stochastic optimization model that determines the berth plans of deep-sea vessels and arrival schedules of feeders, so as to minimize the departure delays of deep-sea vessels and schedule displacements of feeders. The model controls port congestion through restricting the expected queue length of feeders. We develop a three-phase simulation optimization method to solve this problem. Our method comprises a global phase, a local phase, and a clean-up phase, where the simulation budget is wisely allocated to the solutions explored in different phases so that a locally optimal solution can be identified with a reasonable amount of computation effort. We evaluate the performance of the simulation optimization method using test instances generated based on the operational data of a container port in Shanghai.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationTransportation research. Part B, Methodological, Dec. 2020, v. 142, p. 174-196en_US
dcterms.isPartOfTransportation research. Part B, Methodologicalen_US
dcterms.issued2020-12-
dc.identifier.scopus2-s2.0-85095455560-
dc.identifier.eissn1879-2367en_US
dc.description.validate202105 bchyen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera0831-n04, a0986-n02-
dc.identifier.SubFormID1939, 2346-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextRGC: 152186/14E (Ref: 1939 [a0831-n04])en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2022.12.31en_US
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