DC FieldValueLanguage
dc.contributorDepartment of Logistics and Maritime Studiesen_US
dc.creatorZhen, Len_US
dc.creatorWu, Yen_US
dc.creatorWang, Sen_US
dc.creatorLaporte, Gen_US
dc.date.accessioned2021-08-13T06:13:34Z-
dc.date.available2021-08-13T06:13:34Z-
dc.identifier.issn0191-2615en_US
dc.identifier.urihttp://hdl.handle.net/10397/90653-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.subjectEmission control areaen_US
dc.subjectFleet deploymenten_US
dc.subjectGreen technology adoptionen_US
dc.subjectLiner shipping managementen_US
dc.subjectScrubbersen_US
dc.subjectShore poweren_US
dc.titleGreen technology adoption for fleet deployment in a shipping networken_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage388en_US
dc.identifier.epage410en_US
dc.identifier.volume139en_US
dc.identifier.doi10.1016/j.trb.2020.06.004en_US
dcterms.abstractThe Emission Control Areas (ECAs) established by the International Maritime Organization are beneficial to reduce the sulphur emissions in maritime transportation but bring a significant increase in operating cost for shipping liners. Low sulphur emissions are required when ships berth or sail within ECAs. It is an irreversible trend that green technologies such as scrubbers and shore power will be implemented in maritime shipping industry. However, the literature lacks a quantitative decision methodology on green technology adoption for fleet deployment in a shipping network in the context of ECAs. Given a shipping network with multiple routes connected by transshipment hubs, this study proposes a nonlinear mixed integer programming model to optimally determine fleet deployment along routes (including green technology adoption), sailing speeds on all legs, timetables, cargo allocation among routes for each origin-destination pair, and berth allocation considering the availability of shore power at different berths in order to minimize total five types of cost. A three-phase heuristic is also developed to solve this problem. Numerical experiments with real-world data are conducted to validate the effectiveness of the proposed model and the efficiency of the three-phase heuristic. Some managerial implications are also outlined on the basis of the numerical experiments.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationTransportation research. Part B, Methodological, Sept. 2020, v. 139, p. 388-410en_US
dcterms.isPartOfTransportation research. Part B, Methodologicalen_US
dcterms.issued2020-09-
dc.identifier.scopus2-s2.0-85087930110-
dc.identifier.eissn1879-2367en_US
dc.description.validate202108 bchyen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera1003-n01-
dc.identifier.SubFormID2383-
dc.description.fundingSourceRGCen_US
dc.description.fundingText15201718en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2022.09.30en_US
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