Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/98324
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dc.contributorMainland Development Officeen_US
dc.contributorDepartment of Logistics and Maritime Studiesen_US
dc.creatorWang, Sen_US
dc.creatorZhen, Len_US
dc.creatorZhuge, Den_US
dc.date.accessioned2023-04-27T01:04:48Z-
dc.date.available2023-04-27T01:04:48Z-
dc.identifier.issn0191-2615en_US
dc.identifier.urihttp://hdl.handle.net/10397/98324-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.rights© 2017 Elsevier Ltd. All rights reserved.en_US
dc.rights© 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/.en_US
dc.rightsThe following publication Wang, S., Zhen, L., & Zhuge, D. (2018). Dynamic programming algorithms for selection of waste disposal ports in cruise shipping. Transportation Research Part B: Methodological, 108, 235-248 is available at https://doi.org/10.1016/j.trb.2017.12.016.en_US
dc.subjectCruise shipen_US
dc.subjectDynamic programmingen_US
dc.subjectWaste disposalen_US
dc.subjectWater transportationen_US
dc.titleDynamic programming algorithms for selection of waste disposal ports in cruise shippingen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage235en_US
dc.identifier.epage248en_US
dc.identifier.volume108en_US
dc.identifier.doi10.1016/j.trb.2017.12.016en_US
dcterms.abstractThe cruise industry has maintained a steady growth in the past 20 years. Due to the large number of cruise passengers and regulations on sea environment protection, determining at which ports to dispose of the waste generated onboard a cruise ship is a key decision to reduce the cost for a cruise company. We address four versions of the problem: the cruise itinerary is either static or dynamic and the amount of waste generated on each voyage leg is either deterministic or stochastic. We propose a polynomial-time solution algorithm for the static deterministic model, and the idea of the algorithm can also be used to solve the static stochastic model and the dynamic deterministic model. Second, we identify the structure of the optimal policy to the dynamic stochastic problem, based on which an efficient dynamic programming algorithm is developed. Extensive numerical experiments derived from problems of real-case scales demonstrate the efficiency of the proposed algorithms.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationTransportation research. Part B, Methodological, Feb. 2018, v. 108, p. 235-248en_US
dcterms.isPartOfTransportation research. Part B, Methodologicalen_US
dcterms.issued2018-02-
dc.identifier.scopus2-s2.0-85044970155-
dc.identifier.eissn1879-2367en_US
dc.description.validate202304 bckwen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberLMS-0344-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Natural Science Foundation of Chinaen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS24586749-
dc.description.oaCategoryGreen (AAM)en_US
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