Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/117749
DC FieldValueLanguage
dc.contributorDepartment of Aeronautical and Aviation Engineering-
dc.contributorMainland Development Office-
dc.contributorDepartment of Logistics and Maritime Studies-
dc.creatorLiao, X-
dc.creatorWu, L-
dc.creatorShu, S-
dc.creatorZhao, H-
dc.date.accessioned2026-03-05T06:58:18Z-
dc.date.available2026-03-05T06:58:18Z-
dc.identifier.issn0308-8839-
dc.identifier.urihttp://hdl.handle.net/10397/117749-
dc.language.isoenen_US
dc.publisherRoutledge, Taylor & Francis Groupen_US
dc.subject(Mixed) integer programmingen_US
dc.subjectSample average approximationen_US
dc.subjectShip repositioningen_US
dc.subjectSpot marketen_US
dc.subjectTramp shippingen_US
dc.titleStochastic ship repositioning optimization in tramp spot market with a cargo selection mechanismen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.doi10.1080/03088839.2025.2586757-
dcterms.abstractIn tramp shipping, the spot market is characterized by intense competition and volatility. Securing profitable spot cargoes for a tramp shipping company is challenging. To handle this issue, this paper addresses a ship repositioning problem in the dry bulk spot market, aiming to provide short-term operational planning for a tramp shipping company. The goal is to maximize profits by optimizing destination decisions, speed, and cargo selection upon arrival. To handle high competition and market volatility, the cargo selection mechanism considers competitors’ movements, a priority principle, and cargo-worthiness, along with uncertainties in future cargo availability and competitors’ behavior. We propose a two-stage stochastic programming model to solve this problem. The first stage determines the destination and arrival time for each empty ship, while the second stage involves cargo acquisition based on first-stage decisions, revealed uncertainties, and the cargo selection mechanism. We use the Sample Average Approximation (SAA) method to solve the stochastic model. Extensive experiments validate the SAA method’s performance, and the Value of Stochastic Solution (VSS) is calculated by comparing it with a deterministic model. Finally, a sensitivity analysis based on a case study informs the development of operational policies for tramp shipping companies.-
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationMaritime policy and management, Published online: 02 Dec 2025, Latest Articles, https://doi.org/10.1080/03088839.2025.2586757-
dcterms.isPartOfMaritime policy and management-
dcterms.issued2025-
dc.identifier.scopus2-s2.0-105023903412-
dc.identifier.eissn1464-5254-
dc.description.validate202603 bcjz-
dc.description.oaNot applicableen_US
dc.identifier.SubFormIDG001124/2026-01en_US
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis work was supported by the National Natural Science Foundation of China [Grant No. 72301230], the Research Grants Council of the Hong Kong Special Administrative Region, China [Grant No. 25223223], and Shenzhen Science and Technology Program, China [Grant No. JCYJ20240813162012016].en_US
dc.description.pubStatusEarly releaseen_US
dc.date.embargo2027-06-02en_US
dc.description.oaCategoryGreen (AAM)en_US
Appears in Collections:Journal/Magazine Article
Open Access Information
Status embargoed access
Embargo End Date 2027-06-02
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.