Leveraging machine learning and optimization models for enhanced seaport efficiency

Simple item page
dc.contributor.authorJahangard, Mahdi
dc.contributor.authorXie, Ying
dc.contributor.authorFeng, Yuanjun
dc.date.accessioned2025-03-10T14:17:06Z
dc.date.available2025-03-10T14:17:06Z
dc.date.freetoread2025-03-10
dc.date.issued2025-12-31
dc.date.pubOnline2025-02-14
dc.description.abstractThis study provides an overview of the application of predictive and prescriptive analytics in seaport operations and explore the potential of integrating predictive outputs into prescriptive analytics to advance research in this field. A systematic review of 124 papers was performed to identify and classify key topics based on application areas, types of applications, and employed techniques. Our findings show a growing interest in developing either predictive or prescriptive analytics models to improve seaport operational efficiency. However, there is limited research combining predictive outputs with prescriptive analytics for data-driven decision-making. Additionally, the hybridization of machine learning and operations research techniques remains underexplored. One promising area is applying machine learning models, such as reinforcement learning, to solve optimization problems. Predictive maintenance and data-enabled operational control measures for port equipment and facilities are also highlighted as interesting future research areas.
dc.description.journalNameMaritime Economics & Logistics
dc.description.sponsorshipThis work was supported by the Engineering and Physical Sciences Research Council UK [grant number EP/Y024605/1] and Department of Transport UK.
dc.identifier.citationJahangard M, Xie Y, Feng Y. (2025) Leveraging machine learning and optimization models for enhanced seaport efficiency. Maritime Economics & Logistics, Available online 14 February 2025
dc.identifier.eissn1479-294X
dc.identifier.elementsID564712
dc.identifier.issn1479-2931
dc.identifier.urihttps://doi.org/10.1057/s41278-024-00309-w
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23547
dc.languageEnglish
dc.language.isoen
dc.publisherSpringer
dc.publisher.urihttps://link.springer.com/article/10.1057/s41278-024-00309-w
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject3509 Transportation, Logistics and Supply Chains
dc.subject4406 Human Geography
dc.subject35 Commerce, Management, Tourism and Services
dc.subject44 Human Society
dc.subjectMachine Learning and Artificial Intelligence
dc.subjectGeneric health relevance
dc.subjectLogistics & Transportation
dc.subject3509 Transportation, logistics and supply chains
dc.subject4406 Human geography
dc.subjectSeaport operations
dc.subjectPort efficiency
dc.subjectPredictive analytics
dc.subjectPrescriptive analytics
dc.subjectMachine learning
dc.subjectOptimization
dc.subjectSystematic literature review
dc.titleLeveraging machine learning and optimization models for enhanced seaport efficiency
dc.typeArticle
dcterms.dateAccepted2024-12-08

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Leveraging_machine_learning-2025.pdf
Size:
1.92 MB
Format:
Adobe Portable Document Format
Description:
Published version
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Plain Text
Description:
Download

Collections

Staff publications (SoM)