[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290475-105":59,"doc-detail-290475-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","leveraging-machine-learning-and-optimization-models-for-enhanced-seaport-efficiency","Leveraging machine learning and optimization models for enhanced seaport efficiency","","This study reviews how predictive and prescriptive analytics can be used in seaport operations and examines the value of integrating predictive outputs into prescriptive analytics. A systematic review of 124 papers categorizes topics by application areas, application types, and techniques. Results indicate rising interest in standalone predictive or prescriptive models to improve operational efficiency, yet limited research combines them and hybridizes machine learning with operations research, such as reinforcement learning for optimization. Predictive maintenance and data-enabled control are highlighted as future directions.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/leveraging-machine-learning-and-optimization-models-for-enhanced-seaport-efficiency/290475/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/leveraging-machine-learning-and-optimization-models-for-enhanced-seaport-efficiency/290475.png","ImageObject",300,407,{"name":92,"@type":93},"Maeve","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",15,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the main goal of the study on seaport efficiency?","Question",{"text":113,"@type":114},"To overview predictive and prescriptive analytics in seaport operations and explore how predictive outputs can feed into prescriptive analytics to advance data-driven decisionmaking.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How was the research conducted?",{"text":118,"@type":114},"A systematic review of 124 papers was performed, classifying key topics by application areas, types of applications, and employed techniques.",{"name":120,"@type":111,"acceptedAnswer":121},"What gaps does the study identify in current research?",{"text":122,"@type":114},"Research combining predictive outputs with prescriptive analytics is limited, and hybridization of machine learning with operations research techniques remains underexplored.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290475,1789641773,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},5909877438554,"https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272","Maritime Economics & Logistics  \n[https://doi.org/10.1057/s41278-024-00309-w](https://doi.org/10.1057/s41278-024-00309-w)  \nORIGINAL ARTICLE  \nLeveraging machine learning and optimization models for enhanced seaport efficiency  \nMahdi Jahangard1 · Ying Xie2 · Yuanjun Feng3  \nAccepted: 8 December 2024 © The Author(s) 2025  \nAbstract  \nThis 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 decisionmaking. 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.  \nKeywords Seaport operations · Port efficiency · Predictive analytics · Prescriptive analytics · Machine learning · Optimization · Systematic literature review  \n1 Introduction  \nMaritime transport is considered one of the most crucial transportation modes globally, responsible for carrying over 80% of international trade (UNCTAD 2023) . This transportation mode is favored for its low cost and high-volume potential (Hu et al. 2021) . However, this growing reliance on shipping has increased the demands  \n* Ying Xie [ying.xie@cranfield.ac.uk](ying.xie@cranfield.ac.uk)  \n1 Anglia Rusk University, Cambridge, UK  \n2 School of Management, Cranfield University, Cranfield MK43 0AL, UK  \n3 Department of Operations and Supply Chain Management, Management School, University of Liverpool, Liverpool, UK  \non seaports, which serve as critical logistics hubs connecting shipping with inland logistics. This heightened pressure has resulted in several challenges, such as port congestion, emissions, and operational inefficiencies. Additionally, the limited availability of land, or the high cost of expansion, further adds to the complications of such logistics hubs (Hu et al. 2021) . Hence, seaports must enhance their efficiency through both internal and external actions at the operational decision level (Wu and Goh 2010) .  \nBy adopting digital technologies in ports, such as IoT, blockchain, autonomous applications, and robotics, an immense amount of heterogeneous data is generated by ports and their interconnected stakeholders (de la Peña Zarzuelo et al. 2020 ; Munim et al. 2020) . This presents opportunities to industry and academia, to harness the power of data and unlock values in them facilitating decision-making and improving operational efficiency at ports and the wider transport system. Filomet al. (2022) identified six distinct periods in the evolution of decision-making in port operations, spanning from 1910 to the present day. These periods include scientific management from 1910 to 1945; scientific method from 1945 to 1965; management information systems from 1965 to 1970; decision support systems from 1970 to 1990; business intelligence from 1990 to 2005; and business analytics from 2005 to the present day.  \nBusiness analytics is the process of using a large amount of data from various sources, quantitative and statistical analysis, and explanatory and predictive techniques, to gain insight and foresight for supporting decision-making by actions and decisions based on facts and data (Davenport and Harris 2007) . To accomplish this, business analytics utilize vario","cbCaio5JzxNBaDGX","https://ap.wps.com/l/cbCaio5JzxNBaDGX","pdf",1961960,43,"English","# Introduction\n## Seaports as logistics hubs and operational challenges\n## Role of digital technologies and data generation\n## Evolution of decision-making in port operations\n## Business analytics: descriptive, predictive, prescriptive\n## Predictive vs prescriptive analytics\n## Machine learning and operations research integration","[{\"question\":\"What is the main goal of the study on seaport efficiency?\",\"answer\":\"To overview predictive and prescriptive analytics in seaport operations and explore how predictive outputs can feed into prescriptive analytics to advance data-driven decisionmaking.\"},{\"question\":\"How was the research conducted?\",\"answer\":\"A systematic review of 124 papers was performed, classifying key topics by application areas, types of applications, and employed techniques.\"},{\"question\":\"What gaps does the study identify in current research?\",\"answer\":\"Research combining predictive outputs with prescriptive analytics is limited, and hybridization of machine learning with operations research techniques remains underexplored.\"}]","Leveraging machine learning and optimization models for enhanced seaport efficiency | PDF",108]