[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126903-en":3,"doc-seo-126903-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126903,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Service anomaly detection in dry bulk terminals - a machine learning approach","Bulk terminals are complex systems where many interacting variables affect terminal performance and service outcomes. While big-data analysis is becoming central to terminal management, prior research has not directly addressed service anomalies in dry bulk operations. This paper proposes a two-stage unsupervised machine learning workflow: association discovery to characterize key variable links, followed by isolation-forest scoring to quantify anomalous vessels and services. Results support greater transparency and improved contract decisions.","International Journal of Shipping and Transport Logistics  \nISSN online: 1756-6525-ISSN print: 1756-6517  \n[https://www.inderscience.com/ijstl](https://www.inderscience.com/ijstl)  \nService anomaly detection in dry bulk terminals: a machine learning approach  \nIñigo L. Ansorena  \nDOI: 10.1504/IJSTL.2023.10051280  \nArticle History:  \nReceived: 12 June 2021  \nAccepted: 12 September 2022  \nPublished online: 09 November 2023  \nCopyright © 2023 Inderscience Enterprises Ltd.  \n\n| Service anomaly detection in dry bulk terminals: a machine learning approach |\n| --- |\n| Iñigo L. Ansorena\u003Cbr>Universidad Internacional de La Rioja,\u003Cbr>c/ Avenida de la Paz, 137, Logroño, La Rioja, Spain\u003Cbr>Email: [ilopezans@gmail.com](ilopezans@gmail.com)\u003Cbr>Abstract: Bulk terminals are complex environments due to a number of variables that affect terminal performance. Although the analysis of big datasets is destined to become an important component of terminal management, previous research has not addressed this issue yet. This paper aims to shed new light on the operation of dry bulk terminals through a two-stage method based on unsupervised machine learning techniques. The first step gives an overview of the terminal’s performance, revealing the strongest associations between the variables, while the second calculates an anomaly score for each vessel through an optimised implementation of the isolation forest. As a result, we detect anomalous services which could be directly attributable to the terminal operator. This method can be used to increase transparency in service and assist the terminal operator and ship agents in future contracts.\u003Cbr>Keywords: bulk cargo terminals; terminal performance; machine learning; association discovery; anomaly detection; anomalous service; inefficient service; association rules.\u003Cbr>Reference to this paper should be made as follows: Ansorena, I.L. (2023)‘Service anomaly detection in dry bulk terminals: a machine learning approach’, Int. J. Shipping and Transport Logistics, Vol. 17, No. 3, pp.281–302 .\u003Cbr>Biographical notes: Iñigo L. Ansorena is a Civil Engineer and holds a PhD from the Polytechnic University of Madrid. He is the main author of more than 20 studies, which have been published in scientific journals. His research interests focus on port issues, including machine learning, logistics and operations research. He is currently at the Universidad Internacional de La Rioja (Unir) where he is supervising several master theses. |\n\n1 Introduction  \nUNCTAD (2019) estimated that seaborne trade of dry bulks (major and minor bulks) was 5.2 billion tons in 2018. This volume exceeded the total volume of containerised shipping. However, dry bulk terminals receive considerable less attention than container terminals in the port literature. The planning of operations in a dry bulk terminal is a complex task due to multiple interdependencies between the terminals and the ships, such as storage of materials, type and volume of materials, ship size, number of cargo holds, stresses in the ship, etc. In broad terms, the operation of a bulk carrier can be described  \nCopyright © The Author(s) 2023. Published by Inderscience Publishers Ltd. This is an Open Access Article distributed under the CC BY license. ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/))  \n282 I.L. Ansorena  \nby the following parameters: deadweight tonnage, shipload, number of cargo holds, number and type of materials (e.g., iron ore, coking coal, steam coal, etc.), and last but not least the structural stresses during the loading/discharging process. The first parameters vary from ship to ship and the terminal cannot avoid such variations. Only the structural considerations are somehow in the hands of the terminal operator, see IACS (2018) .  \nIn this context, the contract (charter party) between the charterer (operator) and the ship-owner sets a laytime period which is the agreed period of time for loading or unloadin","cbCaikdxkGIUSfTV","https://ap.wps.com/l/cbCaikdxkGIUSfTV","pdf",1151798,1,23,"English","en",105,"# Introduction\n## Service in dry bulk terminals\n# Literature review\n## Service in dry bulk terminals\n# Methodology\n## Association discovery\n## Anomaly detection\n## Robustness evaluation\n# Results and discussion\n## Main service rules and anomalous vessels\n# Conclusions and future work","[{\"question\":\"What problem does the paper address in dry bulk terminals?\",\"answer\":\"It addresses when vessel delays are attributable to terminal service and notes that anomalies in dry bulk terminals have not been studied using existing data with appropriate methods.\"},{\"question\":\"What is the proposed two-stage method?\",\"answer\":\"First, it uses unsupervised learning to discover associations between operational variables and performance. Second, it computes anomaly scores for each vessel using an optimized isolation forest implementation.\"},{\"question\":\"How can the detected anomalous services be used?\",\"answer\":\"The approach can increase transparency in service, helping terminal operators and ship agents support future contracts and potentially link anomalies to operator-related causes.\"}]","Service anomaly detection in dry bulk terminals - a machine learning approach | PDF",1785935530,58,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"service-anomaly-detection-in-dry-bulk-terminals-a-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/service-anomaly-detection-in-dry-bulk-terminals-a-machine-learning-approach/126903/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address in dry bulk terminals?","Question",{"text":75,"@type":76},"It addresses when vessel delays are attributable to terminal service and notes that anomalies in dry bulk terminals have not been studied using existing data with appropriate methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the proposed two-stage method?",{"text":80,"@type":76},"First, it uses unsupervised learning to discover associations between operational variables and performance. Second, it computes anomaly scores for each vessel using an optimized isolation forest implementation.",{"name":82,"@type":73,"acceptedAnswer":83},"How can the detected anomalous services be used?",{"text":84,"@type":76},"The approach can increase transparency in service, helping terminal operators and ship agents support future contracts and potentially link anomalies to operator-related causes.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]