[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120075-en":3,"doc-seo-120075-105":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},120075,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Addressing Data Limitations of Commercial Waterways via Machine Learning and Stochastic Optimization","Freight transportation is a crucial component of the US economy, yet sustaining infrastructure capacity is difficult as tonnage and value continue to rise. Commercial waterways can move large cargo volumes efficiently and with strong environmental performance, but limitations in data granularity and timeliness restrict reliable decisions on port operations and infrastructure investment. This dissertation develops machine learning and stochastic optimization models to address these gaps. Stochastic optimization captures uncertainty in commodity demand and port operations, improving investment and supply chain decisions, while machine learning models enhance forecasting using AIS and historical records. Applied to the Arkansas portion of MKARNS, the models estimate substantial potential savings and better handle disruption uncertainty.","University of Arkansas, Fayetteville  \nScholarWorks@UARK  \nGraduate Theses and Dissertations  \n5-2024  \nAddressing Data Limitations of Commercial Waterways via Machine Learning and Stochastic Optimization  \nSanjeev Bhurtyal  \nUniversity of Arkansas, Fayetteville  \nFollow this and additional works at: [https://scholarworks.uark.edu/etd](https://scholarworks.uark.edu/etd)  \n Part of the Transportation Commons, and the Transportation Engineering Commons  \nCitation  \nBhurtyal, S. (2024) . Addressing Data Limitations of Commercial Waterways via Machine Learning and Stochastic Optimization. Graduate Theses and Dissertations Retrieved from  \n[https://scholarworks.uark.edu/etd/5312](https://scholarworks.uark.edu/etd/5312)  \nThis Dissertation is brought to you for free and open access by ScholarWorks@UARK. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of ScholarWorks@UARK. For more information, please contact [scholar@uark.edu](scholar@uark.edu), [uarepos@uark.edu](uarepos@uark.edu).  \nAddressing Data Limitations of Commercial Waterways via Machine Learning and Stochastic  \nOptimization  \nA dissertation submitted in partial fulfillment  \nof the requirements for the degree of  \nDoctor of Philosophy in Engineering  \nby  \nSanjeev Bhurtyal  \nTribhuvan University  \nBachelor of Science in Civil Engineering 2017  \nUniversity of Arkansas  \nMaster of Science in Civil Engineering 2021  \nMay 2024  \nUniversity of Arkansas  \nThis dissertation is approved for recommendation to the Graduate Council.  \n\n| Sarah Hernandez, Ph.D.\u003Cbr>Dissertation Director |\n| --- |\n| Sandra Eksioglu, Ph.D.\u003Cbr>Committee Member |\n| Suman Mitra, Ph.D.\u003Cbr>Committee Member |\n\nHeather Nachtmann, Ph.D. Committee Member  \nAbstract  \nFreight transportation is a crucial component of the US economy, with truck, rail, and water contributing significantly to the Gross Domestic Product (GDP) . However, challenges arise in maintaining infrastructure capacity to accommodate the growing tonnage and value of freight transportation. Waterways offer high efficiency and environmental friendliness, with the capacity to transport substantial cargo volumes. Utilizing waterways can alleviate bottlenecks inlandside transportation, accommodating the rising tonnage of commodity flow in the US. Despite the advantages of waterway transportation, the data limitations hinder informed decision-making regarding port operation and infrastructure investment. The lack of granularity and timeliness in available data sources makes it challenging to understand local conditions and respond to changing trends. The goal of this study is to address these data limitations through the development of machine learning and stochastic optimization models.  \nThis dissertation addresses data limitations in commercial waterways through the development of stochastic optimization and machine learning models. Stochastic optimization models account for uncertainties in commodity demand and port operations, offering insights for optimal investment strategies and supply chain management. Machine learning models improve predictive capabilities, aiding informed decision-making by port authorities. By integrating these approaches, the dissertation advances data-driven solutions for optimizing commercial waterway operations and infrastructure investments.  \nThe first stochastic optimization model presented minimizes the sum of port infrastructure investment and expected supply chain cost, considering uncertainty in commodity demand. Applied to the Arkansas section of the McClellan-Kerr Arkansas River Navigation System (MKARNS), it reveals potential annual costs of up to $21 million without accounting for  \ndemand uncertainty. To address the limitation of excluding port disruption scenarios, an extended model incorporates port closure uncertainty. This model, utilizing reinforcement learning to enhance efficiency, outperforms traditional methods for large-scale problems.  \nAddi","cbCairhl1KGyAUyh","https://ap.wps.com/l/cbCairhl1KGyAUyh","pdf",3275354,1,138,"English","en",105,"# Abstract\n## Stochastic optimization for investment and supply chain decisions\n## Machine learning for forecasting using AIS and WCS\n## Applications to Arkansas River Navigation System (MKARNS)","[{\"question\":\"Why are data limitations a problem for commercial waterway decision-making?\",\"answer\":\"Limited granularity and timeliness make it difficult to understand local conditions and respond to changing trends, which weakens decisions about port operations and infrastructure investment.\"},{\"question\":\"How do the stochastic optimization models help in the dissertation?\",\"answer\":\"They incorporate uncertainty in commodity demand and port operations to support optimal investment strategies and supply chain management under risk.\"},{\"question\":\"What data sources and machine learning method are used for commodity volume prediction?\",\"answer\":\"The study leverages Automatic Identification System (AIS) data together with historical Waterborne Commerce Statistics (WCS) data, using a Long Short-Term Memory (LSTM) model to predict commodity volume at port terminals.\"}]","Addressing Data Limitations of Commercial Waterways via Machine Learning and Stochastic Optimization | 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are data limitations a problem for commercial waterway decision-making?","Question",{"text":75,"@type":76},"Limited granularity and timeliness make it difficult to understand local conditions and respond to changing trends, which weakens decisions about port operations and infrastructure investment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the stochastic optimization models help in the dissertation?",{"text":80,"@type":76},"They incorporate uncertainty in commodity demand and port operations to support optimal investment strategies and supply chain management under risk.",{"name":82,"@type":73,"acceptedAnswer":83},"What data sources and machine learning method are used for commodity volume prediction?",{"text":84,"@type":76},"The study leverages Automatic Identification System (AIS) data together with historical Waterborne Commerce Statistics (WCS) data, using a Long Short-Term Memory (LSTM) model to predict commodity volume at port 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