Download or read book Optimizing Empty Container Repositioning in a Truck-rail Intermodal Network written by Kathryn Olivia Collins and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The geographic imbalance of freight moved by the transportation industry requires repositioning of empty containers. Empty container repositioning (ECR) describes the strategy for empty container relocation to reduce cost and satisfy demand. ECR costs the transportation industry billions of dollars per year worldwide, so the efficient and effective execution of ECR is necessary for maximal equipment utilization. The trucking industry is greatly impacted by ECR decisions because of the high volumes of freight moved every year, leading to thousands of empty containers in need of relocation every week. The trucking industry is incentivized to partner with other transportation modes, considered intermodal transport, for ECR movements because of other modes' lower transit costs. A breadth of research exists for ECR optimization for intermodal ocean networks, but trucking industries operate cross-country and require a low-cost transcontinental solution. Intermodal railroad networks are the ideal ECR solution for trucking companies, but a lack of research exists addressing ECR flow optimization in a strictly truck-rail network. This thesis focuses on an optimization model for the ECR decisions of a trucking company utilizing a truck-rail intermodal network. Imbalances between inbound and outbound freight flows in metropolitan areas result in sources and demands for empty containers across the network. Empty containers are repositioned via railroad to fulfill demand between these areas. The trucking company's primary goal is to fulfill demand for empty containers while minimizing fees paid to the railroads and its own equipment relocation costs. The research objective of this thesis is to develop an optimization model to support ECR decisions for realistic truck-rail intermodal systems. The model is demonstrated using data from a leading trucking company in North America. Comparing the optimization model results to the plans developed by the company's empty-planning team shows that the model produces high-quality plans, achieves cost savings, can be solved efficiently, and presents novel solutions to the business.