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Handling freight in a crossdocking terminal is labor intensive and therefore costly because workers must unload, sort, and transfer a wide variety of freight from incoming to outgoing trailers. The efficiency of workers depends in large part on how trailers are assigned to doors around the dock; that is, on its layout. A good layout reduces travel distances without creating congestion, but until now no tools have been available to construct such layouts. We describe models of travel cost and three types of congestion typically experienced in crossdocking terminals, and we use them to construct layouts that minimize the labor cost of transferring freight. We report on the use of our models in the less-than-truckload trucking industry, including an implementation at a terminal in Stockton, California that improved productivity by more than 11%.

Consolidation of freight and merging operations are essential for transportation companies to reduce costs and improve the level of service provided to customers. Such operations take place in intermediate facilities or terminals located between the origins and the destinations of freight. This survey reviews the main contributions from the operations research literature on freight transportation planning problems where the presence of intermediate facilities has a strong impact on the cost of the system and on how goods are delivered. In particular, we focus on the tactical planning issues arising in this context. We have identified three classes of problems with intermediate facilities: vehicle routing problems, transshipment problems, and service network design problems. For each class of problems we provide an overview of the main problem variants, survey the methods used for their solution, and indicate open research directions.

Increasing freight volumes and challenging environments in seaports and container terminals worldwide require streamlined and reliable operations. Digital twins are seen as important drivers of the digitalization in seaports by providing a basis for higher transparency, control and data-driven decision making. In this context, however, the concept is rarely studied, and implementation issues are not comprehensively discussed. The paper presents an exploratory study of digital twins in seaports based on a literature review and case studies. The analysis reveals a standardization deficit for digital twin implementations, an inflationary and improper use of the term digital twin, and fields of research that need to be explored further. The application of optimization methods and the integration of simulation-based optimization in the field of seaports and container terminals is examined, due to its relevance for digital twins. Important lessons learned can be taken from the most advanced implementations, integrating simulations and emulations with optimization methods. An in-depth examination of multiple case studies and discussions with global port leaders yields valuable perspectives on the varied levels of digital twin implementations being applied today, including insights into the most advanced implementations currently being used in ports and container terminals. As a result of the analyses conducted, various research directions and a research agenda are presented.

Transportation infrastructures, particularly those supporting intermodal freight, are vulnerable to natural disasters and man-made disasters that could lead to severe service disruptions. These disruptions can drastically degrade the capacity of a transportation mode and consequently have adverse impacts on intermodal freight transport and freight supply chain. To address service disruption, this paper develops a model to reliably route freight in a road-rail intermodal network. Specifically, the model seeks to provide the optimal route via road segments (highway links), rail segments (rail lines), and intermodal terminals for freight when the network is subject to capacity uncertainties. To ensure reliability, the model plans for reduced network link, node, and intermodal terminal capacity. A major contribution of this work is that a framework is provided to allow decision makers to determine the amount of capacity reduction to consider in planning routes to obtain a user-specified reliability level. The proposed methodology is demonstrated using a real-world intermodal network in the Gulf Coast, Southeastern, and Mid-Atlantic regions of the United States. It is found that the total system cost increases with the level of capacity uncertainty and with increased confidence levels for disruptions at links, nodes, and intermodal terminals.

To estimate impacts, support cost–benefit analyses, and enable project prioritization, it is necessary to identify the area of influence of a transportation infrastructure project. For freight related projects, like ports, state-of-the-practice methods to estimate such areas ignore complex interactions among multimodal supply chains and can be improved by examining the multimodal trips made to and from the facility. While travel demand models estimate multimodal trips, they may not contain robust depictions of water and rail, and do not provide direct observation. Project-specific data including local traffic counts and surveys can be expensive and subjective. This work develops a systematic, objective methodology to identify multimodal “freight-shed” (or “catchment” areas) for a facility from vehicle tracking data and demonstrates application with a case study involving diverse freight port terminals. Observed truck Global Positioning System and maritime Automatic Identification System data are subjected to robust pre-processing algorithms to handle noise, cluster stops, assign data points to the network (map-matching), and address spatial and temporal conflation. The method is applied to 43 port terminals on the Arkansas River to estimate vehicle miles and hours travelled, origin, destination, and pass-through zones, and areas of modal overlap within the catchment areas. Case studies show that the state-of-the-practice 100-mile diameter influence areas include between 15 and 34% of the multimodal freight-shed areas mined from vehicle tracking data, demonstrating that adoption of an arbitrary radial area for different ports would lead to inaccurate estimates of project benefits.

Less-than-truckload (LTL) carriers operate networks of consolidation terminals, and route each customer shipment through a sequence of transfer terminals. At each terminal stop, a shipment is unloaded from an inbound trailer and reloaded onto an outbound trailer. A load plan determines the specific path of terminals to be used to transfer freight moving between each origin and destination. The design of the load plan determines the line-haul transportation and handling costs required to serve customers. This paper develops an effective neighborhood search heuristic for solving a natural integer programming model for LTL load plan design. During each iteration of the heuristic, a modified version of the integer programming model is solved to find improving changes freight transfer paths for a subset of the origin-destination terminal pairs. Unlike existing approaches in the literature, the modified model solved here allows the simultaneous rerouting of freight destined for multiple terminals during each neighborhood search iteration, yet remains computationally practical. Computational experiments using data from a large U.S. carrier show substantial cost improvements generated by increased freight consolidation, in the order of 6%–7%, accounting for over $300,000 per week.

The logistics transportation system is critical to the United States economy. Underground Logistics Transportation (ULT) is a class of automated transportation systems in which vehicles carry freight through pipelines and tunnels between terminals. Being able to use a part of the underground space of existing highways will greatly facilitate the construction of such pipelines and tunnels and reduce their construction costs. Underground Logistics Transportation (ULT) could be the answer to make freight transport more sustainable and competitive. Texas highways and railroads are expected to increase by nearly 207% from 2003 to 2030. Truck tonnage will grow by 251%, while rail tonnage is forecasted to increase 118%. The number of trucks carrying NAFTA goods will increase by 263%, and the number of rail units will grow by 195%. This will have a profound impact on the highway and rail systems. The objective of this paper is to present requirements and operational components for three types of ULT lines: standard shipping containers, a standard crate size, and a standard pallet size. This study examines the use of ULT as a mode of underground transportation with the help of three case studies. This research shows that ULT is financially viable, feasible, greener, cost effective, and can become an important part of intermodal freight mobility.

In the last few decades, with the advance of IoT technologies throughout the most different fields have been changing the way some process is handled. However, possessing a great volume of data does not mean the operation will be handled better, in many cases the data lays in the storage and stays untouched. An increasing quantity of freight imposes container terminals to improve their capacities. Therefore, decision-making problems for operation planning, controlling, and evaluation needs to be well defined. Understanding that necessity, managers need to rely on, almost exclusively, the Terminal Operational System (TOS) to identify and solve the bother necks during daily operation. Although the TOS also had some improvements in the past years due to technological advances, it is still not enough to evaluate each operational step for the operation. This paper uses a data mining and data analysis evaluation of the container terminal’s daily operation to find out specific operational issues and evaluate the main key performance indicators to keep track of the terminal efficiency.

The global supply chain and logistics industry is a self-organizing ecosystem consisting of numerous actors that work together to move goods from end to end. The different stakeholders involved are usually interdependent organizations, like freight forwarders, carriers, terminals, and homeland security agencies and information exchange between them is required to coordinate the activities along the individual transport chains. However, the exchange of information has often been analog, flawed, late, and incomplete. New circumstances, like unprecedented supply chain disruptions, new regulatory requirements around greenhouse gas (GHG) emissions, and generally growing shipper expectations create an urgent need for improved data sharing between actors. New technologies, like digital platforms, networks, and architectures as well as social media, mobile, analytics, cloud, and internet of things (SMACT) have brought some improvements, but not the required holistic digital perspective required or expected. Averages and approximations are usually insufficient to close data gaps, e.g., only primary data allows for accurate GHG emission calculations. Primary data sharing is widely seen as the missing piece of the puzzle. Primary data is data from the source providing an accurate state and picture of a situation. Primary data sharing at scale requires a new form of digital collaboration. We propose a rethink of digital collaboration as a means for broader primary private data sharing for complete end-to-end datasets and data quality, particularly focusing on the sharing of data associated with both transport plans and progress made in the respective movements of goods for more better disruption and carbon footprint management through more accurate calculations of estimated times of arrival (ETA) and GHG emissions. We introduce an example for digital collaboration in end-to-end supply chains that is focusing specifically on primary data sharing. The new thinking around digital collaboration manifests itself in the Virtual Watch Tower / VWT initiative (www.virtualwatchtower.org). In 2022, RISE and Singapore Maritime Institute signed a collaboration agreement focused on innovation in shipping. The VWT initiative is the first collaborative project under the umbrella of this partnership. The VWT is led by RISE, A*STAR, and VTT. VWT is a community-driven, digitally empowered initiative, a cargo owner-driven, and terminal-centric approach for improved supply chain management. It is the users themselves who co-create and co-evolve the solution that they need. The initiative aims to create a community that shapes the digital tool (VWTnet) they need to reach the required higher levels of visibility. This, through primary data sharing across the supply chain ecosystem and between actors (VWT Users) participating in individual end-to-end transports (VWT Shipments). The VWT serves as an object of research to hone the new thinking and understand the implications of its implementation.