Principal Investigator(s): Bruce Wang (TAMU), Yunlong Zhang (TAMU), Lee Han (UTK), Bo Zou (UIC)
Project Partners: Wal-Mart, Tennessee Department of Transportation, Oak Ridge National Lab
The US freight network is a complex and dynamic system consisting of highway, railway, pipelines, maritime, air, and their intermodal operations. A traffic jam at one location pushes freight to alternative freight routes while altering highway and railway freight traffic as a result. The failure of any one of the major Mississippi River crossings, such as the Hernando de Soto Bridge on I-40, can cause major delay not just to local interstate traffic but also long detours and days of delay to the US logistics. This project, as the first installment of a five-year effort, will evaluate existing models, identify capability gaps, set research priorities, assess/acquire relevant freight data, and chart the research roadmap forward to eventually adapt and develop a comprehensive set of models and algorithms representing the Nation’s multimodal network. Issues such as larger supply chain locational distribution and uncertainties, resiliency under interruption of services, global impacts, and local realities in the wake of geopolitical shifts and climate changes, etc. can be modeled with this set of tools. The goal is to improve freight system efficiency and resiliency to better serve the national economy and security.
Principal Investigator(s): Mingzhou Jin (UTK) and Shailesh Chandra (CSULB)
Project Partners: Port of Long Beach, Southwest Supply Chain Coalition, California State Transportation Agency, Nevada Governor’s Office of Economic Development, Union Pacific
This project is to develop an integrated intermodal transportation and logistics network in the Southwest to make the American supply chain resilient and sustainable. This project addresses the current supply chain disruptions at and around California ports and fundamentally improves environmental, economic, and social sustainability. This project will develop decision-making models and algorithms to design intermodal transportation networks and operations in Southwest U.S. that will implement the proposed intermodal solution to move containers directly from ports to inland warehouses or intermodal terminals. The benefits of the proposed intermodal solution to environmental, economic, and social sustainability will be quantified for all stakeholders and involved communities. Specially, models and analyses will show railroads that actually they can make money for short-haul businesses in this intermodal system due to the lower loading costs, no need of classification, simple management, large volume, and good work-life balance for conductors and rail engineers. Together, UT and CSULB will accomplish the following objectives to achieve this goal.
Principal Investigator(s): Salvador Hernandez (OSU), Lee Han (UTK), Faisal Alkaabneh (NCAT)
Project Partners: Oregon, Idaho, and Washington DOTs, Robinsight, EROAD, Portland Metro, Tennessee DOT (TDOT), Oak Ridge National Laboratory (ORNL), North Carolina DOT (NCDOT)
Disasters on the transportation network raise awareness of the need to plan for quick mobility and recovery whether they are due to human error, human intent, or nature. Therefore, understanding how resilient a network is to such events provides opportunities for transportation agencies to better prepare. Resilience measures then become a useful tool to evaluate and predict impacts of disruptions and recovery to guide investment decisions to protect against these events. When it comes to freight network system measurements there are two major challenges the states and other agencies face: (1) the absence of data and (2) the lack of methods of analysis. There are robust data for the movement of people and passenger vehicles but understanding the way freight moves presents different types of challenges to decision-makers especially under disruptions scenarios. These movements are based upon supply chain decisions made by individual corporations, which quite often change over time due to various economic conditions. Freight often moves across numerous jurisdictions and by multiple modes of transport (e.g., air, rail, water/marine, and truck). Data that captures origins and destinations, as well as methodologies of collecting and utilizing data across multiple jurisdictions and modes, are extremely limited for freight. Currently, decision-makers are only able to use a few data sources that help in identifying freight movements among States and regions, commodities, tonnage, and value.
Principal Investigator(s): Hector Vergara (OSU) and Salvador Hernandez (OSU)
Project Partners: Oregon DOT, EROAD (Robinsight)
At the strategic level, private sector companies develop their distribution systems through the location of manufacturing plants, distribution centers, and warehouses, largely based on the established multimodal network infrastructure. In many cases, private distribution systems function in the context of global or national supply chains, which adds complexity to their planning and decision making. Accordingly, the private sector’s distribution system efficiency depends on the inherent resiliency and efficiency of the multimodal network. For this reason, it is important to develop a framework to quantify the impact of the multimodal freight transportation network on the efficiency of private distribution systems. This research project will develop network models to quantify the impact of multimodal network components on private sector efficiencies in terms of operational cost. This is envisioned as a project to be completed in phases, with the first phase focusing on the development and testing of a network model for multimodal freight transportation that would be used to assess performance metrics that are relevant for the planning and operation of distribution systems.
Principal Investigator(s): Lee Han (UTK), Bruce Wang (TAMU), Yunlong Zhang (TAMU), and Kouros Mohammadian (UIC)
Project Partners: US DOT freight offices, FAF program, TxDOT freight program, Walmart R&D Center, BNSF, Oak Ridge National Laboratory
Freight flows on a multimodal network and through alternative routes. Each mode and route decision are determined by shipper behavior. There are multiple factors behind shipper behaviors such as time, distance, cost, reliability, etc., each of which is related to the characteristics of the commodity being shipped. To effectively promote multimodal transportation requires in-depth understanding of shipper behavior, which is also critical to the planning and operations of the multimodal transportation system. When a key infrastructure element is disrupted such as a port closeout, how would the O-D flow respond to the lockout of a major port? There may be multiple alternative routes with different set of modes of transportation. In this case, shipper behavior study would help predict the potential distribution of the network flow and assist planners and operational managers in developing proper policies and measure to serve the stakeholders better.
Principal Investigator(s): Bo Zou (UIC), Kazuya Kawamura (UIC), P.S. Sriraj (UIC), Jane Lin (UIC), Mingzhou Jin (UTK)
Project Partners: Illinois DOT, Amazon, and Kenco Logistics
Middle mile logistics, particularly drayage – a specific type of middle mile operation dealing with short-distance movements between transportation hubs and nearby facilities – presents a critical component in the national supply chain. Despite representing a small fraction of the total distance covered in intermodal shipments, drayage incurs a disproportionately large share of the overall shipping cost. In addition, when drayage movements occur in urban areas, they can exacerbate congestion on crowded urban road networks. The emergence of vehicle automation offers exciting opportunities to improve the efficiency, resiliency, and sustainability of drayage operations, yet it has not received adequate research attention. To address this gap, our project will use a mixed method approach – combining qualitative and quantitative research – to assess the potential of automation for drayage operations. On the qualitative side, we will carry out interviews and focus groups with stakeholders from prominent freight hubs, such as the Chicago metro region, to obtain practitioners’ perspectives on how automation can enhance middle-mile logistics. Through this, we aim to glean valuable insights about possible deployment scenarios, and the challenges and opportunities of automation in drayage operations.
Project 7: Impact of Automated Port Operations on Landslide Freight Corridor Performance: Opportunities, Barriers, and Future Directions with the Port of Long Beach
Principal Investigator(s): Shailesh Chandra (CSULB) and Kevin Heaslip (UTK)
Project Partners: Port of Long Beach, Caltrans, NEXT Trucking, Harbor Trucking Association
Automated terminals at the Port of Long Beach have helped process containers faster. Software-assisted cranes and autonomous vehicles have been crucial to improving efficiencies. However, little is known about how automation at the port synchronizes with the other conventional operations of container transportation in the multimodal freight network consisting of the freeways and highways in the Southern California Region. This research will develop tools to assess the status quo of supply chain operations and identify gaps to be filled through research and insights to build an efficient and resilient supply chain operating from the Port of Long Beach to other parts of the nation. Interviews will be conducted with the Port of Long Beach engineering and operations division and the freight truck operators of the region to gather insights into automation integration of the conventional supply chain functioning of container transportation across the trade corridors.
Principal Investigator(s): Luca Quadrifoglio (TAMU), Bruce Wang (TAMU)
Project Partners: NC DOT, GM, UPS, Port of Houston
For months, ports have borne the brunt of the impact from disruptions along the supply chain. The pandemic has exacerbated an already existing problem, as ports and their operations have always been the weakest link of the supply chain network. A significant part of the problem is the scheduling and routing of Automated Guided Vehicles (AGVs), which follow specific logicsto perform loading and unloading operations. This scheduling problem is modeled using highly complex mathematical formulations that are inherently difficult to solve because of their combinatorial nature. Lack of coordination among the vehicles, cranes, trucks at both ends of the port logistics causes significant delays. Uncertainty and unreliable arrival forecasts, along with the lack of proper control mechanisms, cause this lack of synchronization. This research project will explore these challenges and propose innovative solutions to this complex problem by developing algorithms and solution methods.