Containerized grain shipping has been increasingly used as a shipment option by U.S. exporters. Continued evolution and investment decisions in optimizing multimodal operations is a key in continued growth for the container transportation alternative. Agriculture is a leading sector in the Midwest economy. Grain production is particularly important to the natural resource-based economy of the upper Midwest. These increasing volumes of grain are being shipped in containers because containers offer opportunities to lower logistics costs and to broaden marketing options. Exporters are put at a competitive disadvantage when they are unable to obtain containers at a reasonable cost. Consequently exporters incur large costs to acquire these empty containers which are repositioned empty, from ports and intermodal hubs. When the import and export customers are located inland, empty repositioning generates excessive unproductive empty miles. To mitigate this shortage of empty containers and avoid excessive empty vehicle miles, this research proposes to strategically establish inland depots in regions with sufficiently high agriculture trade volumes. Mathematical models are formulated to evaluate the proposed system to determine the optimal number and location of inland depots in region under varying demand conditions. An agent-based model simulates the complex regional empty container supply chain based on rational individual decisions. The model provides insight into the role of establishing new depot facilities, have on reducing the empty repositioning miles while increasing the grain exports in the region. Model parameters are used to simulate the impact of train frequency and velocity, truck and rail drayage, demand changes at elevators and depot capacity. For the proposed system, stakeholders will be able to quantify the economic impacts of discrete factors like adjustments of the rail and truck rates and impacts of elevator storage capacity. The initial model is limited to a single state (MN) and export market. It could be enhanced to present a flexible logistical scenario assessment tool which is of great help to make investment decisions for improving the efficiency of multimodal transportation. The model can be applied similarly to other commodities and/or be used to analyze the potential for new intermodal points.