Daily drayage operations involve moving loaded or empty equipment between customer locations and rail ramps. Drayage orders are generally pickup and delivery requests with time windows. The repositioning of empty equipment may also be required in order to facilitate loaded movements. The drayage orders are satisfied by a heterogeneous fleet of drivers. Driver routes must satisfy various operational constraints. In the first part of the dissertation, our goal is to minimize the cost of daily drayage operations in a region on a given day. We present an optimization methodology for finding cost-effective schedules for regional daily drayage operations. The core of the formulation is a set partitioning model whose columns represent routes. Routes are added to the formulation by column generation. We present numerical results for real-world data which demonstrate that our methodology produces low cost solutions in a reasonably short time. The second part of the dissertation addresses minimizing total empty mileage when driver capacity is not restrictive and new orders are added to the problem in an online fashion. We present a lower bound for the worst case guarantee of any deterministic online algorithm. We develop a solution methodology and provide results for the performance of different scheduling policies and parameters in a simulated environment. In the third part of the dissertation, we study a system with one rail ramp and one customer location which is served by a single driver. The problem has discrete time periods and at most one new order is released randomly each time period. The objective is to maximize the expected number of orders covered. With this simple problem, we seek to learn more about route planning for a single driver under uncertainty. We prove that carrying out an order ready to be picked up at the driver's current location is optimal for the case with one customer location. We show that the structure of the optimal policies is not simple and depends on various parameters. We devise a simple policy which yields provably near-optimal results and identify a case for which that policy is optimal.

The Port of Oakland (“Port”) is the 5th largest container seaport by volume in the U.S. and the largest in Northern California. Maritime shipping activity at the Port exceeds 2 million import and export twenty-foot equivalent unit (TEU) containers annually. Containers may be full or empty, but nonetheless typically require hinterland shipment and intermodal transfer between maritime and land-based freight distribution systems. The freight trucking mode (“drayage”) handles approximately 80% of all TEU throughput at the Port, thus constituting the majority of landside Port traffic. The Port is also situated adjacent to dense urban development thereby exacting certain external impacts. Drayage impacts on regional roadway infrastructure proximate to the Port are explored, to expand knowledge of freight network conditions and relevant policies addressing the topic in the San Francisco Bay Area. Statistical regression analysis and elasticity results estimate a certain level of impact on nearby freight corridors of I-80, I-680, and I-880. Drayage traffic has continued to increase since 2000, as a function of increasing TEU throughput occurring at the Port. Policies to address stable freight flow and infrastructure maintenance are ongoing, although additional studies are also recommended to ascertain comprehensive network impacts.

This dissertation considers daily operations management for a fleet of trucks providing container pickup and delivery service to a port. Truck congestion at access points for ports may lead to serious inefficiencies in drayage operations, and the resultant cost impact to the intermodal supply chain can be significant. Recognizing that port congestion is likely to continue to be a major problem for drayage operations given the growing volume of international containerized trade, this research seeks to develop optimization approaches for maximizing the productivity of drayage firms operating at congested seaports. Specifically, this dissertation addresses two daily drayage routing and scheduling problems. In the first half of this dissertation, we study the problem of managing a fleet of trucks providing container pickup and delivery service to a port facility that experiences different access wait times depending on the time of day. For this research, we assume that the wait time can be estimated by a deterministic function. We develop a time-constrained routing and scheduling model for the problem that incorporates the time-dependent congestion delay function. The model objective is to find routes and schedules for drayage vehicles with minimum total travel time, including the waiting time at the entry to the port due to congestion. We consider both exact and heuristic solution approaches for this difficult optimization problem. Finally, we use the framework to develop an understanding of the potential impact of congestion delays on drayage operations, and the value of planning with accurate delay information. In the second half of this dissertation, we study methods for managing a drayage fleet serving a port with an appointment-based access control system. Responding to growing access congestion and its resultant impacts, many U.S. port terminals have implemented appointment systems, but little is known about the impact of such systems on drayage productivity. To address this knowledge gap, we develop a drayage operations optimization approach based on a column generation integer programming heuristic that explicitly models a time-slot port access control system. The approach determines pickup and delivery sequences with minimum transportation cost. We use the framework to develop an understanding of the potential efficiency impacts of access appointment systems on drayage operations. Findings indicate that the set of feasible drayage tasks and the fleet size required to complete them can be quite sensitive to small changes in time-slot access capacities at the port.