This dissertation proposes approaches that enable effective planning and control of mobile transportation resources in large-scale consolidation networks. We develop models, algorithms, and methodologies that are applied to fleet sizing and fleet repositioning. Three specific but interrelated problems are studied. The first two relate to the trade-offs between fleet size and repositioning costs in transportation resource management, while the third involves a dynamic empty repositioning problem with explicit consideration of the uncertainty of future requirements that will be revealed over time. Chapter 1 provides an overview of freight trucking, including the consolidation trucking systems that will be the focus of this research. Chapter 2 proposes an optimization modeling approach for analyzing the trade-off between the cost of a larger fleet of tractors and the cost of repositioning tractors for a trucking company operating a consolidation network, such as a less-than-truckload (LTL) company. Specifically, we analyze the value of using extra tractor repositioning moves (in addition to the ones required to balance resources throughout the network) to attain savings in the fixed costs of owning or leasing a tractor fleet during a planning horizon. The primary contributions of the research in this chapter are that (1) we develop the first optimization models that explore the impact of fleet size reductions via repositioning strategies that have regularity and repeatability properties, and (2) we demonstrate that substantial savings in operational costs can be achieved by repositioning tractors in anticipation of regional changes in freight demand. Chapter 3 studies the optimal Pareto frontiers between the fleet size and repositioning costs of resources required to perform a fixed aperiodic or periodic schedule of transportation requests. We model resource schedules in two alternative ways: as flows on event-based, time-expanded networks; and as perfect matchings on bipartite networks. The main contributions from this chapter are that (1) we develop an efficient re-optimization procedure to compute adjacent Pareto points that significantly reduces the time to compute the entire Pareto frontier of fleet size versus repositioning costs in aperiodic networks, (2) we show that the natural extension to compute adjacent Pareto points in periodic networks does not work in general as it may increase the fleet size by more than one unit, and (3) we demonstrate that the perfect matching modeling framework is frequently intractable for large-scale instances. Chapter 4 considers robust models for dynamic empty-trailer repositioning problems in very large-scale consolidation networks. We investigate approaches that deploy two-stage robust optimization models in a rolling horizon framework to address a multistage dynamic empty repositioning problem in which information is revealed over time. Using real data from a national package/parcel express carrier, we develop and use a simulation to evaluate the performance of repositioning plans in terms of unmet loaded requests and execution costs. The main contributions from this chapter are that (1) we develop approaches for embedding two-stage robust optimization models within a rolling horizon framework for dynamic empty repositioning, (2) we demonstrate that such approaches enable the solution of very large-scale instances, and (3) we show that less conservative implementations of robust optimization models are required within rolling horizon frameworks. Finally, Chapter 5 summarizes the main conclusions from this dissertation and discusses directions for further research.

This thesis considers the design and analysis of humanitarian supply chains, by which we mean those systems that deliver goods and services in response to natural or man-made disasters as well as ongoing public health challenges. In the first part of the thesis, we introduce a class of problems motivated by humanitarian logistics systems with decentralized decision makers. In contrast to traditional optimization problems in which a centralized planner determines the actions of all entities in the system, decentralized systems are characterized by individual decision makers who make choices to optimize their own objectives and whose actions impact the overall system performance. Decentralized systems often perform poorly in comparison to centralized ones, but centralization is costly or impractical to implement in many circumstances. The goal of this part of the thesis is to characterize the impact of decentralized decision making and identify ways to mitigate this impact. Using concepts from optimization and game theory, we model systems in which individuals choose a facility to visit to receive service, such as during a disaster response, making their choices based on travel time, congestion, and weights on congestion. These weights represent the relative importance individuals place on congestion in their objectives. We provide an efficient algorithm for finding a stable, or equilibrium, solution from which no individual can improve her own objective value by switching unilaterally. We show that the worst- and best-case performances of decentralized solutions depend on the importance individuals place on congestion. Finally, we introduce a mechanism under which the central optimal solution is also an equilibrium. The mechanism acts by influencing the importance individuals place on congestion, and we characterize the values that this importance can and must be to achieve stability. We introduce models to find values of the mechanism that optimize particular policy objectives and show that these models can be solved efficiently. The second part of the thesis describes the application of the ideas developed in the first part to data from a large-scale effort to deliver a limited supply of products to a large number of people in a short time. The goal of this part of the thesis is to understand the impact of decentralized decision making on local access to an actual product and quantify correlations between inequities in access and socioeconomic variables. We find that both the centralized and decentralized systems lead to inequity in access, but the impact is greater in decentralized systems with user choice. The differences in access are correlated with several socioeconomic variables, but these relationships vary across geographic space. This study integrates tools from optimization, game theory, spatial statistics, and geographic information systems in a novel way. The results confirm the importance of accounting for decentralized behavior in system design and point to opportunities to use the mechanism from the first part of the thesis in future distribution efforts of this nature. The study also leads to policy recommendations, namely that planners consider the impact on equity prior to implementing distribution plans and work to recruit additional service providers in areas that have exhibited inequities in the past. The third part of the thesis employs empirical methods to characterize a successful humanitarian supply chain and identify practices from which other organizations can learn to improve their operations. The hurricane response process used by Waffle House Restaurants has been recognized nationally for its effectiveness. We document the process and describe the supply chain concepts that contribute to its success. Further, we place the company's practices in the context of the literature on supply chain disruption, crisis management, and humanitarian logistics. This study provides insight for other organizations that seek to improve their resilience to supply chain disruptions, whether these are caused by natural disasters or other events. The study also led to the creation of teaching materials to help business and engineering students identify the challenges faced in humanitarian supply chains, the ways that operations research methodologies can be used to improve decisions, and the opportunities for cross-learning between humanitarian organizations and the private sector.