published_or_final_version / Urban Planning / Master / Master of Science in Urban Planning

published_or_final_version / Transport Policy and Planning / Master / Master of Arts

No description available.

The objective of this research is to implement multi-modal cost calculations on a freight transportation network, in order to estimate the cost of freight shipments from parts suppliers to original equipment manufacturers (OEMs), and from OEMs to final consumers involved in the automobile manufacturing industry supply chain. The research will describe gaps in the current freight cost estimation literature, determine the strengths and weaknesses of current practices, and offer possible improvement strategies. The necessary components for this research include: a multi-modal (highway-rail-water-air) network database, the geocoded locations and activity levels of auto industry parts suppliers and OEMs; freight movement cost functions; information on the modes and vehicle/vessel types used for the shipment of certain commodity types; and distance- based travel costs per-mile for these modes. A product of this line of research will be a method that other industries, in other locations, might also use to determine overall freight transportation costs throughout an entire supply chain. The present research effort provides an example using data gathered on the automobile manufacturing industry centered in Georgia and Alabama. The network-based freight costs derived in this research should also be useful in other applications, including the estimation of origin-to-destination flows, as well as in the estimation of transportation costs used in regional and statewide freight planning models.

When integer programming (IP) models are used in operational situations there is a need to consider the tradeoff between the conflicting goals of solution quality and solution time, since for many problems solving realistic-size instances to a tight tolerance is still beyond the capability of state-of-the-art solvers. However, by appropriately defining small instances, good primal solutions frequently can be found quickly. We explore this approach in this thesis by studying the design of algorithms that produce solutions to an integer program by solving restrictions of the problem via integer programming technology. We refer to this type of algorithm as IP-based search and present algorithms for network design problems of both real-world and academic interest. Along with algorithms that exploit the structure of the problem studied we also present a general integer programming algorithm that uses column generation to choose the restrictions to solve.

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.

Thesis (M.S.)--Rochester Institute of Technology, 2009. / Typescript. Includes bibliographical references.

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published_or_final_version / abstract / toc / Transport Policy and Planning / Master / Master of Arts in Transport Policy and Planning

In the early 1920s, discontent about freight rates led to a number of major political battles. Statistical analysis and a review of contemporary reports suggest that the economic importance of these issues was greatly exaggerated. They were blamed for problems which were mainly attributable to such factors as the world-wide recession, the movement of the terms of trade against agricultural producers or the trend towards industrial concentration. / Examination of official documents and private papers indicates that government policy-making on freight rates was influenced by political priorities (especially winning back the West), lobbying by affected interests and the opinions of strong-minded individuals. The King Government intervened in the question of railway rates, but its procrastination, obfuscation and reversals of policy reduced the political credit it received for the concessions it decided to make. Its abortive initiatives on lake and ocean rates were empty political gestures, made out of ignorance of the economics of these complex issues.