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.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/42758 |
| Date | 24 August 2011 |
| Creators | Carbajal Orozco, Jose Antonio |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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