An Examination of Railroad Capacity and its Implications for Rail-Highway Intermodal Transportation

After many years of decline in market share, railroads are now experiencing an increasing demand for their services. Service intensive intermodal transportation seems to be an especially promising market area. Since the historic decline in traffic has been accompanied by a reduction in network infrastructure, however, the railroads\u27 ability to handle sizable traffic increases, at least in the short term, is in question. Since rail transportation is critical to the domestic economy of the nation, and is increasingly important in international logistics channels, shortfalls in railroad capacity are not desirable. The published literature on railroad capacity is relatively sparse, especially in comparison to the highway mode. Much of what is available pertains to individual network components such as lines or terminals. Evaluation of system capacity, considering the interactive effects of traffic flowing through a network of lines and terminals, has received less attention. A tool specifically designed for evaluating freight railroad system capacity issues could be a useful addition to the rail analyst\u27s toolbox. The research conducted in this study resulted in the formulation and application of RAILNET, a multicomrnodity, multicarrier network model for predicting equilibrium flows within a railroad network. Designed for strategic planning with a short term horizon, the model assumes fixed external demand. The predicted flows meet the conditions for Wardropian system equilibrium. At completion, the solution algorithm predicts the expected delay per train on each link, allowing the analyst to identify areas of congestion. Following completion of the model, it was applied to a case study examining the railroad network in the southeastern U.S. The public use version of the Interstate Commerce Commission\u27s Commodity Waybill Sample (CWS) provided flow data. The dissertation describes the procedure used to develop the case study and presents some results. The case points to major deficiencies in the CWS data which resulted in substantially less traffic in the network than is actually present. In general, given this limitation, the model behaved well and results appear reasonable, although not necessarily reflective of actual network conditions

Robust and stochastic approaches to network capacity design under demand uncertainty

This thesis considers the network capacity design problem with demand uncertainty using the stochastic, robust and distributionally robust stochastic optimization approaches (DRSO). Network modeling in itself has found wide areas of application in most fields of human endeavor. The network would normally consist of source (origin) and sink (destination) nodes connected by arcs that allow for flows of an entity from the origin to the destination nodes. In this thesis, a special type of the minimum cost flow problem is addressed, the multi-commodity network flow problem. Commodities are the flow types that are transported on a shared network. Offered demands are, for the most part, unknown or uncertain, hence a model that immune against this uncertainty becomes the focus as well as the practicability of such models in the industry. This problem falls under the two-stage optimization framework where a decision is delayed in time to adjust for the first decision earlier made. The first stage decision is called the "here and now", while the second stage traffic re-adjustment is the "wait and see" decision. In the literature, the decision-maker is often believed to know the shape of the uncertainty, hence we address this by considering a data-driven uncertainty set. The research also addressed the non-linearity of cost function despite the abundance of literature assuming linearity and models proposed for this. This thesis consist of four main chapters excluding the "Introduction" chapter and the "Approaches to Optimization under Uncertainty" chapter where the methodologies are reviewed. The first of these four, Chapter 3, proposes the two models for the Robust Network Capacity Expansion Problem (RNCEP) with cost non-linearity. These two are the RNCEP with fixed-charge cost and RNCEP with piecewise-linear cost. The next chapter, Chapter 4, compares the RNCEP models under two types of uncertainties in order to address the issue of usefulness in a real world setting. The resulting two robust models are also comapared with the stochastic optimization model with distribution mean. Chapter 5 re-examines the earlier problem using machine learning approaches to generate the two uncertainty sets while the last of these chapters, Chapter 6, investigates DRSO model to network capacity planning and proposes an efficient solution technique

Decomposition methods for large-scale network expansion problems

Network expansion problems are a special class of multi-period network design problems in which arcs can be opened gradually in different time periods but can never be closed. Motivated by practical applications, we focus on cases where demand between origin-destination pairs expands over a discrete time horizon. Arc opening decisions are taken in every period, and once an arc is opened it can be used throughout the remaining horizon to route several commodities. Our model captures a key timing trade-off: the earlier an arc is opened, the more periods it can be used for, but its fixed cost is higher, since it accounts not only for construction but also for maintenance over the remaining horizon. An overview of practical applications indicates that this trade-off is relevant in various settings. For the capacitated variant, we develop an arc-based Lagrange relaxation, combined with local improvement heuristics. For uncapacitated problems, we develop four Benders decompositi

Integrated network flow model for a reliability assessment of the national electric energy system

Electric energy availability and price depend not only on the electric generation and transmission facilities, but also on the infrastructure associated to the production, transportation, and storage of coal and natural gas. As the U.S. energy system has grown more complex and interdependent, failure or degradation on the performance of one or more of its components may possibly result in more severe consequences in the overall system performance. The effects of a contingency in one or more facilities may propagate and affect the operation, in terms of availability and energy price, of other facilities in the energy grid. In this dissertation, a novel approach for analyzing the different energy subsystems in an integrated analytical framework is presented, by using a simplified representation of the energy infrastructure structured as an integrated, generalized, multi-period network flow model. The model is capable of simulating the energy system operation in terms of bulk energy movements between the different facilities and prices at different locations under different scenarios. Assessment of reliability and congestion in the grid is performed through the introduction and development of nodal price-based metrics, which prove to be especially valuable for the assessment of conditions related to changes in the capacity of one or more of the facilities. Nodal price-based metrics are developed with the specific objectives of evaluating the impact of disruptions and of assessing capacity expansion projects. These metrics are supported by studying the relationship between nodal prices and congestion using duality theory. Techniques aimed at identifying system vulnerabilities and conditions that may significantly impact availability and price of electrical energy are also developed. The techniques introduced and developed through this work are tested using 2005 data, and special effort is devoted to the modeling and study of the effects of hurricanes Katrina and Rita in the energy system. In summary, this research is a step forward in the direction of an integrated analysis of the electric subsystem and the fossil fuel production and transportation networks, by presenting a set of tools for a more comprehensive assessment of congestion, reliability, and the effects of disruptions in the U.S. energy grid

Economic efficiencies of the energy flows from the primary resource suppliers to the electric load centers

The economic efficiency of the electric energy system depends not only on the performance of the electric generation and transmission subsystems, but also on the ability to produce and transport the various forms of primary energy, particularly coal and natural gas. However, electric power systems have traditionally been developed and operated without a conscious awareness of the energy system-wide implications, namely the consideration of the integrated dynamics with the fuel markets and infrastructures. This has been partly due to the difficulty of formulating models capable of analyzing the large-scale, complex, time-dependent, and highly interconnected behavior of the integrated energy system. In this dissertation, a novel approach for studying the movements of coal, natural gas, and electricity in an integrated fashion is presented. Conceptually, the model developed is a simplified representation of the national infrastructures, structured as a generalized, multiperiod network composed of nodes and arcs. Under this formulation, fuel supply and electricity demand nodes are connected via a transportation network and the model is solved for the most efficient allocation of quantities and corresponding prices for the mutual benefits of all. The synergistic action of economic, physical, and environmental constraints produces the optimal pattern of energy flows. Key data elements are derived from various publicly available sources, including publications from the Energy Information Administration, survey forms administered by the Federal Energy Regulatory Commission, and databases maintained by the Environmental Protection Agency. The results of different test cases are analyzed to demonstrate that the decentralized level of decision-making combined with imperfect competition may be preventing the realization of potential cost savings. An overall optimization at the national level shows that there are opportunities to better utilize low cost generators, curtailing usage of higher cost units and increasing electric power trade, which would ultimately allow customers to benefit from lower electricity prices. In summary, the model developed is a simulation tool that helps build a better understanding of the complex dynamics and interdependencies of the coal, natural gas, and electricity networks. It enables public and private decision makers to carry out comprehensive analyses of a wide range of issues related to the energy sector, such as strategic planning, economic impact assessment, and the effects of different regulatory regimes