Optimization of intermodal rail operations and locomotive fleet management

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Canadian railway companies are both network and terminal operators and hence face many complex large-scale optimization problems. Many of these problems have been studied in the operations research (OR) literature for decades but the gap between the state-of-the-art and the state-of-practice is huge. In addition, both the scientific literature and the current industrial practice tend to treat decision problems in isolation of each other and most assume deterministic settings in which all input data are known with certainty. Uncertainty is particularly important as it affects planning and operations on a continuous basis. Decision support tools should thus not only take this uncertainty into account at the planning level but also provide ways to handle deviations from planned operations at the execution level. In this project we deal with two complementary families of problems currently faced in intermodal and freight rail transportation. The first family of problems concerns the planning and operation of intermodal rail transportation. We focus on a tactical network load block planning problem and three related issues: demand forecasting, load planning and track planning. The second family concerns the management of the locomotive fleet that is used to haul all types of cargo, including intermodal. The project addresses two specific problems: locomotive assignment and locomotive routing. Intermodal container freight transportation is the backbone of international trade, as well as of a large part of Canadian and North-American imports and exports. In this context, Canada benefits from having the largest rail network in the world to support its economic development. Rail is also the most environmentally friendly land freight transport mode. It is therefore not only beneficial to the Canadian railway industry but also to the Canadian society to make this mode of transport more efficient and cost effective. The results of this project can contribute to decreased delivery times, costs, and environmental footprint. Furthermore, developing decision aid tools that are based on mathematical models can help personnel in the railway industry make more informed decisions which, in turn, can lead to increased safety.