A data driven operations research approach to intermodal rail transportation load and block capacity planning problems

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Intermodal transport refers to the transportation of freight in containers using more than one mode of rail, ship and truck. This project focuses on optimizing the planning and operations of intermodal rail transportation. 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 state-of-the-art and state-of-practice is huge. The railway industry heavily relies on the experience of human resources rather than mathematical models even though the literature reports potential savings in the order of millions of dollars annually by using optimization models. This project addresses this gap by proposing methods to deal with two complementary problems currently faced in intermodal rail transportation, namely, load planning under uncertainty and block capacity planning. We address these problems by combining demand models and OR methods. Canada has one of the largest rail networks in the world and rail is 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 try to increase its market share by making this mode more efficient and cost effective. The methods and algorithms developed in this project will be implemented at the partner organization which is the largest railway company in Canada. The methods are general and can be useful to other North American railway companies. The results can contribute to decreased delivery times and costs. Moreover, developing decision aid tools that are based on mathematical models can help personnel in the railway industry to make more informed decisions, which in turn, can lead to increased safety.