OR Models in Freight Railroad Industry

The transportation sector, especially the rail mode, is a very rich source of optimization problems and has been a primary focus of operations researchers. A plethora of articles dedicated to each transportation mode (rail, road, air, and water) can be found in the literature. Ironically, the industries are still using rules of thumb instead of mathematical models for most of the planning and scheduling processes. There are two main reasons for not using the operations research (OR) models: (i) the highly complex nature of the problems, and (ii) the huge gap between the academic (theoretical) and industrial (practical) approaches. With the steep growth in computational capabilities, we have started developing innovative algorithms by shifting the focus from finding optimal-but-impractical solutions to finding good-and-implementable solutions. We are developing state-of-the-art ideas combining exact approaches such as network flows and mixed integer programming, with heuristics and meta-heuristics such as the tabu search and the very large-scale neighborhood (VLSN) search. In this article, we provide an overview of the railroad planning and scheduling problems at the strategic, tactical, and operational levels. We describe the best algorithms successfully implemented at US railroads, which have the potential of saving hundreds of millions of dollars annually. Keywords: railroad scheduling; locomotive planning; crew scheduling; track maintenance; heuristics; modeling; networks