Locomotive Planning at Norfolk Southern: An Optimizing Simulator Using Approximate Dynamic Programming

Abstract

For decades, locomotive planning has been approached using the classical tools of mathematical programming; the result has been very large-scale integer programming models that are beyond the capabilities of modern solvers but still require a host of simplifying assumptions that limit their use for analyzing important planning problems. The primary interest of Norfolk Southern was in developing a model that could assist it with fleet sizing. However, the cumulative effect of the simplifications required to produce a practical integer programming formulation resulted in models that underestimated the required fleet. We use the modeling and algorithmic framework of approximate dynamic programming, which uses an intuitive balance of simulation and optimization with feedback learning, to produce a highly detailed model that calibrates accurately against historical metrics. The result was a model that can be used to plan fleet size and mix, be sensitive to a wide range of operating parameters, and adapt to many scenarios.

An improved method of determining the schemes of locomotive circulation with regard to the technological peculiarities of railcar traffic

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A New Separable Piecewise Linear Learning Algorithm for the Stochastic Empty Container Repositioning Problem

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“…Powell et al (2014) define only two levels of the locomotive assignment problem. They define the strategic level problem as estimating the appropriate fleet size and mix, given a projected train schedule.…”