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

Warren B. Powell (), Belgacem Bouzaiene-Ayari (), Coleman Lawrence (), Clark Cheng (), Sourav Das () and Ricardo Fiorillo ()
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Warren B. Powell: Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08544
Belgacem Bouzaiene-Ayari: Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08544
Coleman Lawrence: Norfolk Southern Corporation, Atlanta, Georgia 30309
Clark Cheng: Norfolk Southern Corporation, Atlanta, Georgia 30309
Sourav Das: Norfolk Southern Corporation, Atlanta, Georgia 30309
Ricardo Fiorillo: Norfolk Southern Corporation, Atlanta, Georgia 30309

Interfaces, 2014, vol. 44, issue 6, 567-578

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.

Keywords: transportation: rail; programming: dynamic; approximate dynamic programming; locomotives (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (7)

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