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

Author

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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)

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.

Suggested Citation

  • Warren B. Powell & Belgacem Bouzaiene-Ayari & Coleman Lawrence & Clark Cheng & Sourav Das & Ricardo Fiorillo, 2014. "Locomotive Planning at Norfolk Southern: An Optimizing Simulator Using Approximate Dynamic Programming," Interfaces, INFORMS, vol. 44(6), pages 567-578, December.
    • Handle: RePEc:inm:orinte:v:44:y:2014:i:6:p:567-578
    DOI: 10.1287/inte.2014.0741
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    References listed on IDEAS

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    1. Balachandran Vaidyanathan & Ravindra K. Ahuja & James B. Orlin, 2008. "The Locomotive Routing Problem," Transportation Science, INFORMS, vol. 42(4), pages 492-507, November.
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    Citations

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    Cited by:

    1. Michael F. Gorman & John-Paul Clarke & Amir Hossein Gharehgozli & Michael Hewitt & René de Koster & Debjit Roy, 2014. "State of the Practice: A Review of the Application of OR/MS in Freight Transportation," Interfaces, INFORMS, vol. 44(6), pages 535-554, December.
    2. Piu, F. & Prem Kumar, V. & Bierlaire, M. & Speranza, M.G., 2015. "Introducing a preliminary consists selection in the locomotive assignment problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 82(C), pages 217-237.
    3. Prashant Premkumar & P. N. Ram Kumar, 2019. "Literature Review of Locomotive Assignment Problem from Service Operations Perspective: The Case of Indian Railways," IIM Kozhikode Society & Management Review, , vol. 8(1), pages 74-86, January.
    4. Rempel, M. & Cai, J., 2021. "A review of approximate dynamic programming applications within military operations research," Operations Research Perspectives, Elsevier, vol. 8(C).
    5. Camilo Ortiz-Astorquiza & Jean-François Cordeau & Emma Frejinger, 2021. "The Locomotive Assignment Problem with Distributed Power at the Canadian National Railway Company," Transportation Science, INFORMS, vol. 55(2), pages 510-531, March.
    6. Guo, Peijun, 2022. "Dynamic focus programming: A new approach to sequential decision problems under uncertainty," European Journal of Operational Research, Elsevier, vol. 303(1), pages 328-336.
    7. Geursen, Izaak L. & Santos, Bruno F. & Yorke-Smith, Neil, 2023. "Fleet planning under demand and fuel price uncertainty using actor–critic reinforcement learning," Journal of Air Transport Management, Elsevier, vol. 109(C).

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