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From Single Commodity to Multiattribute Models for Locomotive Optimization: A Comparison of Optimal Integer Programming and Approximate Dynamic Programming

Author

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  • Belgacem Bouzaiene-Ayari

    (Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08544)

  • Clark Cheng

    (Norfolk Southern Corporation, Atlanta, Georgia 30309)

  • Sourav Das

    (Norfolk Southern Corporation, Atlanta, Georgia 30309)

  • Ricardo Fiorillo

    (Norfolk Southern Corporation, Atlanta, Georgia 30309)

  • Warren B. Powell

    (Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08544)

Abstract

We present a general optimization framework for locomotive models that captures different levels of detail, ranging from single and multicommodity flow models that can be solved using commercial integer programming solvers, to a much more detailed multiattribute model that we solve using approximate dynamic programming (ADP). Both models have been successfully implemented at Norfolk Southern for different planning applications. We use these models, presented using a common notational framework, to demonstrate the scope of different modeling and algorithmic strategies, all of which add value to the locomotive planning problem. We demonstrate how ADP can be used for both deterministic and stochastic models that capture locomotives and trains at a very high level of detail.

Suggested Citation

  • Belgacem Bouzaiene-Ayari & Clark Cheng & Sourav Das & Ricardo Fiorillo & Warren B. Powell, 2016. "From Single Commodity to Multiattribute Models for Locomotive Optimization: A Comparison of Optimal Integer Programming and Approximate Dynamic Programming," Transportation Science, INFORMS, vol. 50(2), pages 366-389, May.
    • Handle: RePEc:inm:ortrsc:v:50:y:2016:i:2:p:366-389
    DOI: 10.1287/trsc.2014.0536
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    References listed on IDEAS

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    7. Zhou, Shaorui & Zhang, Hui & Shi, Ning & Xu, Zhou & Wang, Fan, 2020. "A new convergent hybrid learning algorithm for two-stage stochastic programs," European Journal of Operational Research, Elsevier, vol. 283(1), pages 33-46.

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