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Approximate Dynamic Programming Captures Fleet Operations for Schneider National

Author

Listed:
  • Hugo P. Simão

    (Princeton University, Princeton, New Jersey 08544)

  • Abraham George

    (Princeton University, Princeton, New Jersey 08544)

  • Warren B. Powell

    (Princeton University, Princeton, New Jersey 08544)

  • Ted Gifford

    (Schneider National, Green Bay, Wisconsin 54306)

  • John Nienow

    (Schneider National, Green Bay, Wisconsin 54306)

  • Jeff Day

    (Schneider National, Green Bay, Wisconsin 54306)

Abstract

Schneider National needed a simulation model that would capture the dynamics of its fleet of over 6,000 long-haul drivers to determine where the company should hire new drivers, estimate the impact of changes in work rules, find the best way to manage Canadian drivers, and experiment with new ways to get drivers home. It needed a model that could perform as well as its experienced team of dispatchers and fleet managers. In developing our model, we had to simulate drivers and loads at a high level of detail, capturing both complex dynamics and multiple forms of uncertainty. We used approximate dynamic programming to produce realistic, high-quality decisions that capture the ability of dispatchers to anticipate the future impact of decisions. The resulting model closely calibrated against Schneider's historical performance, giving the company the confidence to base major policy decisions on studies performed using the model. These policy decisions helped Schneider to avoid costs of $30 million by identifying problems with a new driver-management policy, achieve annual savings of $5 million by identifying the best driver domiciles, reduce the number of late deliveries by more than 50 percent by analyzing service commitment policies, and save $3.8 million annually by reducing training expenses for new border-crossing regulations.

Suggested Citation

  • Hugo P. Simão & Abraham George & Warren B. Powell & Ted Gifford & John Nienow & Jeff Day, 2010. "Approximate Dynamic Programming Captures Fleet Operations for Schneider National," Interfaces, INFORMS, vol. 40(5), pages 342-352, October.
    • Handle: RePEc:inm:orinte:v:40:y:2010:i:5:p:342-352
    DOI: 10.1287/inte.1100.0510
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    References listed on IDEAS

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    1. Hugo P. Simão & Jeff Day & Abraham P. George & Ted Gifford & John Nienow & Warren B. Powell, 2009. "An Approximate Dynamic Programming Algorithm for Large-Scale Fleet Management: A Case Application," Transportation Science, INFORMS, vol. 43(2), pages 178-197, May.
    2. Marar, Arun & Powell, Warren B., 2009. "Capturing incomplete information in resource allocation problems through numerical patterns," European Journal of Operational Research, Elsevier, vol. 197(1), pages 50-58, August.
    3. Gregory A. Godfrey & Warren B. Powell, 2002. "An Adaptive Dynamic Programming Algorithm for Dynamic Fleet Management, I: Single Period Travel Times," Transportation Science, INFORMS, vol. 36(1), pages 21-39, February.
    4. Huseyin Topaloglu & Warren B. Powell, 2006. "Dynamic-Programming Approximations for Stochastic Time-Staged Integer Multicommodity-Flow Problems," INFORMS Journal on Computing, INFORMS, vol. 18(1), pages 31-42, February.
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    Cited by:

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    2. Rempel, M. & Cai, J., 2021. "A review of approximate dynamic programming applications within military operations research," Operations Research Perspectives, Elsevier, vol. 8(C).
    3. Ilya O. Ryzhov & Martijn R. K. Mes & Warren B. Powell & Gerald van den Berg, 2019. "Bayesian Exploration for Approximate Dynamic Programming," Operations Research, INFORMS, vol. 67(1), pages 198-214, January.
    4. Gu, Liyi & Ryzhov, Ilya O. & Eftekhar, Mahyar, 2021. "The facts on the ground: Evaluating humanitarian fleet management policies using simulation," European Journal of Operational Research, Elsevier, vol. 293(2), pages 681-702.
    5. Mercedes Esteban-Bravo & Jose M. Vidal-Sanz & Gökhan Yildirim, 2014. "Valuing Customer Portfolios with Endogenous Mass and Direct Marketing Interventions Using a Stochastic Dynamic Programming Decomposition," Marketing Science, INFORMS, vol. 33(5), pages 621-640, September.
    6. Song, Haiqing & Cheung, Raymond K. & Wang, Haiyan, 2014. "An arc-exchange decomposition method for multistage dynamic networks with random arc capacities," European Journal of Operational Research, Elsevier, vol. 233(3), pages 474-487.
    7. 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.
    8. Ye Chen & Ilya O. Ryzhov, 2020. "Technical Note—Consistency Analysis of Sequential Learning Under Approximate Bayesian Inference," Operations Research, INFORMS, vol. 68(1), pages 295-307, January.
    9. Phares, Jonathan & Miller, Jason W. & Burks, Stephen V., 2023. "State-Level Trucking Employment and the COVID-19 Pandemic in the U.S: Understanding Heterogenous Declines and Rebounds," IZA Discussion Papers 16265, Institute of Labor Economics (IZA).
    10. Michael F. Gorman, 2016. "From Magnum Opus to Mea Culpa: A Cautionary Tale of Lessons Learned from a Failed Decision Support System," Interfaces, INFORMS, vol. 46(2), pages 183-195, April.
    11. Warren B. Powell, 2016. "Perspectives of approximate dynamic programming," Annals of Operations Research, Springer, vol. 241(1), pages 319-356, June.
    12. 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).
    13. Antoine Sauré & Jonathan Patrick & Martin L. Puterman, 2015. "Simulation-Based Approximate Policy Iteration with Generalized Logistic Functions," INFORMS Journal on Computing, INFORMS, vol. 27(3), pages 579-595, August.

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