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Implementing Real-Time Optimization Models: A Case Application From The Motor Carrier Industry

Author

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  • Warren B. Powell

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

  • Arun Marar

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

  • Jack Gelfand

    (Department of Psychology, Princeton University, Princeton, New Jersey 08544)

  • Steve Bowers

    (Mark VII Logistics)

Abstract

Online models for real-time operations planning face a host of implementation issues that do not arise in more strategic arenas. We use the seemingly simple problem of assigning drivers to loads in the truckload motor carrier industry as an instance to study the issues that arise in the process of implementing a real-time dispatch system. Although the project was moderately successful, our focus is not on documenting the benefits, but rather on summarizing the challenges that arose. The most significant theme running through the implementation hurdles we encountered was the lack of information available to the model. Computers are very good at processing vast quantities of information; humans are very good at challenging the information that is in the computer and augmenting computer-provided data with head knowledge. Our study includes a careful comparison of actual decisions with model recommendations, using a six-month database of actual transactions. This comparison is the first we have seen of its kind and provides the most rigorous evaluation of an online dispatch model that we have seen. Although the model was well used, the results demonstrate that significant improvements could have been obtained if the level of model utilization had been even higher.

Suggested Citation

  • Warren B. Powell & Arun Marar & Jack Gelfand & Steve Bowers, 2002. "Implementing Real-Time Optimization Models: A Case Application From The Motor Carrier Industry," Operations Research, INFORMS, vol. 50(4), pages 571-581, August.
  • Handle: RePEc:inm:oropre:v:50:y:2002:i:4:p:571-581
    DOI: 10.1287/opre.50.4.571.2852
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    References listed on IDEAS

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    Cited by:

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    3. Rempel, M. & Cai, J., 2021. "A review of approximate dynamic programming applications within military operations research," Operations Research Perspectives, Elsevier, vol. 8(C).
    4. Herszterg, Ian & Ridouane, Yassine & Boland, Natashia & Erera, Alan & Savelsbergh, Martin, 2022. "Near real-time loadplan adjustments for less-than-truckload carriers," European Journal of Operational Research, Elsevier, vol. 301(3), pages 1021-1034.
    5. Mortenson, Michael J. & Doherty, Neil F. & Robinson, Stewart, 2015. "Operational research from Taylorism to Terabytes: A research agenda for the analytics age," European Journal of Operational Research, Elsevier, vol. 241(3), pages 583-595.
    6. García, Javier & Florez, José E. & Torralba, Álvaro & Borrajo, Daniel & López, Carlos Linares & García-Olaya, Ángel & Sáenz, Juan, 2013. "Combining linear programming and automated planning to solve intermodal transportation problems," European Journal of Operational Research, Elsevier, vol. 227(1), pages 216-226.
    7. Ozlem Ergun & Gultekin Kuyzu & Martin Savelsbergh, 2007. "Reducing Truckload Transportation Costs Through Collaboration," Transportation Science, INFORMS, vol. 41(2), pages 206-221, May.
    8. 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.
    9. Asvin Goel, 2010. "Truck Driver Scheduling in the European Union," Transportation Science, INFORMS, vol. 44(4), pages 429-441, November.
    10. Allan Larsen & Oli B. G. Madsen & Marius M. Solomon, 2004. "The A Priori Dynamic Traveling Salesman Problem with Time Windows," Transportation Science, INFORMS, vol. 38(4), pages 459-472, November.
    11. Christopher Dance & Alexei Gaivoronski, 2012. "Stochastic optimization for real time service capacity allocation under random service demand," Annals of Operations Research, Springer, vol. 193(1), pages 221-253, March.

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