An Approximate Dynamic Programming Algorithm for Large-Scale Fleet Management: A Case Application

  • Authors:

  • Hugo P. Simão;Jeff Day;Abraham P. George;Ted Gifford;John Nienow;Warren B. Powell

  • Affiliations:

  • Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08544;Schneider National, Green Bay, Wisconsin 54306;Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08544;Schneider National, Green Bay, Wisconsin 54306;Schneider National, Green Bay, Wisconsin 54306;Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08544

  • Venue:
  • Transportation Science
  • Year:
  • 2009

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Abstract

We addressed the problem of developing a model to simulate at a high level of detail the movements of over 6,000 drivers for Schneider National, the largest truckload motor carrier in the United States. The goal of the model was not to obtain a better solution but rather to closely match a number of operational statistics. In addition to the need to capture a wide range of operational issues, the model had to match the performance of a highly skilled group of dispatchers while also returning the marginal value of drivers domiciled at different locations. These requirements dictated that it was not enough to optimize at each point in time (something that could be easily handled by a simulation model) but also over time. The project required bringing together years of research in approximate dynamic programming, merging math programming with machine learning, to solve dynamic programs with extremely high-dimensional state variables. The result was a model that closely calibrated against real-world operations and produced accurate estimates of the marginal value of 300 different types of drivers.