Approximate dynamic programming captures fleet operations for Schneider National

Hugo P. Simão, Abraham George, Warren Buckler Powell, Ted Gifford, John Nienow, Jeff Day

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Schneider National needed a simulation model that would capture the dynamics of its fleet of over 6,000 longhaul 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.

Original languageEnglish (US)
Pages (from-to)342-352
Number of pages11
JournalInterfaces
Volume40
Issue number5
DOIs
StatePublished - Sep 2010

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Management of Technology and Innovation

Keywords

  • Dynamic programming
  • Freight transportation
  • Simulation
  • Transportation models

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