Title
Approximate Dynamic Programming Captures Fleet Operations for Schneider National
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.
Year
DOI
Venue
2010
10.1287/inte.1100.0510
Interfaces
Keywords
Field
DocType
new driver,drivers home,approximate dynamic programming,policy decision,major policy decision,simulation model,fleet operations,new driver-management policy,schneider national,new border-crossing regulation,new way,resulting model,dynamic programming,simulation
Dynamic programming,Complex dynamics,Transportation models,Level of detail,Operations research,Identifying problems,Engineering,Operations management
Journal
Volume
Issue
ISSN
40
5
0092-2102
Citations 
PageRank 
References 
9
0.48
6
Authors
6
Name
Order
Citations
PageRank
Hugo Simão11068.38
Abraham George2393.48
Warren B. Powell31614151.46
Ted Gifford4553.32
John Nienow5401.89
Jeff Day6432.29