An Approximate Dynamic Programming Algorithm for Large-Scale Fleet Management

This white paper showcases the remarkable results of our pioneering work in approximate dynamic programming. By developing a sophisticated simulation model, we successfully achieved the following outcomes:

Capturing Real-World Operations: Our model intricately replicated the movements of over 6,000 drivers, providing a high-level detailed simulation of Schneider National's operations.
Matching Historical Performance: The simulation consistently delivered operating statistics that closely mirrored Schneider National's historical performance, validating the model's accuracy.
Accurate Marginal Value Estimation: With a single simulation run, we provided precise estimates of the marginal value of 300 different driver types, allowing for data-driven decision-making.

Join us on a journey where numbers and logistics unite to revolutionize the trucking industry. Download the white paper now!

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

Discover how we crafted a model to simulate 6,000+ drivers' movements with remarkable precision at Schneider National, the USA's largest truckload motor carrier.

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"Carriers have long been seeking network optimization that is both dynamic and holistic, covering all loads, trucks, and drivers simultaneously.”