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Monday, October 14 • 4:30pm - 4:45pm
RL in Vehicle Routing and Scheduling

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Historically, transportation companies have relied on either (1) scheduling solutions rooted in heuristic-based linear programming where a small set of rules reduce an overwhelming set of computational complexity or (2) rooms full of human schedulers with spreadsheets, 3-ring binders and post-it notes. In either case, companies struggle to maximize revenue miles while efficiently managing empty vehicle movement, personnel turnover and cost, and meeting market demand. In this discussion we will review the use of multi-agent reinforcement learning to significantly improve on current market solutions. We will discuss the simulation of transportation requests based on historical data, the state and action spaces, rewards assignment and supporting technology infrastructure.


Brian Thompson

Presenter, Expero Inc.
avatar for Ryan Brady

Ryan Brady

Expero Inc.

Daniel Fay

Expero Inc.

Emmett Bertram

Expero Inc.

Monday October 14, 2019 4:30pm - 4:45pm CDT
BRC 280

Attendees (4)