  Truckload brokerages, a $100 billion/year industry in the U.S., plays the
critical role of matching shippers with carriers, often to move loads several
days into the future. Brokerages not only have to find companies that will
agree to move a load, the brokerage often has to find a price that both the
shipper and carrier will agree to. The price not only varies by shipper and
carrier, but also by the traffic lanes and other variables such as commodity
type. Brokerages have to learn about shipper and carrier response functions by
offering a price and observing whether each accepts the quote. We propose a
knowledge gradient policy with bootstrap aggregation for high-dimensional
contextual settings to guide price experimentation by maximizing the value of
information. The learning policy is tested using a carefully calibrated fleet
simulator that includes a stochastic lookahead policy that simulates fleet
movements, as well as the stochastic modeling of driver assignments and the
carrier's load commitment policies with advance booking.
