Multi-period vehicle assignment problem with stochastic transportation order availability

The present work investigates a Dynamic Vehicle Allocation problem (DVAP) which is faced by major forwarding companies active in road transportation. A company owning a limited fleet of vehicles wants to maximize its operational profit over an infinite horizon divided into equal periods (days). The profit stems from revenues for transporting full truckloads (FTL) and from costs derived from waiting idle and moving unladen. A decision leading to a set of actions is made at every period of time and is based on the dispatcher’s information over a restricted horizon, called rolling horizon, as it evolves subsequently period per period. The data provided by the customers concern their prospective loads or requirements for transportation: locations of departure and destination cities, and a unique pick-up period for each transportation order. Moreover, the dispatcher has data regarding travel times between cities, current location and status (unladen or loaded) of trucks. This is known for sure and represents the deterministic part of the problem.