While intermodal transport has the potential to introduce efficiency to the transport network, this transport environment also suffers from a lot of uncertainty at the interface of modes. For example, trucks moving containers to and from a port terminal are often uncertain as to when exactly their container will be released from the ship, from the stack, or from customs. This leads to much difficulty and inefficiency in planning a profitable routing for multiple containers in one day. In this paper, we examine agent-based solutions as a mechanism to handle job arrival uncertainty in the context of a drayage case at the Port of Rotterdam. We compare our agent-based solution approach to a well known on-line optimization approach and study the comparative performance of both systems across four scenarios of varying job arrival uncertainty. We conclude that when less than 50% of all jobs are known at the start of the day then an agent-based approach performs competitively with an on-line optimization approach.