Experiments studying the behavior of agent based methods over varying levels of uncertainty in comparison to traditional optimization methods are generally absent from the literature. In this paper we apply two structurally distinct solution approaches, an on-line optimization and an agent based approach, to a drayage problem with time windows under two types of uncertainty. Both solution approaches are able to respond to dynamic events. The on-line optimization approach utilizes a mixed integer program to obtain a feasible route at 30-second intervals. The second solution approach deploys agents that engage in auctions to satisfy their own objectives based on the information they perceive and maintain locally. Our results reveal that the agent-based system can out perform the online optimization when service time duration is highly uncertain. The on-line optimization approach, on the other hand, performs competitively with the agent-based system under conditions of job arrival uncertainty. When both moderate service time and job arrival uncertainties are combined, the agent system outperforms the on-line optimization; however, in the case of extremely high combined uncertainty, the on-line optimization outperforms the agent-based approach.
The simulation framework presented in this paper is an extension of an earlier on-line optimization and agent-based comparison REF .
Can agents measure up? A comparative study of an agent-based and on-line optimization approach for a drayage problem with uncertainty
Engineering