A cooperation of agents in competitive environments is more complicated than in collaborative ones. Both the replanning and reconfiguration play the crucial role in the cooperation and introduce a means for an implementation of a system flexibility. The concepts of commitments, decommitments with the penalties and subcontractions may facilitate e#ective reconfiguration and replanning.

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.

Distributed logistics and transportation is an important and emerging area of application for multi-agent systems, which has recently attracted a lot of research interest. In previous research ([1], [2]) we have proposed and developed novel techniques to deal with some of the challenges and problems in this application domain. In this paper we describe the software system which was built to visualize and demonstrate our multi-agent model.

Abstract. In real-life multi-agent planning problems, long-term plans will often be invalidated by changes in the environment during or after the planning process. When this happens, short-term operational planning and scheduling methods have to be applied in order to deal with these changed situations. In addition to the dynamic environment, in such planning systems we also have to be aware of sometimes conflicting interests of different parties, rendering a centralized approach undesirable. In this paper we investigate two agent-based scheduling architectures where stakeholders are modelled as autonomous agents. We discuss this approach in the context of an interesting airport planning problem: the planning and scheduling of deicing and anti-icing activities. Based on our view that multi-agent scheduling is scheduling combined with agent coordination, these two architectures apply different mechanisms to coordinate the competition of agents over scarce resources: one mechanism based on decommitment penalties, and one based on a more traditional (Vickrey) auction. Experiments show that the auction-based mechanism best respects the preferences of the individual agents, whereas the decommitment mechanism ensures a fairer distribution of delay over the agents. 1

Cooperation of agents in competitive environments is more complicated than in collaborative environments. Both replanning and reconfiguration play a crucial role in cooperation, and introduce a means for implementating a system flexibility. The concepts of commitments, decommitments with penalties and subcontracting may facilitate effective reconfiguration and replanning. Agents in competitive environments are fully autonomous and selfinterested. Therefore the setting of penalties and profit computation cannot be provided centrally. Both the costs and the gain differ from agent to agent with respect to contracts already agreed and resources load. This paper proposes an acquaintance model for contracting in competitive environments and introduces possibilities of reconfigurating in competitive environments as a means of decommitment optimization with respect to resources load and profit maximization. The presented algorithm for contract price setting does not use any centralized knowledge and provides results corresponding to a realistic environment. A simple customerprovider scenario proves this algorithm in competitive contracting.

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