A case based reasoning based multi-agent system for the reactive container stacking in seaport terminals

Abstract With the continuous development of seaports, problems related to the storage of containers in terminals have emerged. Unfortunately, existing systems suffer limitations related to the distributed monitoring and control, real-time stacking strategies efficiency and their ability to handle dangerous containers. In this paper, we suggest a multi-agent architecture based on a set of knowledge models and learning mechanisms for disturbance and reactive decision making management. The suggested system is able to capture, store and reuse knowledge in order to detect disturbances and select the most appropriate container location by using a Case Based Reasoning (CBR) approach. The proposed system takes into account the storage of dangerous containers and combines Multi-Agent Systems (MAS) and case based reasoning to handle different types of containers.