ON THE USE OF CASE-BASED WORKING-PRINCIPLES OF SELF-OPTIMIZATION FOR INTELLIGENT SHUTTLE TRANSPORTATION SYSTEMS

摘要

The demand for flexible and individualized passenger and cargo transportation becomes more and more apparent. Considering modern railway-systems this development provokes a modular approach for optimum operation from the top logistics-level down to the low-level of mechatronic components. The resulting vision comprises innovative vehicles - so called shuttles - that behave autonomously within changing environments and requests from passengers. The shuttles require inherent intelligence to fulfill these demands. We propose working-principles of self-optimization as basic building blocks for intelligent self-optimizing transportation systems. An approach for a knowledge model together with a process model of self-optimizing systems shall direct the experience-based behavior of each system element. The prediction of a shuttle's negotiation-strategy when crossing a switch acts as an application case for the forwarded idea of working-principles of self-optimization within modular autonomous shuttle transportation systems.