Choosing Solutions for Current Problems in Operational Logistics Featuring Self-Learning Systems

For the operational control of logistics systems, the application of optimization methods using self-learning algorithms is increasingly the subject of research and development. Knowledge management systems, which address the specific reaction to deviations, i. e. disturbances and fluctuations of system parameters, form a special application use case. This paper discusses in detail, how such a system can prioritize solutions to answer disturbances. Several ideas are part of the discussion on how to identify the most suitable or promising solution to a deviation. Furthermore, these different approaches converge to a single holistic approach via a step between. The rules, individual and holistic, also feature a mathematical expression. Furthermore, future possibilities for enhancement complete the paper.