Self-Learning Problem Prioritization for Operating Tugger Train Systems Wuddi Philipp Fottner Johannes 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 evaluate, which present deviation in the logistic system should ideally be subject to the reaction of the control system. Several ideas are part of the discussion and narrow down to four different approaches. An overall evaluation and a synthesis of the individual approaches to a universally valid and applicable approach follow. Furthermore, future possibilities for enhancement complete the paper Leitsysteme Logistiksteuerung control systems decision-making logistics control operational control operative Steuerung selbstlernende Systeme self-learning systems 620 periodical academic journal Logistics Journal : Proceedings 2021 17 2021 2192-9084 urn:nbn:de:0009-14-54492 10.2195/lj_Proc_wuddi_en_202112_01 http://nbn-resolving.de/urn:nbn:de:0009-14-54492 wuddi2021