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