Abstract: A machine learning system (100) for train route optimization includes a machine learning module (120) in communication with an optimization module (110) and including instructions stored in a memory that when executed by a processor (82) cause the machine learning module (120) to receive a plurality of schedules for railroad vehicles travelling through a track network transmitted by the optimization module (110), prioritize the plurality of schedules by applying at least one business rule (140) to the plurality of schedules based on dynamically learned behavior, and provide a prioritized list of schedules.

Abstract: A machine learning system (100) for train route optimization includes a machine learning module (120) in communication with an optimization module (110) and including instructions stored in a memory that when executed by a processor (82) cause the machine learning module (120) to receive a plurality of schedules for railroad vehicles travelling through a track network transmitted by the optimization module (110), prioritize the plurality of schedules by applying at least one business rule (140) to the plurality of schedules based on dynamically learned behavior, and provide a prioritized list of schedules.

Abstract: In a method for the transparent replication of a software component (SWC1) of a software system (SWC1, SWC2), in particular according to the AUTOSAR standard, in a computation system with two or more processing units (VEA, VEB), the processing units (VEA, VEB) are connected to one another, by one or more communication channels (KK1, KK2), for the purpose of interchanging data. Each of the processing units (VEA, VEB) has a runtime environment (RTE) in which respective runtime environments (RTE) of the processing units (VEA, VEB), which are to be replicated, are provided with a synchronization and selection functionality (Sync, Voting).