Swarm Intelligence Layer to ControlAuto...

The project SWILT focuses on swarms of cyber-physical sys-tem (CPS)s in industrial plants (e.g., formed of products, machines, orequipment).  CPSs  find  their  application  in  many  disciplines  includingInternet of Things (IoT), smart mobility, smart grids, Industry 4.0 andsmart houses. Swarms of CPSs are even more complex, hard to controland program. To handle the complexity of swarms of CPSs, natural sys-tems can serve as inspiration. Only through their interactions, a collectivebehaviour emerges to solve complex tasks. SWILT considers the use casesof production scheduling in industrial plants and transportation in logis-tics. Currently, linear optimization is a widely used approach but due tothe increasing complexity it is typically performed only on a subset ofthe industrial plant. Thus, current methods are unable to cope with thesearch space of scheduling problems in large industrial plants. Since theproblem sizes in these use cases are extremely large and pre-calculatedschedules or transportation tables are not sufficient, the innovation is touse swarm algorithms with reactive local rules on individual agents whichare able to compensate for dynamic system changes via local interactionswithin their vicinity.