TY - CHAP A1 - Strassburger, Steffen A1 - Galka, Stefan A1 - Lang, Sebastian A1 - Reggelin, Tobias ED - Bui, Tung T1 - Introduction to the Minitrack on Simulation Modeling, Artificial Intelligence and Digital Twins for Decision Making in Production and Logistics T2 - Proceedings of the 58th Hawaii International Conference on System Sciences, January 7-10, 2025, Honolulu, Hawaii N2 - The minitrack contains contributions with a focus on simulation modeling and digital twins and decision making in the context of Industry 4.0. The use of simulation models and digital twins is manifold, from planning to virtual commission and real-time operational decision support. Cyber-physical systems integrate the real world and the virtual world to enable decision making in the age of Industry 4.0. For example, customer decisions and supply disruptions which directly influence manufacturing and logistics systems require immediate decisions. Thus, models must have the capability to support decision making in manufacturing as well as in internal and external logistics in real-time. Methods include discrete-event simulation, discrete-rate simulation, hybrid simulation, system dynamics simulation, the combination of simulation modeling with machine learning or optimization heuristics, prescriptive analytics, and adaptive systems. Furthermore, this minitrack addresses simulation education and simulation models and digital twins used for education and training in manufacturing and logistics. Y1 - 2025 SN - 978-0-9981331-8-8 U6 - https://doi.org/10.24251/HICSS.2025.205 SP - 1677 EP - 1678 PB - University of Hawaii at Manoa ER - TY - CHAP A1 - Reggelin, Tobias A1 - Galka, Stefan A1 - Strassburger, Steffen A1 - Lang, Sebastian T1 - Introduction to the Minitrack “Simulation Modeling and Digital Twins for Decision Making in the Age of Industry 4.0” T2 - Proceedings of the 56th Hawaii International Conference on System Sciences (HICSS), January 3-6, 2023, Lahaina, Hawaii N2 - The minitrack contains contributions with a focus on simulation modeling and digital twins and decision making in the context of Industry 4.0. The use of simulation models and digital twins is manifold, from planning to virtual commission and real-time operational decision support. Cyber-physical systems integrate the real world and the virtual world to enable decision making in the age of Industry 4.0. For example, customer decisions and supply disruptions which directly influence manufacturing and logistics systems require immediate decisions. Thus, models must have the capability to support decision making in manufacturing as well as in internal and external logistics in real-time. Methods include discrete-event simulation, discrete-rate simulation, hybrid simulation, system dynamics simulation, the combination of simulation modeling with machine learning or optimization heuristics, prescriptive analytics, and adaptive systems. Furthermore, this minitrack addresses simulation education and simulation models used for education and training in manufacturing and logistics. Y1 - 2023 UR - https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1456&context=hicss-56 U6 - https://doi.org/10.24251/HICSS.2023.179 SP - 1436 EP - 1437 PB - ScholarSpace ER - TY - CHAP A1 - Galka, Stefan A1 - Strassburger, Steffen A1 - Lang, Sebastian A1 - Reggelin, Tobias ED - Bui, Tung T1 - Introduction to the Minitrack on Simulation Modeling and Digital Twins for Decision Making in the Age of Industry 4.0 T2 - Proceedings of the 57th Hawaii International Conference on System Sciences (HICSS), January3-6, 2024, Honolulu, Hawaii N2 - The minitrack contains contributions with a focus on simulation modeling and digital twins and decision making in the context of Industry 4.0. The use of simulation models and digital twins is manifold, from planning to virtual commission and real-time operational decision support. Cyber-physical systems integrate the real world and the virtual world to enable decision making in the age of Industry 4.0. For example, customer decisions and supply disruptions which directly influence manufacturing and logistics systems require immediate decisions. Thus, models must have the capability to support decision making in manufacturing as well as in internal and external logistics in real-time. Methods include discrete-event simulation, discrete-rate simulation, hybrid simulation, system dynamics simulation, the combination of simulation modeling with machine learning or optimization heuristics, prescriptive analytics, and adaptive systems. Furthermore, this minitrack addresses simulation education and simulation models used for education and training in manufacturing and logistics. Y1 - 2024 SN - 978-0-9981331-7-1 U6 - https://doi.org/10.24251/HICSS.2024.206 SP - 1647 EP - 1648 PB - ScholarSpace ER -