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 - Ivanov, Dmitry A1 - Lang, Sebastian T1 - Introduction to the Minitrack on Modeling and Decision Making in Manufacturing and Logistics in the Age of Industry 4.0 T2 - Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS): 2021 Hawaii, USA N2 - The minitrack contains contributions with a focus on modeling and decision making in manufacturing and logistics in the context of Industry 4.0. Cyber-physical systems integrate the real world and the virtual world. For example, customer decisions and supply disruptions which directly influence manufacturing and logistics systems require immediate decisions in terms of planning and controlling the manufacturing and logistics systems. In order to immediately respond to these dynamics, models must have the capability to support decision making in manufacturing as well as in internal and external logistics in real-time. Two of the Mintrack papers describe concepts and models for digital twins. One paper provides an adaptive scheduling framework for solving multi-objective hybrid flow shop scheduling problems and one paper investigates matrix production systems and with their requirements and impacts on logistics planning. Y1 - 2021 SN - 978-0-9981331-4-0 U6 - https://doi.org/10.24251/HICSS.2021.198 SP - 1643 EP - 1644 PB - University of Hawai'i at Manoa Hamilton Library CY - Honolulu, HI ER - TY - CHAP A1 - Rolf, Benjamin A1 - Reggelin, Tobias A1 - Lang, Sebastian A1 - Galka, Stefan T1 - Developing a Decision Support System for Integrated Decision-Making in Purchasing and Scheduling under Lead Time Uncertainty T2 - Proceedings of the 55th Hawaii International Conference on System Sciences (HICSS55): January 3-7, 2022, Maui, Hawaii, USA N2 - Decision-making in supply chain management is complex because of the relations between planning tasks from different stages and planning levels. Uncertainties such as unpredictable supplier lead times and supply chain disruptions further complicate decision-making. Considering the case study of a company in printed circuit board assembly, a three-level concept is proposed that includes a decision support system. The global single-source supply network is characterized by highly variable lead times. Hence, the company maintains high inventory levels to prevent running out of stock. The decision support system considers the purchasing and scheduling decision problems in an integrated way. The prototypical implementation of the purchasing algorithm uses a genetic algorithm that recommends reorder days and order quantities using a simulation model. In addition, it evaluates the risks of the recommended solution by calculating the probability of stockouts for each order cycle. KW - decision support system KW - simulation KW - supply chain optimization, KW - purchasing KW - cyber-physical system KW - Industry 4.0 Y1 - 2022 U6 - https://doi.org/10.24251/HICSS.2022.247 VL - 2022 SP - 1964 EP - 1973 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 -