Simulation Modeling and Digital Twins for Decision Making in the Age of Industry 4.0
Permanent URI for this collectionhttps://hdl.handle.net/10125/107444
Browse
Recent Submissions
Item type: Item , Reviving Simulated Annealing: Lifting its Degeneracies for Real-Time Job Scheduling(2024-01-03) Schmidt, Johann; Köhler, Benjamin; Borstell, HagenInspired by the success of Simulated Annealing in physics, we transfer insights and adaptations to the scheduling domain, specifically addressing the one-stage job scheduling problem with an arbitrary number of parallel machines. In optimization, challenges arise from local optima, plateaus in the loss surface, and computationally complex Hamiltonian (cost) functions. To overcome these issues, we propose the integration of corrective actions, including symmetry breaking, restarts, and freezing out non-optimal fluctuations, into the Metropolis-Hastings algorithm. Additionally, we introduce a generalized Hamiltonian that efficiently fuses straightforward but widely applied processing-time cost functions. Our approach outperforms decision rules, meta-heuristics, and novel reinforcement learning algorithms. Notably, our method achieves these superior results in real-time, thanks to its computationally efficient evaluation of the Hamiltonian.Item type: Item , Optimizing Safety Stock Placement in Large Real-World Automotive Supply Networks Using the Guaranteed-Service Model(2024-01-03) Rolf, Benjamin; Lavassani, Kayvan; Lang, Sebastian; Reggelin, TobiasThis paper presents an optimization model for the placement of safety stocks in multi-echelon supply networks using the Guaranteed-service Model. Our model handles complex network topologies and multiple products while examining the impact of service level and service time on total costs, formulated with mixed-integer linear programming. We utilize a unique network dataset acquired through data mining of financial databases to generate scenarios that reflect the complexities of real-world supply networks of five major automotive corporations. Experimental results validate the effectiveness of a dynamic-programming based solver in obtaining optimal solutions within large general network topologies. Furthermore, a sensitivity analysis reveals a negative correlation between safety stock costs and the maximum allowed service and a positive correlation between safety stock costs and the service level.Item type: Item , Imitation Learning Based on Deep Reinforcement Learning for Solving Scheduling Problems(2024-01-03) Nahhas, Abdulrahman; Kharitonov, Andrey; Haertel, Christian; Turowski , KlausScheduling problems are present in various industrial and service sectors and have a great deal of impact on the performance of these systems. The overwhelming majority of industrial problems exhibit a data-analytic or optimization nature, which can be reduced to known machine learning or optimization problems, respectively. This paper demonstrates the integration of optimization and Deep Reinforcement Learning (DRL) techniques to address scheduling problems. The study explores the potential advantages of Imitation Learning (IL) principles in achieving an optimization and machine learning pipeline for online scheduling. We employ an evolutionary optimization algorithm as an expert policy to generate high-quality solutions for solving scheduling problems. The obtained solutions are passed in the form of experiences to train a DRL-based IL technique. The presented approach is based on adopting the Nondominated Sorting Genetic Algorithm three (NSGA III) and the Monotonic Advantage Re-Weighted Imitation Learning (MARWIL). The presented approach is evaluated using real instances of a Hybrid Flow Shop (HFS) scheduling problem. The experimental analysis demonstrates that the presented DRL-based IL approach learns an appropriate scheduling policy, which is superior to training an agent without previous experiences. Additionally, the derived policy sustains a steady increase in performance when exposing the agent to different unknown problems in contrast to an established baseline from the literature for solving the same problems.Item type: Item , Introduction to the Minitrack on Simulation Modeling and Digital Twins for Decision Making in the Age of Industry 4.0(2024-01-03) Strassburger, Steffen; Galka, Stefan; Reggelin, Tobias; Lang, Sebastian