Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
Due to the rising complexity of production systems in the automotive industry, simulation has become an established tool for analyzing dynamic systems. However, once the number of parameter combinations rises exponentially, the generation and evaluation of all possible solutions gets impractical. While the combination of simulation and optimization has a long tradition in academic research, its adoption in the automotive industry remains limited, often due to the high execution time associated with optimization experiments. To enable more efficient decision- making, this paper explores the integration of machine learning and optimization for simulation optimization. Specifically, it focuses on the use of metamodels incorporating various machine learning algorithms and metaheuristics to optimize two production planning problems with multiple parameter classes. The presented approach enables decision-makers to conduct a rapid assessment of complex production systems.
Recommended Citation
Schweitzer, Felicia; Habel, Lars; Canaviri Vilca, Oscar Jhonny; Kulzer, Tobias; and Wenzel, Sigrid, "Metamodel-based Simulation Optimization Using Machine Learning for Solving Production Planning Problems in the Automotive Industry" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 5.
https://aisel.aisnet.org/hicss-58/da/digital_twins/5
Metamodel-based Simulation Optimization Using Machine Learning for Solving Production Planning Problems in the Automotive Industry
Hilton Waikoloa Village, Hawaii
Due to the rising complexity of production systems in the automotive industry, simulation has become an established tool for analyzing dynamic systems. However, once the number of parameter combinations rises exponentially, the generation and evaluation of all possible solutions gets impractical. While the combination of simulation and optimization has a long tradition in academic research, its adoption in the automotive industry remains limited, often due to the high execution time associated with optimization experiments. To enable more efficient decision- making, this paper explores the integration of machine learning and optimization for simulation optimization. Specifically, it focuses on the use of metamodels incorporating various machine learning algorithms and metaheuristics to optimize two production planning problems with multiple parameter classes. The presented approach enables decision-makers to conduct a rapid assessment of complex production systems.
https://aisel.aisnet.org/hicss-58/da/digital_twins/5