T. Sobottka, F. Kamhuber, M. Faezirad, W. Sihn:
"Potential for machine learning in optimized production planning with hybrid simulation";
Procedia Manufacturing, 39 (2019), S. 1844 - 1853.

Kurzfassung englisch:
Advanced production planning and scheduling approaches increasingly rely on simulation-based optimization methods. This entails the problem of a high computational effort due to complex models, resulting in limitations for the practical application of otherwise powerful methods. While machine-learning methods offer a potential for performance improvement, approaches for real-life applications with a high complexity are still lacking. This paper explores the potential for machine learning, especially artificial neural networks, used as surrogate models, to improve the performance of a recently developed planning method for real life production planning applications. The simulation considered in this paper is a complex hybrid discrete-continuous model, enabling the method to pursue energy efficiency simultaneously with economic goals, in a complex multi-criteria goal system. The artificial neural network is trained via offline learning and is meant to provide a computationally cheap evaluation of intermediate planning solutions, compiled by an optimization algorithm during an optimization run.
The approach is developed and evaluated in a case-study on the food industry, indicating a basic feasibility of the approach but also pointing out necessary future challenges to be solved towards practical applicability.

Simulation, Optimization, Production Planning and Control, Machine Learning, Neural Networks

"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)

Erstellt aus der Publikationsdatenbank der Technischen Universitšt Wien.