Emulation of control strategies through machine learning in manufacturing simulations. Issue 1 (February 2017)

• Record Type: Journal Article
• Authors: Bergmann, S; Feldkamp, N; Strassburger, S
• Is Part Of: Journal of simulation. Volume 11:Issue 1(2017)
• Page Start: 38
• Page End: 50
• Publication Date: 2017-02
• Subjects: approximation -- dispatching rules -- automatic model generation -- data mining; Operations research -- Periodicals; Mathematical models -- Periodicals; Simulation methods -- Periodicals; 511.805
• DOI: 10.1057/s41273-016-0006-0
• Languages: English
• ISSNs: 1747-7778

Abstract

Abstract Discrete-event simulation is a well-accepted method for planning, evaluating, and monitoring processes in production and logistics. To reduce time and effort spent on creating simulation models, automatic simulation model generation is an important area in modeling methodology research. When automatically generating a simulation model from existing data sources, the correct reproduction of dynamic behavior of the modeled system is a common challenge. One example is the representation of dispatching and scheduling strategies of production jobs. When generating a model automatically, the underlying rules for these strategies are typically unknown but yet have to be adequately emulated. In this paper, we summarize our work investigating the suitability of various data mining and supervised machine learning methods for emulating job scheduling decisions based on data obtained from production data acquisition. We report on the performance of the algorithms and give recommendations for their application, including suggestions for their integration in simulation systems.