Journal of Simulation

, Volume 11, Issue 1, pp 38–50 | Cite as

Emulation of control strategies through machine learning in manufacturing simulations

Article
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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.

Keywords

approximation dispatching rules automatic model generation data mining 

Notes

Statement of contribution

Paper invited from ASIM 2015 conference.

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Copyright information

© The Operational Research Society 2016

Authors and Affiliations

  1. 1.Department for Industrial Information Systems, School of Economic Sciences and MediaIlmenau University of TechnologyIlmenauGermany

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