Journal
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Volume 54, Issue 22, Pages 6812-6824Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2016.1178406
Keywords
scheduling; simulation; production; artificial intelligence; flexible manufacturing systems; Gaussian processes
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Funding
- Deutsche Forschungsgemeinschaft [540/17-2, 540/30-1]
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Decentralised scheduling with dispatching rules is applied in many fields of production and logistics, especially in highly complex manufacturing systems. Since dispatching rules are restricted to their local information horizon, there is no rule that outperforms other rules across various objectives, scenarios and system conditions. In this paper, we present an approach to dynamically adjust the parameters of a dispatching rule depending on the current system conditions. The influence of different parameter settings of the chosen rule on the system performance is estimated by a machine learning method, whose learning data is generated by preliminary simulation runs. Using a dynamic flow shop scenario with sequence-dependent set-up times, we demonstrate that our approach is capable of significantly reducing the mean tardiness of jobs.
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