Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times

Research output: Journal contributionsJournal articlesResearchpeer-review


  • Jens Heger
  • Jurgen Branke
  • Torsten Hildebrandt
  • Bernd Scholz-Reiter
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.
Original languageEnglish
JournalInternational Journal of Production Research
Issue number22
Pages (from-to)6812-6824
Number of pages13
Publication statusPublished - 16.11.2016

    Research areas

  • scheduling, simulation, production, artificial intelligence, flexible manufacturing systems, Gaussian processes
  • Engineering