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Optimizing real-world factory flows using aggregated discrete event simulation modelling

Author

Listed:
  • Simon Lidberg

    (University of Skövde)

  • Tehseen Aslam

    (University of Skövde)

  • Leif Pehrsson

    (University of Skövde)

  • Amos H. C. Ng

    (University of Skövde)

Abstract

Reacting quickly to changing market demands and new variants by improving and adapting industrial systems is an important business advantage. Changes to systems are costly; especially when those systems are already in place. Resources invested should be targeted so that the results of the improvements are maximized. One method allowing this is the combination of discrete event simulation, aggregated models, multi-objective optimization, and data-mining shown in this article. A real-world optimization case study of an industrial problem is conducted resulting in lowering the storage levels, reducing lead time, and lowering batch sizes, showing the potential of optimizing on the factory level. Furthermore, a base for decision-support is presented, generating clusters from the optimization results. These clusters are then used as targets for a decision tree algorithm, creating rules for reaching different solutions for a decision-maker to choose from. Thereby allowing decisions to be driven by data, and not by intuition.

Suggested Citation

  • Simon Lidberg & Tehseen Aslam & Leif Pehrsson & Amos H. C. Ng, 2020. "Optimizing real-world factory flows using aggregated discrete event simulation modelling," Flexible Services and Manufacturing Journal, Springer, vol. 32(4), pages 888-912, December.
    • Handle: RePEc:spr:flsman:v:32:y:2020:i:4:d:10.1007_s10696-019-09362-7
    DOI: 10.1007/s10696-019-09362-7
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    References listed on IDEAS

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    1. Tobias Reggelin & Juri Tolujew, 2008. "Mesoscopic Modeling And Simulation Of Logistics Systems," Polish Journal of Management Studies, Czestochowa Technical University, Department of Management, vol. 2(1), pages 21-28, December.
    2. Tobias Reggelin & Juri Tolujew, 2008. "Mesoscopic Modeling And Simulation Of Logistics Systems," Advanced Logistic systems, University of Miskolc, Department of Material Handling and Logistics, vol. 2(1), pages 21-28, December.
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    4. Erjen Lefeber & Dieter Armbruster, 2011. "Aggregate Modeling of Manufacturing Systems," International Series in Operations Research & Management Science, in: Karl G. Kempf & Pınar Keskinocak & Reha Uzsoy (ed.), Planning Production and Inventories in the Extended Enterprise, chapter 0, pages 509-536, Springer.
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    Cited by:

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