Design space exploration of a poultry fillet processing system using discrete-event simulation

  • N. Paape ,
  • J.A.W.M. van Eekelen, 
  • M.A. Reniers 
  • a,b,c  Eindhoven University of Technology, Eindhoven, The Netherlands
Cite as
Paape N., van Eekelen J., and Reniers M. (2022).,Design space exploration of a poultry fillet processing system using discrete-event simulation. Proceedings of the 8th International Food Operations and Processing Simulation Workshop (FoodOPS 2022). , 001 . DOI: https://doi.org/10.46354/i3m.2022.foodops.001
 Download PDF

Abstract

Developments in the poultry processing industry, such as how livestock is raised and how consumers buy meat, make it increasingly difficult to design poultry processing systems that meet evolving standards. More and more iterations of (re)design are required to optimize the product flow in these systems. This paper presents a method for design space exploration of production systems using discrete-event simulation. This method automates most steps of design space exploration: iterating on the design, model construction, performing simulation experiments, and interpreting the simulation results. This greatly reduces the time and effort required to iterate through different designs. A case study is presented which shows that this method can be effective for design space exploration of poultry processing systems.

References

  1. Centobelli, P., Cerchione, R., Murino, T., and Gallo, M. (2016). Layout and material flow optimization in digital factory. International Journal of Simulation Modelling,
    15(2):223–235.
  2. Kikolski, M. (2017). Study of Production Scenarios with the Use of Simulation Models. Procedia Engineering,
    182:321–328.
  3. Kranz, M., Duisberg, M., Burgert, F. L., Gerdes, L., and Mütze-Niewöhner, S. (2021). Algorithmic layout generation in discrete-event simulation of assembly systems. 35th Annual European Simulation and Modelling Conference 2021, ESM 2021, (October):96–104.
  4. Laemmle, A. and Gust, S. (2019). Automatic layout generation of robotic production cells in a 3D manufacturing
    simulation environment. Procedia CIRP, 84:316–321.
  5. Owens, S. F. and Levary, R. R. (2002). Evaluating design alternatives of an extruded food production line using
    simulation. Simulation, 78(10):626–632.
  6. Penazzi, S., Accorsi, R., Ferrari, E., Manzini, R., and Dunstall, S. (2017). Design and control of food job-shop processing systems: A simulation analysis in the catering industry. The International Journal of Logistics Management, 28(3):782–797
  7. Pimentel, A. D. (2017). Exploring Exploration: A Tutorial Introduction to Embedded Systems Design Space
    Exploration. IEEE Design and Test, 34(1):77–90.
  8. Plà-Aragonés, L., Pagès-Bernaus, A., Novoa-Tufet, E., Mateo-Fornés, J., Tarrafeta, P., Mendioroz, D., PérezCànovas, L., and López-Nogales, S. (2017). Discrete event simulation of a pigmeat packing plant. Proceedings of FOODOPS 2017, pages 68–72.
  9. Plà-Aragonés, L. M., Pagès-Bernaus, A., Nadal-Roig, E., Mateo-Fornés, J., Tarrafeta, P., Mendioroz, D., PérezCànovas, L., and López-Nogales, S. (2020). Economic Assessment of Pig Meat Processing and Cutting Production by Simulation. International Journal of Food Engineering, 16(5-6):1–11
  10. Rodič, B. and Kanduč, T. (2015). Optimisation of a complex manufacturing process using discrete event simulation
    and a novel heuristic algorithm. International Journal of Mathematical Models and Methods in Applied Sciences,
    9:320–329.
  11. Sanchez-Sabate, R. and Sabaté, J. (2019). Consumer attitudes towards environmental concerns of meat consumption: A systematic review. International Journal of Environmental Research and Public Health, 16(7).
  12. Simio (2022). https://www.simio.com/. Accessed: 2022-04-12.
  13. The Anylogic Company (2022). https://www.anylogic. com/. Accessed: 2022-04-12.
  14. Thornton, P. K. (2010). Livestock production: Recent trends, future prospects. Philosophical Transactions of
    the Royal Society B: Biological Sciences, 365(1554):2853–2867.
  15. Verbeke, W. I. M. A. J. and Viaene, J. (2000). Ethical Challenges for Livestock Production:Meeting Consumer Concerns about Meat Safety and AnimalWelfare. Journal of Agricultural and Environmental Ethics, 12:141–151.
  16. Vogel-Heuser, B., Simon, T., and Fischer, J. (2016). Variability management for automated production systems
    using product lines and feature models. IEEE International Conference on Industrial Informatics (INDIN),
    0:1231–1237
  17. Xie, X. and Li, J. (2012). Modeling, analysis and continuous improvement of food production systems: A case study
    at a meat shaving and packaging line. Journal of Food Engineering, 113(2):344–350
  18. Xu, J., Huang, E., Chen, C. H., and Lee, L. H. (2015). Simulation optimization: A review and exploration in the
    new era of cloud computing and big data. Asia-Pacific Journal of Operational Research, 32(3):1–34.