Advertisement

Implementation of Calculation for Simulation of Milk-run Intralogistics System

  • Kamila KluskaEmail author
  • Patrycja Hoffa-Dąbrowska
Chapter
Part of the EcoProduction book series (ECOPROD)

Abstract

Designing and modeling milk-run intralogistics systems create many aspects which have to be considered. Determining the appropriate number of trains together with defining the right parts for transport in trains and the definition of routes for them is often a big challenge for the designer. Next challenge is validation of designed system (number of trains, routes, and choosing right trailer for defined parts). For validation useful can be simulation model. In this chapter, authors present one step of methodology of designing milk-run intralogistics systems. They describe how to implement the calculation results about number of trains, trailers, and routes into the simulation model.

Keywords

Logistic train Intralogistics Transport Simulation 

Notes

Acknowledgements

The work was carried out as part of the POIR.01.01.01-00-0485/17 project, “Development of a new type of logistic trailer and methods of collision-free and deadlock-free implementation of intralogistics processes,” financed by NCBiR.

References

  1. 1.
    Alnahhal, M.: Efficient material flow in mixed model assembly lines. Universität Duisburg-Essen (2015)Google Scholar
  2. 2.
    Bae, K.G., Evans, L.A., Summers, A.: Lean design and analysis of a milk-run delivery system: case study. In: Roeder, T.M.K., Frazier, P.I., Szechtman, R., Zhou, E., Huschka, T., Chick, S.E. (eds.) Proceedings of the 2016 Winter Simulation Conference (2016)Google Scholar
  3. 3.
    Chee, S.L., Chong, M.Y., Chin, J.F.: Milk-run kanban system for raw printed circuit board withdrawal to surface-mounted equipment. J. Ind. Eng. Manag. 5(2), 382–405 (2012)Google Scholar
  4. 4.
    Costa, B., Dias, L.D., Oliveira, J.A., Pereira, G.: Simulation as a tool for planning a material delivery system to manufacturing lines. In: IEMC International Engineering Management Conference (2008)Google Scholar
  5. 5.
    Greenwood, A.G., Kluska, K., Pawlewski, P.: A multi-level framework for simulating milk-run, in-plant logistics operations. In: Bajo, J., et al. (eds.) Highlights of Practical Applications of Cyber-Physical Multi-agent Systems, PAAMS 2017. Communications in Computer and Information Science, vol. 722. Springer, Cham (2017)Google Scholar
  6. 6.
    Greenwood, A.G., Kluska, K., Pawlewski, P.: A hybrid modeling approach for simulating milk-run in-plant logistics operations. In: Bajo, J., et al. (eds.) Highlights of Practical Applications of Cyber-Physical Multi-agent Systems, PAAMS 2017. Communications in Computer and Information Science, vol. 722. Springer, Cham (2017)Google Scholar
  7. 7.
    Hao, Q., Shen, W.: Implementing a hybrid simulation model for a Kanban based material handling system. Robot. Comput. Integer Manuf. 24, 635–646 (2008)CrossRefGoogle Scholar
  8. 8.
    Hosseini, S.D., Shirazi, M.A., Karimi, B.: Cross-docking and milk run logistics in a consolidation network: a hybrid of harmony search and simulated annealing approach. J. Manuf. Syst. 33(4), 567–577 (2014)CrossRefGoogle Scholar
  9. 9.
    Kilic, H.S., Durmusoglu, M.B., Baskak, M.: Classification and modeling for in-plant milk-run distribution systems. J. Ind. Eng. Manag. 5(2), 382–405 (2012)Google Scholar
  10. 10.
    Kitamura, T., Okamoto, K.: Automated route planning for milk-run transport logistics using model checking. In: Proceeding of 2012 Third International Conference on Networking and Computing, pp. 240–246 (2012)Google Scholar
  11. 11.
    Korytkowski, P., Karkoszka, R.: Simulation-based efficiency analysis of an in-plant milk-run operator under disturbances. Int. J. Adv. Manuf. Technol. 82(5–8), 827–837 (2016)CrossRefGoogle Scholar
  12. 12.
    Pawlewski, P.: Script language to describe agent’s behaviors. In: Bajo, J., et al. (eds.) Highlights of Practical Applications of Complex Multi-agent Systems—International Workshops of PAAMS 2018. Springer, Cham (2018)CrossRefGoogle Scholar
  13. 13.
    Staab, T., Klenk, E., Günthner, W.A.: Simulating dynamic dependencies and blockages in in-plant milk-run traffic systems. In: Proceedings of the 27th European Conference on Modelling and Simulation, Aalesund, pp. 622–628 (2013)Google Scholar
  14. 14.
    Vieira, A., Dias, L., Pereira, G., Oliveira, J., Carvalho, M., Martins, P.: 3D microsimulation of milkruns and pickers in warehouses using SIMIO. In: Modelling and Simulation 2014—European Simulation and Modelling Conference, ESM 2014, pp. 261–269 (2014)Google Scholar
  15. 15.
    Wang, X., He, M., Jiang, H.: A discrete firefly algorithm for routing optimization of milk-run. In: 5th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2015) (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Chair of Production Engineering and Logistics, Faculty of Engineering ManagementPoznan University of TechnologyPoznanPoland

Personalised recommendations