The in-plant supply has a great impact on the performance of manufacturing operation, because the manufacturing-related logistics operations influence the efficiency of manufacturing. There are different solutions to perform in-plant supply, in the automotive industry the milkrun and water spider solutions are widely used. Within the frame of this article the authors describe the optimization of milkrun routes in the manufacturing plant of an automotive supplier. The described methodology simplifies the problem for single- and multi-milkrun problems and the solution is demonstrated with an Excel Solver-based methodology. The optimization process and its practicability will be demonstrated through an example.

Author Biographies

Ádám Francuz, University of Miskolc, 3515 Miskolc, Hungary

Bachelor of Engineering, Institute of Logistics, University of Miskolc, 3515 Miskolc, Hungary

Dr. Bányai Tamás, University of Miskolc, 3515 Miskolc, Hungary

Associate professor, Institute of Logistics, University of Miskolc, 3515 Miskolc, Hungary


Lean logistics: Solutions in production: The milkrun and the waterspider. Available: http://www.leanlogisztika.hu/termeles-kiszolgalo-megoldasok-a-milkrun-es-a-water-spider/ Downloaded: 11/07/2020.

Mácsay, V., Bányai, T.: Toyota production system in milkrun based in-plant supply, Journal of Production Engineering vol.9(1) (2017) pp. 141-146. http://doi.org/10.24867/JPE-2017-01-141.

Bányai, T., Telek, P., Landschützer, C.: Milkrun based in-plant supply – An automotive approach, Lecture Notes in Mechanical Engineering (2018) pp. 170-185. https://doi.org/10.1007/978-3-319-75677-6_14.

Kilic, H.S., Durmusoglu, M.B., Baskak, M.: Classification and modeling for in-plant milk-run distribution systems, International Journal of Advanced Manufacturing Technology vol.62(9-12) (2012) pp. 1135-1146. https://doi.org/10.1007/s00170-011-3875-4.

Sadjadi, S.J., Jafari, M., Amini, T.: A new mathematical modeling and a genetic algorithm search for milk run problem (an auto industry supply chain case study), International Journal of Advanced Manufacturing Technology vol.44(1-2) (2009) pp. 194-200. https://doi.org/10.1007/s00170-008-1648-5.

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, Journal of Manufacturing Systems vol. 33(4) (2014) pp. 567-577. https://doi.org/10.1016/j.jmsy.2014.05.004.

You, Z.L., Jiao, Y.: Development and application of milk-run distribution systems in the express industry based on saving algorithm, Mathematical Problems in Engineering (2014) 536459. https://doi.org/10.1155/2014/536459.

Ma, J.H., Sun, G.H.: Mutation ant colony algorithm of milk-run vehicle routing problem with fastest completion time based on dynamic optimization. Discrete Dynamics in Nature and Society. 2013, 418436. https://doi.org/10.1155/2013/418436.

Lin, Y., Xu, T.Y., Bian, Z.Y.: A two-phase heuristic algorithm for the common frequency routing problem with vehicle type choice in the milk run, Mathematical Problems in Engineering (2015) 404868. https://doi.org/10.1155/2015/404868.

Ranjbaran, F. Kashan, A.H., Kazemi, A.: Mathematical formulation and heuristic algorithms for optimisation of auto-part milk-run logistics network considering forward and reverse flow of pallets, International Journal of Production Research vol.58(6) (2020) pp. 1741-1775. https://doi.org/10.1080/00207543.2019.1617449.

Korytkowski, P., Karkoszka, R.: Simulation-based efficiency analysis of an in-plant milkrun operator under disturbances, International Journal of Advanced Manufacturing Technology vol.82(5-8) (2016) pp. 827-837. https://doi.org/10.1007/s00170-015-7442-2.

Fedorko, G., Molnar, V., Honus, S., Neradilova, H., Kampf, R.: The application of simulation model of a milk run to identify the occurrence of failures, International Journal of Simulation Modelling vol.17(3) (2018) pp. 444-457. https://doi.org/10.2507/IJSIMM17(3)440.

Tamás, P.: Decision support simulation method for process improvement of intermittent production systems, Applied Sciences-Basel vol. 7(9) (2017) 950. https://doi.org/10.3390/app7090950.

Bohács, G., Kovács, G., Rinkács, A.: Production logistics simulation supported by process description languages, Management and Production Engineering Review vol.7(1) (2016) pp. 13-20. https://doi.org/10.1515/mper-2016-0002.

Fedorko, G., Vasil, M., Bartosova, M.: Use of simulation model for measurement of milkrun system performance, Open Engineering vol.9(1) (2019) pp. 600-605. https://doi.org/10.1515/eng-2019-0067.

Veres, P.;

Illés, B.; Landschützer, C.: Supply Chain Optimization in Automotive Industry: A Comparative Analysis of Evolutionary and Swarming Heuristics, In Vehicle and Automotive Engineering 2; Jármai, K., Bolló, B., Eds.; Springer: Cham, Switzerland, 2018; pp. 666–676. https://doi.org/10.1007/978-3-319-75677-6_57.

Bányai, Á.: Optimisation of intermediate storage network of JIT purchasing, Advanced Logistic Systems: Theory and Practice vol.5 (2011) pp. 35-40.

Nagy, G., Illés, B., Bányai, Á.: Impact of Industry 4.0 on production logistics, IOP Conference Series: Materials Science and Engineering vol.448(1) (2018) 012013.






Organization of production (production process). Production planning.