OPTIMISATION OF MILKRUN ROUTES IN MANUFACTURING SYSTEMS IN THE AUTOMOTIVE INDUSTRY
DOI:
https://doi.org/10.20998/2078-7405.2022.96.04Abstract
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.
References
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.
Downloads
Published
Issue
Section
License
Copyright Notice
Authors who publish with this Collection agree to the following terms:
1. Authors retain copyright and grant the Collection right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this Collection.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the Collection's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this Collection.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.