Optimization of the Supply of Components for Mass Production with the Use of the Ant Colony Algorithm

  • Joanna Kotowska
  • Marcin Markowski
  • Anna BurdukEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 637)


The paper raises the issue of adapting the logistics processes to the changes in the manufacturing area. The continuous improvement of production processes enforces searching for better solutions for the milk run to achieve shorter time of the milk run loop. In the paper we propose integer linear programming (ILP) model ant the intelligent ant-colony based meta-heuristic algorithm for the milkman problem. The tuning process of algorithm and the verification of algorithm performance are reported in the paper. Proposed algorithm is then utilized for solving real-life production line provisioning problem.


Logistics processes Milk run Ant colony optimization (ACO) Meta-heuristics Intelligent optimization methods of production systems 


  1. 1.
    Antosz, K., Stadnicka, D.: The results of the study concerning the identification of the activities realized in the management of the technical infrastructure in large enterprises. Eksploat. Niezawodn. (Maintenance and Reliability) 16(1), 112–119 (2014)Google Scholar
  2. 2.
    Dorigo, M.: Optimization, learning and natural algorithms. Ph.D. thesis. Politecnico di Milano, Italy (1992)Google Scholar
  3. 3.
    Dorigo, M., Birattari, M.: Ant Colony Optimization. Encyclopedia of Machine Learning. Springer, New York (2010)Google Scholar
  4. 4.
    Dorigo, M., Stutzle, T.: Ant Colony Optimization. Massachusetts Institute of Technology, London (2004)zbMATHGoogle Scholar
  5. 5.
    Grzybowska, K., Kovács, G.: The modelling and design process of coordination mechanisms in the supply chain. J. Appl. Logic (2016)Google Scholar
  6. 6.
    Kempa, W.M., Paprocka, I. Grabowik, C., Kalinowski, K.: Time-dependent solution for the manufacturing line with unreliable machine and batched arrivals. In: Conference on Modern Technologies in Industrial Engineering (ModTech), IOP Conference Series-Materials Science and Engineering, vol. 95 (2015)Google Scholar
  7. 7.
    Kłos, S., Patalas-Maliszewska, J., Trebuna, P.: Improving manufacturing processes using simulation methods. Appl. Comput. Sci. 12(4), 42–53 (2016)Google Scholar
  8. 8.
    IBM ILOG CPLEX Documentation.
  9. 9.
    Krenczyk, D., Skolud, B.: Transient states of cyclic production planning and control. Appl. Mech. Mater. 657, 961–965 (2014)CrossRefGoogle Scholar
  10. 10.
    Lean Enterprise Institute.
  11. 11.
    Loska, A.: Exploitation assessment of selected technical objects using taxonomic methods. Eksploat. Niezawodn. (Maintenance and Reliability) 15(1), 1–8 (2013)Google Scholar
  12. 12.
    Mazurkiewicz, D.: Computer-aided maintenance and reliability management systems for conveyor belts. Eksploat. Niezawodn. (Maintenance and Reliability) 16(3), 377–382 (2014)Google Scholar
  13. 13.
    De Moura, D.A., Botter, R.C.: Delivery and pick-up problem transportation – milk run or conventional systems. Indep. J. Manage. Prod. 7(3), 746–749 (2016)CrossRefGoogle Scholar
  14. 14.
    Oshin, Chabra, A.: Job scheduling using ant colony optimization in grid environment. In: International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) (2016)Google Scholar
  15. 15.
    Panneerselvam, R.: Design and Analysis of Algorithms. Prentice Hall of India, Delhi (2007)Google Scholar
  16. 16.
    Patel, D., Patel, M.B., Vadher, J.A.: Implementation of milk run material supply system in vehicle routing problem with simultaneous pickup and delivery. Int. J. Appl. Innov. Eng Manage. (IJAIEM) 3(11), 122–123 (2014)Google Scholar
  17. 17.
  18. 18.
    Talbi, E.G.: Metaheuristics: From Design to Implementation. Wiley, Hoboken (2009)CrossRefzbMATHGoogle Scholar
  19. 19.
    Tavares Neto, R.F., Godinho Filho, M.: Literature review regarding ant colony optimization applied to scheduling problems: guidelines for implementation and directions for future research. Eng. Appl. Artif. Intell. 26(1), 150–161 (2013)CrossRefGoogle Scholar
  20. 20.
    Weiss, Z., Diakun, J., Dostatni, E.: Design management in virtual intranet environment. Conf. Cybern. Inf. Technol. Syst. Appl. (ISAS/CITSA) 3, 253–256 (2004)Google Scholar
  21. 21.
    Zang, H., Zhang, S., Hapeshi, K.: A review of nature-inspired algorithms. J. Bionic Eng. 7, 232–237 (2010)CrossRefGoogle Scholar
  22. 22.
    Zhao, N., Wu, Z., Zhao, Y., Quan, T.: Ant colony optimization algorithm with mutation mechanism and its applications. Expert Syst. Appl. 37(7), 4805–4810 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Joanna Kotowska
    • 1
  • Marcin Markowski
    • 2
  • Anna Burduk
    • 1
    Email author
  1. 1.Faculty of Mechanical EngineeringWroclaw University of Science and TechnologyWroclawPoland
  2. 2.Faculty of ElectronicsWroclaw University of Science and TechnologyWroclawPoland

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