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

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Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017 (ISPEM 2017)

Abstract

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.

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References

  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. Dorigo, M.: Optimization, learning and natural algorithms. Ph.D. thesis. Politecnico di Milano, Italy (1992)

    Google Scholar 

  3. Dorigo, M., Birattari, M.: Ant Colony Optimization. Encyclopedia of Machine Learning. Springer, New York (2010)

    Google Scholar 

  4. Dorigo, M., Stutzle, T.: Ant Colony Optimization. Massachusetts Institute of Technology, London (2004)

    MATH  Google Scholar 

  5. Grzybowska, K., Kovács, G.: The modelling and design process of coordination mechanisms in the supply chain. J. Appl. Logic (2016)

    Google Scholar 

  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. Kłos, S., Patalas-Maliszewska, J., Trebuna, P.: Improving manufacturing processes using simulation methods. Appl. Comput. Sci. 12(4), 42–53 (2016)

    Google Scholar 

  8. IBM ILOG CPLEX Documentation. www-01.ibm.com

  9. Krenczyk, D., Skolud, B.: Transient states of cyclic production planning and control. Appl. Mech. Mater. 657, 961–965 (2014)

    Article  Google Scholar 

  10. Lean Enterprise Institute. http://lean.org.pl

  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. 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. 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)

    Article  Google Scholar 

  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. Panneerselvam, R.: Design and Analysis of Algorithms. Prentice Hall of India, Delhi (2007)

    Google Scholar 

  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. LeanCenter. www.leancenter.pl

  18. Talbi, E.G.: Metaheuristics: From Design to Implementation. Wiley, Hoboken (2009)

    Book  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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. Zang, H., Zhang, S., Hapeshi, K.: A review of nature-inspired algorithms. J. Bionic Eng. 7, 232–237 (2010)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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Correspondence to Anna Burduk .

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Kotowska, J., Markowski, M., Burduk, A. (2018). Optimization of the Supply of Components for Mass Production with the Use of the Ant Colony Algorithm. In: Burduk, A., Mazurkiewicz, D. (eds) Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017. ISPEM 2017. Advances in Intelligent Systems and Computing, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-319-64465-3_34

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  • DOI: https://doi.org/10.1007/978-3-319-64465-3_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64464-6

  • Online ISBN: 978-3-319-64465-3

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