Advertisement

Layout Optimization for Cyber-Physical Material Flow Systems Using a Genetic Algorithm

  • Nikita ShchekutinEmail author
  • Ludger Overmeyer
  • Vyacheslav P. Shkodyrev
Conference paper
  • 189 Downloads
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 95)

Abstract

Cyber-physical production systems are a key solution in the Industry 4.0 age. Still, the advantages of using them are not always easyly presented with numbers. In this paper, the task of arranging a cyber-physical material flow system is addressed as a multi-objective optimization problem and a genetic algorithm is used to search for a Pareto front of optimal layouts. As an example of such a material flow system, a decentralized modular conveyor, which was developed at the Institute of Transport and Automation Technology at Leibniz University Hannover, is used.

Keywords

Logistics Cyber-physical systems Genetic algorithm Layout optimization Facility layout problem 

References

  1. 1.
    Allahyari, M.Z., Azab, A.: Mathematical modeling and multi-start search simulated annealing for unequal-area facility layout problem. Expert Syst. Appl. 91, 46–62 (2018)CrossRefGoogle Scholar
  2. 2.
    Ardito, L., et al.: Towards Industry 4.0: Mapping digital technologies for supply chain management-marketing integration. Bus. Process Manage. J. 25, 323–346 (2018)CrossRefGoogle Scholar
  3. 3.
    Bauernhansl, T., Hompel, M.T., Vogel-Heuser, B. (Hg.): Industrie 4.0, Produktion, Automatisierung und Logistik: Anwendung-Technologien-Migration. Springer, Wiesbaden (2014)Google Scholar
  4. 4.
    Bozorgi, N., Abedzadeh, M., Zeinali, M.: Tabu search heuristic for efficiency of dynamic facility layout problem. Int. J. Advmanuf. Technol. 77(1), 689–703 (2015)CrossRefGoogle Scholar
  5. 5.
    Burkard, R.E., Stratmann, K.H.: Numerical investigations on quadratic assignment problems. Naval Res. Log. Quar. 25, 129–148 (1978)CrossRefGoogle Scholar
  6. 6.
    Drira, A., Pierreval, H., Hajri-Gabouj, S.: Facility layout problems: a survey. Ann. Rev. Control 31(2), 255–267 (2007).  https://doi.org/10.1016/j.arcontrol.2007.04.001,2007CrossRefGoogle Scholar
  7. 7.
    Faller, C., Feldmüller, D.: Industry 4.0 learning factory for regional SMEs. Procedia CIRP 32, 88–91 (2015)CrossRefGoogle Scholar
  8. 8.
    Kirks, T., Stenzel, J., Kamagaew, A., Hompel, M.T.: Zellulare Transportfahrzeuge für flexible und wandelbare Intralogistiksyste-me. Logistics J. (2012)Google Scholar
  9. 9.
    Kotothari, R., Ghosh, D.: A scatter search algorithm for the single row facility layout problem. Int. J. Adv. Manuf. Technol. 68(5–8), 1665–1675 (2013)Google Scholar
  10. 10.
    Kruhn, T.D.: Verteilte Steuerung flächiger Fördersysteme für den innerbetrieblichen Materialfluss. Zugl.: Hannover, Univ., Diss.,. Hg. v. Ludger Overmeyer. Garbsen, Garbsen: PZH-Verl., TEWISS - Technik-und-Wissen-GmbH (Berichte aus dem ITA, 2015, Bd. 1) (2015)Google Scholar
  11. 11.
    Liu, J., Zhang, H., He, K., Jiang, S.: Multi-objective particle swarm optimization algorithm based on objective space division for the un-equal-area facility layout problem. Expert Syst. Appl. 102, 179–192 (2018)CrossRefGoogle Scholar
  12. 12.
    Matai, R., Singh, S.P., Mittal, M.L.: A non-greedy systematic neighbourhood search heuristic for solving facility layout problem. Int. J. Adv. Manuf. Technol. 68(5–8), 1665–1675 (2013)CrossRefGoogle Scholar
  13. 13.
    Ning, X., Qi, J., Wu, C., Wang, W.: A tri-objective ant colony optimization based model for planning safe construction site layout. Autom. Const. 89, 1–12 (2018)CrossRefGoogle Scholar
  14. 14.
    Pardalos, P.M., Migdalas, A., Pitsoulis, L. (eds.): Pareto Optimality, Game Theory and Equilibria (2008)Google Scholar
  15. 15.
    Phanden, R.K., Demir, H.I., Gupta, R.D.: Application of genetic algorithm and variable neighborhood search to solve the facility lay-out planning problem in job shop production system. In: 7th International Conference on Industrial Technology and Management (ICITM), Oxford, pp. 270–274 (2018)Google Scholar
  16. 16.
    Pichka, K., Bajgiran, A.H, Petering, M.E., Jang, J., Yue, X.: The two echelon open location routing problem: Mathematical model and hybrid heuristic. Comput. Ind. Eng. 121, 97–112 (2018)CrossRefGoogle Scholar
  17. 17.
    Poschke, A.: Pareto-Ansatz zur Layoutoptimierung kogntiver Fördersysteme. Master Thesis supervised by, N. Shchekutin, Leibniz Universität Hannover, Institute of Transport and Automation Technology (2018)Google Scholar
  18. 18.
    Shchekutin, N., Zobnin, S., Overmeyer, L., Shkodyrev, V.: Mathematical methods for the configuration of transportation systems with a focus on continuous and modular matrix conveyors. Logistics Journal: Proceedings, Jg. (8) (2015)Google Scholar
  19. 19.
    Shchekutin, N., Sohrt, S., Overmeyer, L.: Multi-objective layout optimization for material flow system with decentralized and scalable control. Logistics Journal: Proceedings, Jg. (10) (2017)Google Scholar
  20. 20.
    Sohrt, S., Heinke, A., Shchekutin, N., Eilert, B., Overmeyer, L., Krühn, T.: Kleinskalige, cyber-physische Fördertechnik, Vogel-Heuser, B., Bauernhansl, T., ten Hompel, M. (Hrsg.): Handbuch Industrie 4.0: Produktion, Automatisierung und Logistik, Springer, Heidelberg (2016)Google Scholar
  21. 21.
    Wascher, G.: Innerbetriebliche Standortplanung bei einfacher und mehrfacher Zielsetzung, Bochumer Beiträge zur Unterneh-mungsführung und Unternehmensforschung. Gabler Verlag, Wiesbaden (1982)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Nikita Shchekutin
    • 1
    Email author
  • Ludger Overmeyer
    • 1
  • Vyacheslav P. Shkodyrev
    • 2
  1. 1.Leibniz Universität HannoverHanoverGermany
  2. 2.Peter the Great St. Petersburg Polytechnic UniversitySaint PetersburgRussia

Personalised recommendations