3D Discrete Events Simulation to Evaluate the Internal Logistic Strategies in a Shipyard

  • Adolfo Lamas RodríguezEmail author
  • David Chas Álvarez
  • Jose Antonio Muiña Dono
Part of the EcoProduction book series (ECOPROD)


This paper presents an innovative parametric decision model designed by applying 3D Discrete Events Simulation (DES) concepts and customized to be applicable in offshore wind turbine foundations manufacturing plant. The high penalties applicable per day in case of delay in the fulfillment of any Load-Out milestones and the limited space available in the shipyard justify the use of this kind of 3D and parametric decision tool. This tool uses as a restriction the available internal logistic resources and buffers spaces, taking into account the high penalties defined by the customer in case of break the Load-Out milestones in order to achieve the best internal logistic strategy.


Buffer Internal logistics 3D discrete event simulation 



The authors are thankful to Unidad Mixta de Investigación (UMI) Navantia-UDC for its valuable support.


  1. 1.
    Akpan, J.I., Shanker, M.: The confirmed realities and myths about the benefits and costs of 3D visualization and virtual reality in discrete event modeling and simulation: a descriptive meta-analysis of evidence from research and practice. Comput. Ind. Eng. 112, 197–211 (2017)CrossRefGoogle Scholar
  2. 2.
    Akpan, J.I., Brooks, R.J: Experimental investigation of the impacts of virtual reality on discrete-event simulation. In: Proceedings—Winter Simulation Conference 2005 (Smith 1999), 1968–1975 (2005)Google Scholar
  3. 3.
    Boer, C.A., Bijl, J. L.: Advanced 3D visualization for simulation using game technology. In: Proceedings of the 2011 Winter Simulation Conference, pp. 2815–2826 (2011)Google Scholar
  4. 4.
    EWEA: The European offshore wind industry. Key trends and statistics 2016 (2017)Google Scholar
  5. 5.
    Kádár, B., Pfeiffer, A., Monostori L.: Discrete event simulation for supporting production planning and scheduling decisions in digital factories. In: Proceedings of the 37th CIRP International Seminar on Manufacturing Systems, pp. 444–448 (2004)Google Scholar
  6. 6.
    Kim, B., Kim, Tae-wan: Scheduling and cost estimation simulation for transport and installation of floating hybrid generator platform. Renew. Energy 111, 131–146 (2017)CrossRefGoogle Scholar
  7. 7.
    Krenczyk, D. et al.: Production planning and scheduling with material handling using modelling and simulation. In: MATEC Web of Conferences 112, pp. 1–6 (2017)CrossRefGoogle Scholar
  8. 8.
    Kumar, U.S., Narayan, Y.S.: Productivity improvement in a windows manufacturing layout using Flexsim simulation software. Int. J. Res. Advent Technol. 3(9), 86–90 (2015)Google Scholar
  9. 9.
    Lantz, E., Hand, M., and Wiser, R.: The past and future cost of wind energy. World Renew. Energy Forum, 1–10 (2012)Google Scholar
  10. 10.
    Walter, M. et al.: 2016 Offshore Wind Technologies Market Report. U.S. Department of Energy, p. 131 (2016).
  11. 11.
    Stewart, R.: Journal of Simulation. In: Simulation: The Practice of Model Development and Use (2004)Google Scholar
  12. 12.
    Sun, X., Diangui, H., Guoqing, W.: The current state of offshore wind energy technology development. Energy 41(1), 298–312 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Adolfo Lamas Rodríguez
    • 1
    Email author
  • David Chas Álvarez
    • 2
  • Jose Antonio Muiña Dono
    • 2
  1. 1.University of a CoruñaFerrolSpain
  2. 2.UMI Navantia-University of a CoruñaFerrolSpain

Personalised recommendations