Alternative Performance Indicators for Optimizing Container Assignment in a Synchromodal Transportation Network

  • M. R. Ortega del Vecchyo
  • F. PhillipsonEmail author
  • A. Sangers
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11184)


Several different attributes are deemed important in the con-tainer-to-mode assignment on a synchromodal transportation network. This paper proposes a way to quantify several of this different attributes: Robustness, Flexibility and Customer Satisfaction. These attributes are used as alternative objectives when optimizing the container assignment in a Synchromodal Transportation Network, modelling it as a Minimum Cost Multi-Commodity Flow on a Space-Time Network.


Synchromodal logistics Minimum cost multi commodity flow Space-time graphs Objectives Robustness Flexibility 



This work has been carried out within the project ‘Complexity Methods for Predictive Synchromodality’ (Comet-PS), supported by NWO (the Netherlands Organisation for Scientific Research), TKI-Dinalog (Top Consortium Knowledge and Innovation) and the Early Research Program ‘Grip on Complexity’ of TNO (The Netherlands Organisation for Applied Scientific Research).


  1. 1.
    Ahluwalia, P.K., Nema, A.K.: Multi-objective reverse logistics model for integrated computer waste management. Waste Manage. Res. 24(6), 514–527 (2006)CrossRefGoogle Scholar
  2. 2.
    Andersen, J., Crainic, T., Christiansen, M.: Service network design with asset management: Formulations and comparative analyses. Transp. Res. Part C Emerg. Technol. 17(2), 197–207 (2009)CrossRefGoogle Scholar
  3. 3.
    Baykasoğlu, A., Subulan, K.: A multi-objective sustainable load planning model for intermodal transportation networks with a real-life application. Transp. Res. Part E Logistics Transp. Rev. 95, 207–247 (2016)CrossRefGoogle Scholar
  4. 4.
    Beuthe, M., Bouffioux, C.: Analysing qualitative attributes of freight transport from stated orders of preference experiment. J. Transp. Econ. Policy (JTEP) 42(1), 105–128 (2008)Google Scholar
  5. 5.
    Caplice, C., Jauffred, F.: Balancing robustness and flexibility in transportation networks (2014).
  6. 6.
    Caramia, M., Dell’Olmo, P.: Multi-objective Management in Freight Logistics. Increasing Capacity, Service Level and Safety with Optimization Algorithms. Springer, London (2008). Scholar
  7. 7.
    Crainic, T.: Service network design in freight transportation. Eur. J. Oper. Res. 122(2), 272–288 (2000)CrossRefGoogle Scholar
  8. 8.
    Govindan, K., Paam, P., Abtahi, A.R.: A fuzzy multi-objective optimization model for sustainable reverse logistics network design. Ecol. Ind. 67, 753–768 (2016)CrossRefGoogle Scholar
  9. 9.
    Husdal, J.: A conceptual framework for risk and vulnerability in virtual enterprise networks. In: Managing Risk in Virtual Enterprise Networks: Implementing Supply Chain Principles, p. 1 (2010)Google Scholar
  10. 10.
    Ishfaq, R., Sox, C.R.: Intermodal logistics: the interplay of financial, operational and service issues. Transp. Res. Part E Logistics Transp. Rev. 46(6), 926–949 (2010)CrossRefGoogle Scholar
  11. 11.
    Miller-Hooks, E., Zhang, X., Faturechi, R.: Measuring and maximizing resilience of freight transportation networks. Comput. Oper. Res. 39(7), 1633–1643 (2012)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Ramezani, M., Bashiri, M., Tavakkoli-Moghaddam, R.: A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Appl. Math. Model. 37(1), 328–344 (2013)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Riessen, B.v., Negenborn, R., Dekker, R.: Synchromodal container transportation: an overview of current topics and research opportunities. In: Computational Logistics (2015)Google Scholar
  14. 14.
    SteadieSeifi, M., Dellaert, N.P., Nuijten, W., Van Woensel, T., Raoufi, R.: Multimodal freight transportation planning: a literature review. Eur. J. Oper. Res. 233(1), 1–15 (2014)CrossRefGoogle Scholar
  15. 15.
    Tuzkaya, G., Kilic, H.S., Aglan, C.: A multi-objective supplier selection and order allocation model for green supply chains. J. Mil. Inf. Sci. 4(3), 87–96 (2016)Google Scholar
  16. 16.
    Xifeng, T., Ji, Z., Peng, X.: A multi-objective optimization model for sustainable logistics facility location. Transp. Res. Part D Transp. Environ. 22, 45–48 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • M. R. Ortega del Vecchyo
    • 1
    • 2
  • F. Phillipson
    • 1
    Email author
  • A. Sangers
    • 1
  1. 1.TNOThe HagueThe Netherlands
  2. 2.Delft University of TechnologyDelftThe Netherlands

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