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Hybrid simulation and optimization approach for green intermodal transportation problem with travel time uncertainty

Author

Listed:
  • Martin Hrušovský

    (WU Vienna University of Economics and Business)

  • Emrah Demir

    (Cardiff Business School, Cardiff University)

  • Werner Jammernegg

    (WU Vienna University of Economics and Business)

  • Tom Woensel

    (Eindhoven University of Technology)

Abstract

The increasing volumes of road transportation contribute to congestion on road, which leads to delays and other negative impacts on the reliability of transportation. Moreover, transportation is one of the main contributors to the growth of carbon dioxide equivalent emissions, where the impact of road transportation is significant. Therefore, governmental organizations and private commercial companies are looking for greener transportation solutions to eliminate the negative externalities of road transportation. In this paper, we present a novel solution framework to support the operational-level decisions for intermodal transportation networks using a combination of an optimization model and simulation. The simulation model includes stochastic elements in form of uncertain travel times, whereas the optimization model represents a deterministic and linear multi-commodity service network design formulation. The intermodal transportation plan can be optimized according to different objectives, including costs, time and $$CO_2e$$ C O 2 e emissions. The proposed approach is successfully implemented to real-life scenarios where differences in transportation plans for alternative objectives are presented. The solutions for transportation networks with up to 250 services and 20 orders show that the approach is capable of delivering reliable solutions and identifying possible disruptions and alternatives for adapting the unreliable transportation plans.

Suggested Citation

  • Martin Hrušovský & Emrah Demir & Werner Jammernegg & Tom Woensel, 2018. "Hybrid simulation and optimization approach for green intermodal transportation problem with travel time uncertainty," Flexible Services and Manufacturing Journal, Springer, vol. 30(3), pages 486-516, September.
    • Handle: RePEc:spr:flsman:v:30:y:2018:i:3:d:10.1007_s10696-016-9267-1
    DOI: 10.1007/s10696-016-9267-1
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    1. Gendreau, Michel & Laporte, Gilbert & Seguin, Rene, 1996. "Stochastic vehicle routing," European Journal of Operational Research, Elsevier, vol. 88(1), pages 3-12, January.
    2. Bilge Bilgen & Yelda Çelebi, 2013. "Integrated production scheduling and distribution planning in dairy supply chain by hybrid modelling," Annals of Operations Research, Springer, vol. 211(1), pages 55-82, December.
    3. Christian Almeder & Margaretha Preusser & Richard F. Hartl, 2009. "Simulation and optimization of supply chains: alternative or complementary approaches?," Springer Books, in: Herbert Meyr & Hans-Otto Günther (ed.), Supply Chain Planning, pages 29-53, Springer.
    4. Tolga Bektaş & Emrah Demir & Gilbert Laporte, 2016. "Green Vehicle Routing," International Series in Operations Research & Management Science, in: Harilaos N. Psaraftis (ed.), Green Transportation Logistics, edition 127, chapter 0, pages 243-265, Springer.
    5. Richard S.J. Tol, 2011. "The Social Cost of Carbon," Annual Review of Resource Economics, Annual Reviews, vol. 3(1), pages 419-443, October.
    6. Crainic, Teodor G. & Rousseau, Jean-Marc, 1986. "Multicommodity, multimode freight transportation: A general modeling and algorithmic framework for the service network design problem," Transportation Research Part B: Methodological, Elsevier, vol. 20(3), pages 225-242, June.
    7. Demir, Emrah & Bektaş, Tolga & Laporte, Gilbert, 2014. "A review of recent research on green road freight transportation," European Journal of Operational Research, Elsevier, vol. 237(3), pages 775-793.
    8. William D. Nordhaus, 2011. "Estimates of the Social Cost of Carbon: Background and Results from the RICE-2011 Model," NBER Working Papers 17540, National Bureau of Economic Research, Inc.
    9. Anthoff, David & Rose, Steven K. & Tol, Richard S. J. & Waldhoff, Stephanie, 2011. "The Time Evolution of the Social Cost of Carbon: An Application of FUND," Papers WP405, Economic and Social Research Institute (ESRI).
    10. Demir, Emrah & Huang, Yuan & Scholts, Sebastiaan & Van Woensel, Tom, 2015. "A selected review on the negative externalities of the freight transportation: Modeling and pricing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 77(C), pages 95-114.
    11. Dekker, Rommert & Bloemhof, Jacqueline & Mallidis, Ioannis, 2012. "Operations Research for green logistics – An overview of aspects, issues, contributions and challenges," European Journal of Operational Research, Elsevier, vol. 219(3), pages 671-679.
    12. Arnt-Gunnar Lium & Teodor Gabriel Crainic & Stein W. Wallace, 2009. "A Study of Demand Stochasticity in Service Network Design," Transportation Science, INFORMS, vol. 43(2), pages 144-157, May.
    13. Macharis, C. & Bontekoning, Y. M., 2004. "Opportunities for OR in intermodal freight transport research: A review," European Journal of Operational Research, Elsevier, vol. 153(2), pages 400-416, March.
    14. Crainic, Teodor Gabriel, 2000. "Service network design in freight transportation," European Journal of Operational Research, Elsevier, vol. 122(2), pages 272-288, April.
    15. Reis, Vasco, 2014. "Analysis of mode choice variables in short-distance intermodal freight transport using an agent-based model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 61(C), pages 100-120.
    16. Robert B. Noland & John W. Polak, 2002. "Travel time variability: A review of theoretical and empirical issues," Transport Reviews, Taylor & Francis Journals, vol. 22(1), pages 39-54, January.
    17. Mes, Martijn & van der Heijden, Matthieu & van Harten, Aart, 2007. "Comparison of agent-based scheduling to look-ahead heuristics for real-time transportation problems," European Journal of Operational Research, Elsevier, vol. 181(1), pages 59-75, August.
    18. Demir, Emrah & Burgholzer, Wolfgang & Hrušovský, Martin & Arıkan, Emel & Jammernegg, Werner & Woensel, Tom Van, 2016. "A green intermodal service network design problem with travel time uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 93(PB), pages 789-807.
    19. SteadieSeifi, M. & Dellaert, N.P. & Nuijten, W. & Van Woensel, T. & Raoufi, R., 2014. "Multimodal freight transportation planning: A literature review," European Journal of Operational Research, Elsevier, vol. 233(1), pages 1-15.
    20. Laporte, Gilbert, 1992. "The vehicle routing problem: An overview of exact and approximate algorithms," European Journal of Operational Research, Elsevier, vol. 59(3), pages 345-358, June.
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    Cited by:

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