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TurboRouter: An Interactive Optimisation-Based Decision Support System for Ship Routing and Scheduling

Author

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  • Kjetil Fagerholt

    ([1] Norwegian Marine Technology Research Institute (MARINTEK), Trondheim, Norway.
    [2] Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Trondheim, Norway)

  • Haakon Lindstad

    (Norwegian Marine Technology Research Institute (MARINTEK), Trondheim, Norway.)

Abstract

Shipping companies must manage their fleets efficiently in order to stay in business. Ship routing and scheduling is a complex planning problem that may play an important role in this respect. We present TurboRouter, a decision support system that has been developed to aid planners in this process. The system includes both functions for manual planning and advanced optimisation algorithms for automatic planning, as well as semi-automatic planning functions. The system is currently in use by a number of shipping companies. We report on the practical impact of using the system in the ship routing and scheduling planning process. The savings are considerable, both regarding the quality of solutions and in terms of the time used for planning. The system has also proven to function well as a support in negotiation of spot cargoes, as well as in strategic analyses such as contract optimisation and determination of optimal fleet size and mix both for existing business and in bidding processes. Maritime Economics & Logistics (2007) 9, 214–233. doi:10.1057/palgrave.mel.9100180

Suggested Citation

  • Kjetil Fagerholt & Haakon Lindstad, 2007. "TurboRouter: An Interactive Optimisation-Based Decision Support System for Ship Routing and Scheduling," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 9(3), pages 214-233, September.
    • Handle: RePEc:pal:marecl:v:9:y:2007:i:3:p:214-233
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    Citations

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    Cited by:

    1. Gustavo Diz & Luiz Felipe Scavarda & Roger Rocha & Silvio Hamacher, 2014. "Decision Support System for PETROBRAS Ship Scheduling," Interfaces, INFORMS, vol. 44(6), pages 555-566, December.
    2. Du, Yuquan & Chen, Qiushuang & Quan, Xiongwen & Long, Lei & Fung, Richard Y.K., 2011. "Berth allocation considering fuel consumption and vessel emissions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1021-1037.
    3. Daniel Müller & Kevin Tierney, 2017. "Decision support and data visualization for liner shipping fleet repositioning," Information Technology and Management, Springer, vol. 18(3), pages 203-221, September.
    4. Hamza Bouzekri & Gülgün Alpan & Vincent Giard, 2019. "Modélisation des problèmes de l’allocation des planches et des postes à quai," Post-Print hal-02277658, HAL.
    5. Mulder, J. & Dekker, R., 2016. "Optimization in container liner shipping," Econometric Institute Research Papers EI2016-05, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Christiansen, Marielle & Fagerholt, Kjetil & Nygreen, Bjørn & Ronen, David, 2013. "Ship routing and scheduling in the new millennium," European Journal of Operational Research, Elsevier, vol. 228(3), pages 467-483.
    7. Nikolaos P. Rachaniotis & Marisa Masvoula, 2020. "A decision tool for scheduling fleets of fuel supply vessels," Operational Research, Springer, vol. 20(3), pages 1543-1557, September.
    8. Mansouri, S. Afshin & Lee, Habin & Aluko, Oluwakayode, 2015. "Multi-objective decision support to enhance environmental sustainability in maritime shipping: A review and future directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 78(C), pages 3-18.

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