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Container shipping route design incorporating the costs of shipping, inland/feeder transport, inventory and CO2 emission

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Abstract

As container shipping networks have become important components in global supply chains, route design should take both maritime and inland factors into consideration. In this article, a model is proposed to optimise container flows between two continents via an end-to-end service. The model is concerned not only with the design of an optimal shipping route but also with inland connections between hinterlands and ports. The objective is to minimise total costs, consisting of ship costs, port costs, inland/feeder transport costs, inventory costs and CO2 costs. The model is applied to the actual trade between Europe and the United States. Computational outcomes show that ship costs and port costs (port dues and terminal handling charges) represent less than one third of total costs. Therefore, the maritime network is only a part of a bigger system and piecemeal optimisation may not guarantee the optimisation of the whole network. Inland/feeder transport costs contribute the most to total costs, and they are influenced significantly by port choice. Although the use of a greater number of ports results in longer distances and higher shipping costs, this benefits in terms of lower distribution costs between hinterlands and ports. Inventory costs play a considerable part in total costs and they increase as vessel capacity goes up. In other words, these costs present a barrier to the introduction of bigger vessels. Optimal size is obviously a trade-offs between inventory and shipping costs.

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Notes

  1. Our model would obviously have been stronger if ship size were a decision variable. However, without the assumption of fixed size, the solution space would become very large and it is infeasible to find a good solution.

  2. On the one hand, mega vessels result in scale economies at sea. On the other hand, they cause diseconomies in ports. Whereas the saving of ship costs has been widely accepted, that of port costs has been still questionable, especially with high pressure of mega vessels on port investment and operation (see more in Dragovic et al, 2010; Saanen, 2013; Tran and Haasis, 2015a). In our research, port costs are mainly retrieved from Brouer et al (2014). According to their work, unit port costs will decrease as ships become bigger. This outcome may be debatable. Nevertheless, as far as we know, their model is the only one in the market that can provide cost estimation of key ports in the world. In addition, it is created using operational data of Maersk Line, which more or less ensures its reasonableness.

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Acknowledgements

The authors are thankful to the editors and reviewers of MEL for many detailed comments and corrections; they have been extremely helpful in terms of improving the quality of this article. The authors are also thankful to Mr Tonny Bennet (PIERS – UK) for providing container trade data between Europe and the United States; this research could not have been done without it. Many thanks are also due to numerous experts for their advice regarding the more practical aspects of shipping operations. The co-operative junior research group on Computational Logistics is funded by the University of Bremen in line with the Excellence Initiative of German federal and state governments.

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Authors and Affiliations

  1. Faculty of Business Studies & Economics, University of Bremen, Wilhelm-Herbst-Str. 12, Bremen, 28359, Germany

    Nguyen Khoi Tran, Hans-Dietrich Haasis & Tobias Buer

  2. Chair in Maritime Business and Logistics, Bremen, Germany

    Nguyen Khoi Tran & Hans-Dietrich Haasis

  3. Computational Logistics, Cooperative Junior Research Group of University of Bremen and ISL - Institute of Shipping Economics and Logistics, Bremen, Germany

    Tobias Buer

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  1. Nguyen Khoi Tran
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Tran, N., Haasis, HD. & Buer, T. Container shipping route design incorporating the costs of shipping, inland/feeder transport, inventory and CO2 emission. Marit Econ Logist 19, 667–694 (2017). https://doi.org/10.1057/mel.2016.11

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