The network design problem in liner shipping is of increasing importance in a strongly competitive market where potential cost reductions can influence market share and profits significantly. In this paper the network design and fleet assignment problems are combined into a mixed integer linear programming model minimizing the overall cost. To better reflect the real-life situation we take into account the cost of transhipment, a heterogeneous fleet, route dependant capacities, and butterfly routes. To the best of our knowledge it is the first time an exact solution method to the problem considers transhipment cost. The problem is solved with branch-and-cut using clover and transhipment inequalities. Computational results are reported for instances with up to 15 ports.

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Shortest path problems appear as subproblems in numerous optimization problems. In most papers concerning multiple objective shortest path problems, additivity of the objective is a de-facto assumption, but in many real-life situations objectives and criteria, can be non-additive. The purpose of this paper is to give a general framework for dominance tests for problems involving a number of non-additive criteria. These dominance tests can help eliminate paths in a dynamic programming framework when using multiple objectives. Results on real-life multi-objective problems containing non-additive criteria are reported. We show that in many cases the framework can be used to efficiently reduce the number of generated paths.

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Major liner shipping companies offer pre-and end-haulage as part of a door-to-door service. However, pre-and end-haulage is one of the major bottlenecks in liner shipping due to the lack of coordination between the customers often leading to inefficiency. In this paper we apply techniques from vehicle routing problems to the problem of pre-and end-haulage of containers, and test it on data from a major liner shipping company. The paper contains several versions of the problem such as multiple empty container depots, and balancing the empty container depot levels and their influence on the cost is shown. Finding a set of optimal vehicle routes allowing these combinations is NP-hard. However, by exploring the fact that the number of possible routes in the considered case is quite limited, we show that the model can be solved within a minute by using column enumeration. Alternative constraints and problem formulations are considered. Computational results are reported on real-life data from a major liner shipping company.

 Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

In 2018 the International Maritime Organization (IMO) agreed to cut the shipping sector's overall CO2 output by 50% by 2050. One of the key methods in reaching this goal is to improve operations to limit fuel consumption. However, it is difficult to optimize speed for a complete liner shipping network as routes interact with each other, and several business constraints must be respected. This paper presents a unified model for speed optimization of a liner shipping network, satisfying numerous real-life business constraints. The speed optimization is in this research achieved by rescheduling the port call times of a network, thus, the network is not changed. The business constraints are among others related to transit times, port work shifts and emission control areas. Other restrictions are fixed times for canal crossing, speed restrictions in the piracy areas and desire for robust solutions. Vessel sharing agreements and other collaboration between companies must also be included. The modeling of the different restrictions is described in detail and tested on real-life data. The scientific contribution of this paper is threefold: We present a unified model for speed optimization together with numerous business constraints. We present a general framework for handling routes with different frequencies. Moreover, we present a bi-objective model for balancing robustness of schedules against fuel consumption. The tests show that the real-life requirements can be handled by mixed integer programming and that the model finds significant reductions of bunker consumption and cost for large-scale real-life instances.