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This paper contributes to the literature by deriving upper and lower bounds on the fuel consumption in container shipping. The bounds are derived from sailing distances, port times, and the possible arrival times at ports/ the berth windows negotiated between the ocean carrier and the port operators. Crucially, the derived bounds can be used in conjunction with any of the common fuel consumption functions proposed in the literature. This latter is especially important since currently there is no consensus on a specific functional form for the fuel consumption function. The behavior of the bounds will be illustrated with numerical examples.

The maritime industry is witnessing an increasingly loud call to contribute to the global trend towards environmental sustainability and decarbonization. One possible policy measure is the use of speed limits to reduce harmful emissions from ocean shipping. While the idea of slowing down ships to obey speed limits might seem intuitive, one key distinguishing characteristic of container shipping is that it is designed to adhere to a fixed, typically weekly, sailing schedule. This paper contributes to the literature investigating the feasibility of speed limits by demonstrating that speed limits are not always feasible in container shipping because of the rigid scheduling requirement. Conditions under which speed limits would become infeasible are derived, illustrated, and discussed.

With the growth in global trade and its environmental footprint, sustainable modes of freight movement are increasingly important in today’s globalized world. This study focuses on on-dock rail, where the rail terminal is located within the marine container terminal. On-dock rail has in recent years become an essential mode of transportation to move containers out of congested marine container terminals. This study contributes to the literature by presenting tailored exact solution algorithms for a recently proposed optimization model to optimize the loading of double-stack trains. In particular, a 3-stage solution framework is presented for the case when rail cars have a single well. A complementary solution algorithm for the case when a rail car has multiple wells is also provided. Our results show that computational times can be drastically reduced, from 2+ hours to a matter of seconds, rendering the model suitable for practical use.