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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.

The marine container chassis continues to plague the maritime industry in the U.S., with chassis dislocation being a major recurring problem. To prevent chassis dislocation and maintain fluidity of supply chains, chassis pool operators at container ports face the challenging and recurring task of repositioning chassis between the various chassis storage sites within the port complex. To complement prior work, in this paper, a model to support chassis repositioning is presented for the case when the probability distribution of chassis demand is known. A case study using empirical chassis usage data illustrates the model in a practical setting.

The rise in natural and man-made disasters in recent years has led to an increased interest in emergency evacuation planning. Athough the vast majority of the existing evacuation planning models assumes system optimal (cooperative) behavior, recent research has shown that during large evacuations people tend to exhibit selfish (noncooperative) behavior. This article presents a hybrid bilevel model that balances both behavioral assumptions (in the upper level, shelter assignment occurs in a system optimal fashion, whereas evacuees are free to choose how to reach their assigned shelters in the lower level), hence providing a model that is more in line with the current state-of-the-knowledge of human behavior during disasters. The proposed model is solved using a simulated annealing algorithm. A hypothetical evacuation scenario in Sioux Falls, South Dakota, illustrates the proposed model. We demonstrate that the resulting evacuation strategies can be significantly different from conventional system optimal evacuation plans. [ABSTRACT FROM AUTHOR]

Periodic infrastructure maintenance is crucial for a safe and efficient transportation system. Numerous decision models for the maintenance planning problem have been proposed in the literature. However, to the best of our knowledge, no model exists that simultaneously accounts for traffic dynamics and is intended for long-term planning purposes. This article addresses this gap in the literature. A mixed-integer bi-level program is introduced that minimizes the long-term maintenance cost as well as the total system travel time. For the solution approach we utilize a genetic algorithm in conjunction with mesoscopic traffic simulation. The model is illustrated via a numerical example. [ABSTRACT FROM AUTHOR]