Maritime Transport Research最新文献

The Kiel Canal is a two-way waterway that connects the Baltic Sea and the North Sea. The canal consists of an alternating sequence of narrow transit segments and wider siding segments. This calls for solving a ship scheduling problem to decide which ships have to wait in sidings to let opposing traffic pass through such that the total traversing time of all ships is minimized. This paper extends previous studies on scheduling ships through the Kiel Canal by considering that the arrival times of the ships at the entrance to the canal are subject to uncertainty. This is a major challenge in the planning as it gives frequent need of replanning to make the schedules feasible. We propose a mathematical formulation for the problem to mitigate the negative effects of the uncertainty. This formulation incorporates time-corridors, so that the schedule will still be valid as long as the ships arrive within their given time-corridors. To solve real-sized instances of the problem, we adapt a matheuristic that adds violated constraints iteratively to the problem. The matheuristic was tested within a rolling horizon simulation framework to study the effect of arrival time uncertainty. We show by experiment that solutions of the matheuristic for different time-corridor widths can be used to identify a suitable corridor width that trades off the average traversing time of ships and the number of reschedules required in the planning. A simple myopic heuristic, reflecting the current scheduling practice, was used to generate benchmark results, and tests on real data showed that the matheuristic provides solutions with significantly less need of replanning, while at the same time keeping the total traversing times for the ships short. We also provide simulations to gain insight about the effect on the ships’ average traversing time from upgrading the narrow transit segments.

This paper discusses the efficiency of cooperation among ports under congested conditions and determines an approach for strategic cooperation between ports through model analysis. We develop a bilevel optimization model based on the benefit maximization problem of port authorities and cost minimization problem of cargo carriers. We consider several port cooperation scenarios in the numerical computation. From the computation results, the following conclusions are derived: (1) when each port authority prioritizes its own profit, port cooperation in a congested port would be successful because the cooperation can be beneficial for the carrier; (2) when each port authority considers the benefit of its local customers in terms of shipping costs related to both economies of density and port congestion, port cooperation in a congested port would rarely be successful because the cooperation cannot be beneficial for the carrier and the carrier cannot support it.

The novel coronavirus (COVID-19), due to the limited supply of vaccines, put a strain on worldwide economy, also on the maritime sector. As a result, the adoption of non-pharmaceutical interventions to limit the biological agent's spread became fundamental. Such preventing actions can be performed in accordance with various International and National Regulations even though not specifically issued for the maritime sector. In this context, the authors introduce a new methodology for biological risk management on-board ships using a qualitative risk matrix. Moreover, with respect to the traditional approach, an importance weight scale was added, in order to classify the different on-board activities. To perform a comparative analysis between the new and the traditional approach, a case study based on a cargo ship was carried out.

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.