Benchmarking the energy efficiency of ships is not a straightforward task, mainly due to the diversity of operations. Although driving cycles have been used for decades in evaluating the performance of road vehicles, these do not exist in formal policy-making for maritime transport. This work builds on a previously proposed methodology. It uses noon reports of 327 vessels for 2019 to construct operational cycles for seven size classes of container ships using the main engine power as the main parameter. Concerning the main engine emissions, the resulting cycles reduce variation in the carbon intensity indicator values by more than 30% while maintaining an average accuracy of 97.7% in absolute emissions. These figures show that the concept can improve operational carbon intensity indicators in terms of robustness and their technical counterparts in optimizing ship design. The paper also proposes further work required for benchmarking applications in policy-making.
Operational cycles for maritime transportation is a new concept to improve the assessment of ships’ energy efficiency and offer benchmarking options among similar ship types and sizes. This work extends previous research to consolidate the methodology, bring more comprehensiveness, and provide a more holistic assessment of these operational cycles. The cycles are designed from noon reports from a fleet of around 300 container ships divided into eight size groups. The comparison between cycles derived from speed and draft with those based on main engine power identifies that the cycles based on speed and draft are more accurate and allow for estimating the Energy Efficiency Operational Index but require more data. The main-engine-power cycles are more effective in benchmarking through the Annual Efficiency Ratio. These cycles reduce the inherent variability of the carbon intensity indicator and present good opportunities as a benchmarking tool for strengthening the regulatory framework of international shipping.
Roll-on roll-off (RoRo) shipping plays an important role in freight transport on the European continent, and is faced with the challenge of reducing its CO2 emissions while increasing its efficiency. Dual cycling, in which loading and discharging processes are carried out simultaneously, achieves this goal by reducing the turnaround time of vessels in port and thus the CO2 output of handling equipment in port and fuel consumption through slow steaming at sea. Optimizing the dual cycling operations on RoRo vessels has not yet been investigated in the literature. This paper presents the novel RoRo dual cycling problem (RRDCP), and formulates it using integer programming (IP) with the objective to minimize the total makespan of discharging and loading operations. We further prove that the RRDCP is NP-complete by a reduction from a general machine scheduling problem, and introduce a novel heuristic to solve the problem called a generalized random key algorithm (GRKA). We evaluate the IP model and GRKA approach on both generated and industrial instances, showing that the GRKA heuristic finds optimal or near-optimal solutions to real-world problems in just seconds. We provide managerial insights on industrial instances, which indicate that our approach leads to a reduction in fuel consumption and CO2 emissions of up to 25% for RoRo operations.
Ship designers face increasing pressure to comply with global emission reduction ambitions. Alternative fuels, potentially derived from bio-feedstock or renewable electricity, provide promising solutions to this problem. The main challenge is to identify a suitable ship power system, given not only uncertain emission requirements but also uncertain fuel and carbon emission prices. We develop a two-stage stochastic optimization model that explicitly considers uncertain fuel and carbon emission prices, as well as potential retrofits along the lifetime. The bi-objective setup of the model shows how the choice of optimal power system changes with reduced emission levels. Methanol and LNG configurations appear to be relatively robust initial choices due to their ability to run on fuel derived from different feedstocks, and their better retrofittability towards ammonia or hydrogen. From a policy perspective, our model provides insight into the effect of the different types of carbon pricing mechanisms on a shipowner's decisions.
Seaborne trade is the lynchpin in almost every international supply chain, and about 90% of non-bulk cargo worldwide is transported by container. In this survey we give an overview of data-driven optimization problems in liner shipping. Research in liner shipping is motivated by a need for handling still more complex decision problems, based on big data sets and going across several organizational entities. Moreover, liner shipping optimization problems are pushing the limits of optimization methods, creating a new breeding ground for advanced modelling and solution methods. Starting from liner shipping network design, we consider the problem of container routing and speed optimization. Next, we consider empty container repositioning and stowage planning as well as disruption management. In addition, the problem of bunker purchasing is considered in depth. In each section we give a clear problem description, bring an overview of the existing literature, and go in depth with a specific model that somehow is essential for the problem. We conclude the survey by giving an introduction to the public benchmark instances LINER-LIB. Finally, we discuss future challenges and give directions for further research.
Major liner shipping companies offer pre- and end-haulage as part of a door-to-door service, but unfortunately pre- and end-haulage is frequently one of the major bottlenecks in efficient liner shipping due to the lack of coordination between customers. In this paper, we apply techniques from vehicle routing problems to schedule pre- and end-haulage of containers, and perform tests on data from a major liner shipping company. The paper considers several versions of the scheduling problem such as having multiple empty container depots, and having to balance the empty container depot levels. The influence of the side constraints on the overall cost is analysed. 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 use of column enumeration. Alternative constraints and problem formulations, such as balancing empty container storage level at depots, are considered. Computational results are reported on real-life data from a major liner shipping company.
The design of emission control areas (ECAs), including ECA width and sulfur limits, plays a central role in reducing sulfur emissions from shipping. To promote sustainable shipping, we investigate an ECA design problem that considers the response of liner shipping companies to ECA designs. We propose a mathematical programming model from the regulator’s perspective to optimize the ECA width and sulfur limit, with the aim of minimizing the total sulfur emissions. Embedded within this regulator’s model, we develop an internal model from the shipping liner’s perspective to determine the detoured voyage, sailing speed, and cargo transport volume with the aim of maximizing the liner’s profit. Then, we develop a tailored hybrid algorithm to solve the proposed models based on the variable neighborhood search meta-heuristic and a proposition. We validate the effectiveness of the proposed methodology through extensive numerical experiments and conduct sensitivity analyses to investigate the effect of important ECA design parameters on the final performance. The proposed methodology is then extended to incorporate heterogeneous settings for sulfur limits, which can help regulators to improve ECA design in the future.
Maritime shipping is the transmission belt of the global economy. It is also a major contributor to global environmental change through its under-regulated air, water and land impacts. It is puzzling that shipping is a lagging sector as it has a well-established global regulatory body—the International Maritime Organization. Drawing on original empirical evidence and archival data, we introduce a four-factor framework to investigate two main questions: why is shipping lagging in its environmental governance; and what is the potential for the International Maritime Organization to orchestrate emerging private ‘green shipping’ initiatives to achieve better ecological outcomes? Contributing to transnational governance theory, we find that conditions stalling regulatory progress include low environmental issue visibility, poor interest alignment, a broadening scope of environmental issues, and growing regulatory fragmentation and uncertainty. The paper concludes with pragmatic recommendations for the International Maritime Organization to acknowledge the regulatory difficulties and seize the opportunity to orchestrate environmental progress.
Recent trends in port development show that ports are making increasing efforts to forge mutually beneficial cooperation strategies, particularly ports sharing a common hinterland. In this paper, we analyse the North Adriatic ports (Koper, Rijeka, Trieste and Venice) with a focus on two related themes. First, the complementarity of the North Adriatic (NA) ports in the container market is analysed based on port vessel service patterns and shipping line interviews. We operationalize the analysis of complementarity with an analysis of the effects of multiple port calls on the revenue required to make a call in a specific NA port economically feasible. We conclude that the inclusion of another NA port reduces the minimum required revenue for a call in an additional NA port.
Second, we assess the scope and depth of cooperation between ports. We map current and potential future cooperation using a 'cooperation matrix' with two dimensions: the involvement of stakeholders (limited vs. broad), and the depth of cooperation (pre-competitive vs. commercial). We use in-depth interviews with port authorities, terminal operators, rail operators, major shipping lines and forwarders in the NA region to position the NA ports in the matrix. We conclude by discussing prospects of future NAPA ports cooperation.
Port selection is of vital importance for both port operators and shipping lines. In this contribution, an Automatic Identification System (AIS) big data approach is developed. This approach allows identifying container ships using only AIS data without the need for supplementary information from commercial databases. This approach is applied to investigate the port selection statistics of container ships between Shanghai and Ningbo Zhoushan Port, two of the largest ports in the world in terms of calling frequency, to generate practical insights. Results show that: i) the ratios among large ships, medium ships and small ships of these two ports are both approximately 1: 4: 5; ii) these two ports both have an exclusive (i.e., more feeder ports covered in geographical coverage) and intensive (i.e., more feeder ships deployed in shipping service frequency) collection and distribution network mainly consisting of small ships, but that of Shanghai is more intensive; iii) in terms of ultra-large ships over 380 m, Shanghai has accommodated an extra 18.5% compared to that of Ningbo Zhoushan, this indicates Shanghai's attraction for such vessels in global fleet deployment; iv) the feeder network between Shanghai and Ningbo Zhoushan is weak, and their relationship is actually in competition; v) Ningbo Zhoushan could offer more choices for ultra-large container ships (over 380 m), which implies its greater potential in future port competition; vi) when the depth of channels and berths is sufficient, the distance to hinterland and the convenience of a collection and distribution network begin to get more important in port selection. The empirical findings unveil the decision-making of container lines, competition between ports and implications for shipping policy.