A scenario on the evolving road service informatics in Europe

Road Transport Informatics is being developed to improve road transport. Telecommunication links will connect microcomputers in vehicles with roadside beacons and traffic control centers. Drivers can get better information and guidance en route. If these functions are charged as services rather than sold as products, a better traffic can be obtained. This can improve traffic safety, efficiency and the environment.     

A simulation-based probabilistic design method for arctic sea transport systems

When designing an arctic cargo ship, it is necessary to consider multiple stochastic factors. This paper evaluates the merits of a simulation-based probabilistic design method specifically developed to deal with this challenge. The outcome of the paper indicates that the incorporation of simulations and probabilistic design parameters into the design process enables more informed design decisions. For instance, it enables the assessment of the stochastic transport capacity of an arctic ship, as well as of its long-term ice exposure that can be used to determine an appropriate level of ice-strengthening. The outcome of the paper also indicates that significant gains in transport system cost-efficiency can be obtained by extending the boundaries of the design task beyond the individual vessel. In the case of industrial shipping, this allows for instance the consideration of port-based cargo storage facilities allowing for temporary shortages in transport capacity and thus a reduction in the required fleet size / ship capacity.     

A two-stage optimization approach for sulphur emission regulation compliance

In this paper, we present a two-stage optimization model for the machinery system selection problem. The objective is to minimize total cost, while aggregated power requirement and emission regulations are constraining the problem. Future fuel prices are considered to be uncertain. From a set of alternatives, the machinery configuration providing the lowest total cost is found. Also design flexibility in terms of future reconfiguration possibilities is taken into account. The machinery selection for a 2000 TEU container vessel is used as an illustrative case. Five initial machinery concepts are considered: diesel machinery, diesel machinery with a scrubber system, dual fuel (DF) machinery, pure gas engines, and a DF ready machinery. There is also a set of reconfiguration possibilities available for each alternative. From solving the case study, DF machinery is found optimal, while pure gas machinery is close to equally good. By solving the problem with deterministic fuel prices, the value of flexibility is not properly accounted for, resulting in an unreasonably high total cost for the flexible machinery alternatives. This demonstrates the need for a decision support approach that explicitly handles future uncertainty, as the two-stage stochastic model presented in this paper does.     

Norwegian port connectivity and its policy implications

The importance of a seaport depends on how well it is connected in a transportation network. A port’s connectivity is therefore one of the key issues in determining its competitiveness and developments in regions and countries. We construct a port connectivity index for major Norwegian ports based on a unique dataset derived from the automated identification system (AIS) for multiple vessel types over a 7-year period. Port connectivity is evaluated empirically by the number of unique vessel visits, vessel sizes, and cargo sizes. The research has implications for port authorities and policy makers in the areas of port planning, infrastructure investment, short sea shipping promotion, and environmental policies. The contributions of this research are twofold. First, the methodology linking the AIS vessel-tracking system with port connectivity is a pioneering empirical application of maritime big data. Second, the port connectivity index is constructed for multiple vessel types and regional port groups, which is an improvement from the current literature where conceptual measures are constructed based on hypothetical and usually too simple optimization rules. The methodology can be easily expanded to other regions in the world.     

Optimized selection of vessel air emission controls—moving beyond cost-efficiency

Shipping currently has an unexploited potential for improved energy efficiency and reduced emissions to air. Many existing air emission controls have been proved to be cost-efficient but are still not commonly installed on board vessels. This paper discusses the so-called ‘energy paradox’ in maritime transportation, presenting barriers to overcome and criteria to consider when selecting cost-efficient air emission controls. Current approaches typically select available controls based on their cost-effectiveness. While this is an important aid in the decision-making process, and, in relative terms, easy to quantify, it is not a sufficient criterion to capture the true preferences of the decision-maker. We present in this paper a multi-criteria optimization model for the selection of air emission controls. This decision framework can also incorporate subjective and qualitative factors, and is applied to the shipping company Grieg Shipping. A survey among internal Grieg Shipping stakeholders identifies the important criteria to consider, their relative importance, and the scoring of the controls. This empirical data is used as parameters in the model and the model is then applied on a vessel of the Grieg Shipping fleet. The results show that nonfinancial factors play an important role in the selection of air emission controls in shipping.     

Planning vessel air emission regulations compliance under uncertainty

In this paper we consider the reduction of air emissions from vessels when uncertainty is taken into account. Uncertainty in the reduction effects of the different existing air emission controls is currently high and makes their selection for vessel emission regulations compliance a challenging process. We develop a two-stage stochastic optimization model that addresses this uncertainty. The model’s objective is to plan the installation of air emission controls over a specified time horizon for a vessel to comply in the most cost-efficient way with the air emission regulations. The uncertain reduction effects of the controls are modelled by a set of scenarios. The approach is applied to a case study with real data. The solution exposes the important impact of uncertainty on this problem, especially on the SO _X reduction, while the CO₂ reduction plan seems in this case not affected by uncertainty.