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 nonlinear model predictive control formulation for obstacle avoidance in high-speed autonomous ground vehicles in unstructured environments

This paper presents a nonlinear model predictive control (MPC) formulation for obstacle avoidance in high-speed, large-size autono-mous ground vehicles (AGVs) with high centre of gravity (CoG) that operate in unstructured environments, such as military vehicles. The term ‘unstructured’ in this context denotes that there are no lanes or traffic rules to follow. Existing MPC formulations for passenger vehicles in structured environments do not readily apply to this context. Thus, a new nonlinear MPC formulation is developed to navigate an AGV from its initial position to a target position at high-speed safely. First, a new cost function formulation is used that aims to find the shortest path to the target position, since no reference trajectory exists in unstructured environments. Second, a region partitioning approach is used in conjunction with a multi-phase optimal control formulation to accommodate the complicated forms the obstacle-free region can assume due to the presence of multiple obstacles in the prediction horizon in an unstructured environment. Third, the no-wheel-lift-off condition, which is the major dynamical safety concern for high-speed, high-CoG AGVs, is ensured by limiting the steering angle within a range obtained offline using a 14 degrees-of-freedom vehicle dynamics model. Thus, a safe, high-speed navigation is enabled in an unstructured environment. Simulations of an AGV approaching multiple obstacles are provided to demonstrate the effectiveness of the algorithm.     

Improving the robustness of an MPC-based obstacle avoidance algorithm to parametric uncertainty using worst-case scenarios

Previous work by the authors focused on obstacle avoidance in large, high-speed autonomous ground vehicles within unknown and unstructured environments. This work resulted in a nonlinear model predictive control based algorithm that simultaneously optimises both the speed and steering commands. The algorithm can exploit the dynamic limits of the vehicle to navigate it to a target position as quickly as possible without compromising safety. In the algorithm, a model of the vehicle is used explicitly to predict and optimise future actions, but in practice the model parameter values are not known exactly. Thus, in this paper, the robustness of the algorithm to parametric uncertainty is evaluated. It is first demonstrated that using nominal parameter values in the algorithm leads to safety issues in 24% of the evaluated scenarios with the considered parametric uncertainty distributions. To improve the algorithm's robustness, a novel double-worst-case formulation is developed that simultaneously accounts for the robust satisfaction of the two safety requirements of high-speed obstacle avoidance: collision-free and no-wheel-lift-off. Results from simulations with stratified random scenarios and worst-case scenarios show that the double-worst-case formulation renders the algorithm robust to all uncertainty realisations tested. The trade-off between robustness and the task completion performance is also quantified.     

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.     

An inequality between two speed averages

In this note we invoke Schwartz's inequality to relate two speed averages.     

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.