An application of BBNs on the integrated energy efficiency of ship–port interface: a dry bulk shipping case

ABSTRACT

A considerable amount of energy is consumed with every completed ship voyage and each port operation performed. Recent regulative approaches by the IMO are to enforce the shipping industry to become more energy efficient and sustainable. There is a growing amount of literature on the energy efficiency management of ships and ports. However, there is still inadequate research effort on the ship–port interface in order to obtain an Integrated Energy Efficiency framework for marine transport operations. In this research, a theoretical concept is generated to measure holistic energy efficiency in shipping operations. The main purpose of this paper is to identify nodes of the integrated ship-port energy efficiency framework and develop a probabilistic approach, which can help to increase energy efficiency and reduce CO2 emissions for shipping companies. This research generates an application of BBNs on ship–port interface regarding the integrated operational energy efficiency interactions by aiming to optimise holistic operational energy efficiency and CO2 emissions. The outcomes suggest that it would be possible to increase the probability of a more energy efficient and sustainable marine transport operation by integrating the influences of port and ship operation performances and their elements on the related route planning and slow steaming decision-making.     

An advanced solution for efficient logistics management at both company and network level

This paper proposes an advanced solution for efficient logistics management in urban areas based on a unified scheme able to address both static and dynamic decision making at a company and network level. The proposed solution generates the most efficient urban distribution plan utilizing an evolutionary metaheuristic approach and a backpressure framework that provide competitive scheduling and routing decisions. An empirical study based on real data is conducted assessing the performance of the proposed advanced solution and the reported results of the evaluation experiments demonstrate its generality and robustness.     

Neural localization of acoustic emission sources in ship hulls

In this paper a radial-basis-function neural network is used to localize acoustic emission events in ship hulls. It is shown that using a tiny network configuration and a small set of robust features, selected automatically by the K-means algorithm from a superset of 90 signal parameters, the location of a single event can be classified efficiently into three typical areas found in ship hulls. In simulation experiments, where a stiffened plate model is partially sunk into the water, the localization rate of acoustic emission events in a noise-free environment is 100% using four sensors and only four features per sensor. In the proposed method, the feature set is adapted and estimated automatically in cases of noisy environments. Robust acoustic emission localization rates, greater than 90%, are achieved using less than ten features per sensor in case of additive white Gaussian noise at 0 dB SNR or more.