EDI Technique and its Application in Shipping Transportation

EDI is an abbreviation for Electronic Data Interchange. It can make different enterprise and organization interchange trade and commercial datas through Computers in according with the standardized specification. Since it can save various of documents,we also call it"paperless" operation. The development of information technique from primary to advanced, which computers had been used as main part,involves three stages: The first stage is that computers bad been used to replace man to process various of business and documents inside the enterprise. The second stage is the network sys-

Experimental Study on the Resistance of a Transport Ship Navigating in Level Ice

This study investigates the resistance of a transport ship navigating in level ice by conducting a series of model tests in an ice tank at Tianjin University. The laboratory-scale model ship was mounted on a rigid carriage using a one-directional load cell and then towed through an ice sheet at different speeds. We observed the ice-breaking process at different parts of the ship and motion of the ice floes and measured the resistances under different speeds to determine the relationship between the ice-breaking process and ice resistance. The bending failure at the shoulder area was found to cause maximum resistance. Furthermore, we introduced the analytical method of Lindqvist （1989） for estimating ice resistance and then compared these calculated results with those from our model tests. The results indicate that the calculated total resistances are higher than those we determined in the model tests.

A fine-grained perspective on the robustness of global cargo ship transportation networks

The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transportation network based on the most recent Automatic Identification System（AIS） data available. First, we subdivide three typical cargo ship transportation networks（i.e., oil tanker, container ship and bulk carrier） from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, including random attack and three intentional attacks（i.e., degree-based attack, betweenness-based attack and flux-based attack） to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1） compared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation; 2） different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3） small-scale intentional attacks may have significant influence on the integrity of the container network but a minor impact on the bulk carrier and oil tanker transportation networks. These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation system.Abstract： The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transporta- tion network based on the most recent Automatic Identification System （AIS） data available. First, we subdivide three typical cargo ship transportation networks （i.e., oil tanker, container ship and bulk carrier） from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, in- cluding random attack and three intentional attacks （i.e., degree-based attack, between- ness-based attack and flux-based attack） to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1） com- pared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation 2） different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3） small-scale intentional attacks may have significant influence on the integrity of the con- tainer network but a minor impact on the bulk carrier and oil tanker transportation networks.These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation system.

Comparative analysis on the fuel consumption prediction model for bulk carriers from ship launching to current states based on sea trial data and machine learning technique

MARPOL 73/78,Chapter IV is considered to reveal the emission control engineering on ships.The probability model of the fuel oil consumption is established based on the machine learning technique.The proposed methods are applied into this research in order to establish the probability model of fuel oil consumption.The combination of Monte Carlo(MC)simulation method with Artificial Neural Networks(ANNs)is an optimal solution to deal the fuel consumption of marine main diesel engine.The sample data has been established based on the Monte Carlo simulation method.The model of fuel oil consumption is designed by Artificial Neural Network method.The proposed prediction model of fuel oil consumption is based on a back-propagation training algorithm of ANNs method.The research results of proposed model have been verified with the actual operation data that have been collected from a certain bulk carrier of VINIC shipping transportation company in Vietnam.The collected data is the actual operation parameters from the noon-log report of voyage during two years of the ship.The probability model of fuel oil consumption for main diesel engine is very useful in the field of ships energy efficiency management with higher accurancy than the other previous models.