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The paper aims to identify the contributors to freight rate fluctuations in the Suezmax tanker market; this study selected the refinery output, crude oil price, one-year charter rate and fleet development as the main influencing factors for the market analysis.

The paper used the vector error correction model to evaluate the degree of impact of each influencing factor on Suezmax tanker freight rates, as well as the interplay between these factors.

The conclusion and results were tested using the 20-year data from 1999 to 2019, and the methodology and theory of this paper were proved to be effective. Results of this study provide effective reference for scholars to find the law of fluctuations in Suezmax tanker freight rates.

This paper provides a decision-making support tool for tanker operators to cope with fluctuation risks in the tanker shipping market.

Recently Foster and Ryan developed a new method for the vehicle scheduling problem (VSP). This article applies their method to a practical VSP involving the delivery of pressurised fluids, and demonstrates its effectiveness.

The purpose of this paper is to verify claims that water supplied by operators of tanker trucks in Cape Coast does not meet quality standards recommended for human consumption, and to investigate the sources of any contamination.

Samples were collected from a water hydrant from which tanker operators draw water from the Ghana Water Company Limited distribution system in Cape Coast and a number of tankers sampled at random. Additional samples were taken from the premises of a patron of the tanker service and a regular customer of the Ghana Water Company Limited. All samples were subjected to physico‐chemical and bacteriological analyses and the results compared with the World Health Organization's guidelines for drinking water.

It was found out that water supplied by the tanker operators indeed failed to meet the World Health Organization's guidelines for some quality parameters as alleged by patrons of the service. The tanker‐supplied water was found to contain high levels of Escherichia coli, colour, turbidity and total iron. This was found to arise from the management of the water hydrant and the tankers by the Ghana Water Company Limited and the tanker operators respectively.

The study provides a basis for the set of actions that must be taken to safeguard public health and consumer confidence in drinking water supply using tankers as an emerging alternative to conventional water supply in urban centres of the developing world.

This paper aims to propose a machine learning-based automatic labeling methodology for chemical tanker activities that can be applied to any port with any number of active tankers and the identification of important predictors. The methodology can be applied to any type of activity tracking that is based on automatically generated geospatial data.

The proposed methodology uses three machine learning algorithms (artificial neural networks, support vector machines (SVMs) and random forest) along with information fusion (IF)-based sensitivity analysis to classify chemical tanker activities. The data set is split into training and test data based on vessels, with two vessels in the training data and one in the test data set. Important predictors were identified using a receiver operating characteristic comparative approach, and overall variable importance was calculated using IF from the top models.

Results show that an SVM model has the best balance between sensitivity and specificity, at 93.5% and 91.4%, respectively. Speed, acceleration and change in the course on the ground for the vessels are identified as the most important predictors for classifying vessel activity.

The study evaluates the vessel movements waiting between different terminals in the same port, but not their movements between different ports for their tank-cleaning activities.

The findings in this study can be used by port authorities, shipping companies, vessel operators and other stakeholders for decision support, performance tracking, as well as for automated alerts.

This analysis makes original contributions to the existing literature by defining and demonstrating a methodology that can automatically label vehicle activity based on location data and identify certain characteristics of the activity by finding important location-based predictors that effectively classify the activity status.

Scandia, Inc., is a commercial vessel management company located in the New York Metropolitan area and is part of a family of firms including Scandia Technical; International Tankers, Ltd.; Global Tankers, Ltd.; Sun Maritime S.A.;Adger Tankers AS; Leeward Tankers, Inc.; Manhattan Tankers, Ltd.; and Liuʼs Tankers, S.A. The companyʼs current market niche is the commercial management of chemical tankers serving the transatlantic market with a focus on the east and gulf coast of the United States and Northern Europe. This three-part case describes the commercial shipping industry as well as several mishaps that the company and its President, Chris Haas, have had to deal with including withdrawal of financial support by creditors, intercorporate firm conflict, and employee retention. Part A, which was published in the Fall 2010 issue, presented an overview of the commercial vessel industry and set the stage for Parts B and C where the firm℉s operation is discussed.