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This paper aims to identify the interplay of standard Capesize optimal speeds for time charter equivalent (TCE) maximization in the Australia–China iron ore route and the optimal speeds as an operational tool for compliance with the International Maritime Organization (IMO) carbon intensity indicator (CII).

The TCE at different speeds have been calculated for four standard Capesize specifications: (1) standard Capesize with ecoelectronic engine; (2) standard Capesize with non-eco engine (3) standard Capesize vessel with an eco-electronic engine fitted with scrubber and (4) standard Capesize with non-eco engine and no scrubber fitted.

Calculations imply that in a highly inflationary bunker price context, the dollar per ton freight rates equilibrates at levels that may push optimal speeds below the speeds required for minimum CII compliance (C Rating) in the Australia–China trade. The highest deviation of optimal speeds from those required for minimum CII compliance is observed for non-eco standard Capesize vessels without scrubbers. Increased non-eco Capesize deployment would see optimal speeds structurally lower at levels that could offer CII ratings improvements.

While most of the studies have covered the use of speed as a tool to improve efficiency and emissions in the maritime sector, few have been identified in the literature to have examined the interplay between the commercial and operational performance in the dry bulk sector stemming from the freight market equilibrium. The originality of this paper lies in examining the above relation and the resulting optimal speed selection in the Capesize sector against mandatory environmental targets.

Liner shipping plays a crucial role in facilitating the movement of manufactured goods around the world. While previous literature has shown that liner shipping is an important trade driver, potential differences across trade routes and world regions have not as yet been explored. This paper examines whether the impact of liner shipping on bilateral trade flows differs significantly across world regions, as well as exploring other geographical patterns.

Using state-of-the-art gravity modelling, this paper investigates the impact of the UNCTAD's Liner Shipping Bilateral Connectivity Index on bilateral trade in manufactured goods using a comprehensive database of disaggregated trade data for the period from 2006 to 2019.

The results show that the trade effect of liner shipping is greater in long-distance and interregional bilateral flows. For some regions, such as North America and Oceania, the effect is greater than the world average, while for others, such as Africa and South America, the effect is significantly smaller. The trade effects of liner shipping connectivity on the main east–west routes are average, but clear asymmetry emerges when analysing China's inward and outward trade flows separately.

The results of this paper show that the major east–west routes determine the baseline trade effects of liner shipping, demonstrate that some north–south trades such as those involving Oceania generate larger trade effects and confirm that the trade effects of liner shipping can be improved for some world regions such as South America and Africa.

Energy groups are cargo owners with large amounts of energy sources (such as coal) to transport. To achieve a satisfactory tradeoff between the reliability requirements of the sea transportation process and the need to control the investment cost, they usually set up a self-owned fleet supplemented by a chartered fleet. This paper aims to investigate the best fleet structure and to evaluate the investment scheme under volatile circumstances in the shipping market.

The authors construct a mathematical model to determine the ratio of the self-owned fleet to the total fleet to minimize fleet operating costs. The volatility of both freight rates and oil prices is taken into consideration. The CPLEX solver is used to empirically analyze real data from an energy group in China, and the ship investment plan is evaluated considering the technical and economic feasibility.

If the ratio of the self-owned fleet to the total fleet is increased to the optimal of 90.40%, the total operating cost is reduced by 33.98%. Thus, the energy group should increase its capacity with a Panamax vessel of approximately 82,000 DWT. Purchasing a 5-year-old secondhand ship and building a new ship both have good investment return indicators.

For cargo owners engaging in transporting bulk cargo domestically in China, the suggested fleet ratio can provide a reference with a universal application scale, given the boundary economic conditions (including the volatility of freight rates and oil prices in the shipping market) in the paper.

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.

Economic studies have always underlined the cyclical trends of many industries and their different relations to the macro-economic cycles. Shipping is one of those industries and it has been often characterised by peaks that have influenced both the trade patterns and industry investment structure (e.g. fleet, shipyard activity, freight rates). One of the main issues related with the cycles is the effect on overcapacity and prices for newbuilding and how the understanding of these patterns can help in preventing short-hand strategies. The purpose of this paper is to evaluate different effects of business elements on shipbuilding activity, in relation to different economic-cycle phases.

This paper proposes a non-linear econometric model to identify the relations between shipbuilding and economic cycles over the past 30 years. The research focuses on identifying the cycle characteristics and understanding the asymmetrical effect of economic- and business-related variables on its development.

The study underlines the presence of an asymmetric effect of several business variables on the shipbuilding productions, depending on the cyclical phases (i.e. market expansion or economic slowdown). Moreover, lagged effects seem to be stronger than contemporaneous variables.

The paper is a first attempt of using non-linear modelling to shipbuilding cycles, giving indications that could be included in relevant investment policies.

Marine transportation has been faced with an increasing demand for containerized cargo during the past decade. Marine container terminals (MCTs), as the facilities for connecting seaborne and inland transportation, are expected to handle the increasing amount of containers, delivered by vessels. Berth scheduling plays an important role for the total throughput of MCTs as well as the overall effectiveness of the MCT operations. This study aims to propose a novel island-based metaheuristic algorithm to solve the berth scheduling problem and minimize the total cost of serving the arriving vessels at the MCT.

A universal island-based metaheuristic algorithm (UIMA) was proposed in this study, aiming to solve the spatially constrained berth scheduling problem. The UIMA population was divided into four sub-populations (i.e. islands). Unlike the canonical island-based algorithms that execute the same metaheuristic on each island, four different population-based metaheuristics are adopted within the developed algorithm to search the islands, including the following: evolutionary algorithm (EA), particle swarm optimization (PSO), estimation of distribution algorithm (EDA) and differential evolution (DE). The adopted population-based metaheuristic algorithms rely on different operators, which facilitate the search process for superior solutions on the UIMA islands.

The conducted numerical experiments demonstrated that the developed UIMA algorithm returned near-optimal solutions for the small-size problem instances. As for the large-size problem instances, UIMA was found to be superior to the EA, PSO, EDA and DE algorithms, which were executed in isolation, in terms of the obtained objective function values at termination. Furthermore, the developed UIMA algorithm outperformed various single-solution-based metaheuristic algorithms (including variable neighborhood search, tabu search and simulated annealing) in terms of the solution quality. The maximum UIMA computational time did not exceed 306 s.

Some of the previous berth scheduling studies modeled uncertain vessel arrival times and/or handling times, while this study assumed the vessel arrival and handling times to be deterministic.

The developed UIMA algorithm can be used by the MCT operators as an efficient decision support tool and assist with a cost-effective design of berth schedules within an acceptable computational time.

A novel island-based metaheuristic algorithm is designed to solve the spatially constrained berth scheduling problem. The proposed island-based algorithm adopts several types of metaheuristic algorithms to cover different areas of the search space. The considered metaheuristic algorithms rely on different operators. Such feature is expected to facilitate the search process for superior solutions.

This paper aims to compare different univariate forecasting methods to provide a more accurate short-term forecasting model on the crude oil price for rendering a reference to manages.

Six different univariate methods, namely the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey forecast, the hybrid grey model and the seasonal autoregressive integrated moving average (SARIMA), have been used.

The authors found that the grey forecast is a reliable forecasting method for crude oil prices.

The contribution of this research study is using a small size of data and comparing the forecasting results of the six univariate methods. Three commonly used evaluation criteria, mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percent error (MAPE), were adopted to evaluate the model performance. The outcome of this work can help predict the crude oil price.

Truck appointment systems have been applied in critical container ports in the United States due to their potential to improve handling operations. This paper aims to develop a truck appointment system to optimise the total cost experiencing at the entrance of container terminals by managing truck arrivals and the number of service gates satisfying a given level of service.

The approximation of M_t/G/n_t queuing model is applied and integrated into a cost optimisation model to identify (1) the number of arrival trucks allowed at each time slot and (2) the number of service gates operating at each time slot that ensure the average waiting time is less than a designated time threshold. The optimisation model is solved by the Genetic Algorithm and tested with a case study. Its effectiveness is identified by comparing the model's outcomes with observed data and other recent studies.

The results indicate that the developed truck appointment system can provide more than threefold and twofold reductions of the total cost experiencing at the terminal entrance compared to the actual data and results from previous research, respectively.

The proposed approach provides applicably coordinated truck plans and operating service gates efficiently to decrease congestion, emission and expenses.

The purpose of this paper is to examine the long-term cointegrating relationship between ocean, rail, truck and air cargo freight rates, as well as the short-term dynamics between these four series. The authors also test the predictive ability of these freight rates on major economic indicators.

The authors employ a vector error-correction model using 16 years of monthly time series data on freight rate data in the ocean, truck, rail and air cargo sectors to examine the interrelationship between these series as well as their interrelationship with major economic indicators.

The authors find that truck freight rates and as well as dry bulk freight rates have the strongest predictive power over other transportation freight rates as well as for the four major economic indicators used in this study. The authors find that dry bulk freight rates lead other freight rates in the short-run but lag other freight rates in the long run.

While ocean freight rate time series have been examined in a large number of studies, little research has been done on the interrelationship between ocean freight rates and the freight rates of other modes of transportation. Through the use of data on five different freight rate series, the authors are able to assess which rates lead and which rates lag each other and thus assist future researchers and practitioners forecast freight rates. The authors are also one of the few studies to assess the predictive power of non-ocean freight rates on major economic indicators.

Emissions produced by oceangoing vessels not only negatively affect the environment but also may deteriorate health of living organisms. Several regulations were released by the International Maritime Organization (IMO) to alleviate negative externalities from maritime transportation. Certain polluted areas were designated as “Emission Control Areas” (ECAs). However, IMO did not enforce any restrictions on the actual quantity of emissions that could be produced within ECAs. This paper aims to perform a comprehensive assessment of advantages and disadvantages from introducing restrictions on the emissions produced within ECAs. Two mixed-integer non-linear mathematical programs are presented to model the existing IMO regulations and an alternative policy, which along with the established IMO requirements also enforces restrictions on the quantity of emissions produced within ECAs. A set of linearization techniques are applied to linearize both models, which are further solved using the dynamic secant approximation procedure. Numerical experiments demonstrate that introduction of emission restrictions within ECAs can significantly reduce pollution levels but may incur increasing route service cost for the liner shipping company.

Two mixed-integer non-linear mathematical programs are presented to model the existing IMO regulations and an alternative policy, which along with the established IMO requirements also enforces restrictions on the quantity of emissions produced within ECAs. A set of linearization techniques are applied to linearize both models, which are further solved using the dynamic secant approximation procedure.

Numerical experiments were conducted for the French Asia Line 3 route, served by CMA CGM liner shipping company and passing through ECAs with sulfur oxide control. It was found that introduction of emission restrictions reduced the quantity of sulfur dioxide emissions produced by 40.4 per cent. In the meantime, emission restrictions required the liner shipping company to decrease the vessel sailing speed not only at voyage legs within ECAs but also at the adjacent voyage legs, which increased the total vessel turnaround time and in turn increased the total route service cost by 7.8 per cent.

This study does not capture uncertainty in liner shipping operations.

The developed mathematical model can serve as an efficient practical tool for liner shipping companies in developing green vessel schedules, enhancing energy efficiency and improving environmental sustainability.

Researchers and practitioners seek for new mathematical models and environmental policies that may alleviate pollution from oceangoing vessels and improve energy efficiency. This study proposes two novel mathematical models for the green vessel scheduling problem in a liner shipping route with ECAs. The first model is based on the existing IMO regulations, whereas the second one along with the established IMO requirements enforces emission restrictions within ECAs. Extensive numerical experiments are performed to assess advantages and disadvantages from introducing emission restrictions within ECAs.