Over the recent decades, there has been an increasing focus on energy-efficient operation of vessels. It has become part of the political agenda, where regulation is the main driver, but the maritime industry itself has also been driven towards more energy-efficient operation of the vessels, due to increasing fuel costs. Improving the energy efficiency on board vessels is not only a technical issue - factors such as awareness of the problem, knowledge skills and motivation are also important parameters that must be considered.
The paper shows how training in energy-efficient operation and awareness can affect the energy consumption of vessels. The study is based on navigational, full-mission simulator tests conducted at the International Maritime Academy SIMAC. A full-mission simulator is an image of the world allowing the students to obtain skills through learning-by-doing in a safe environment. Human factors and technical issues were included and the test sessions consisted of a combination of practical simulator exercises and reflection workshops.
The result of the simulator tests showed that a combination of installing technical equipment and raising awareness - making room for reflections-on and in-action - has a positive effect on energy consumption. The participants, on average, saved approximately 10% in fuel.
This chapter discusses European policies as regards short sea shipping (SSS) and intermodality. To that effect, a broad perspective is mainly taken, as recently there have been numerous policy initiatives in Europe that deal directly or indirectly with both sectors. The chapter takes stock at the situation as regards European ports and SSS and discusses challenges and prospects for the future. The analysis looks into both ports and SSS in a strict sense and other factors that are related, directly or indirectly, and that may have important ramifications. These other factors include EU port deregulation, the role of rail transport, environmental regulations, sulphur regulations, port security, and others. The chapter tries to explain the causes of current problems, investigates cross-linkages and makes suggestions for possible improvements.
The accelerated melting of the Arctic ice leads to the navigation of the Northern Sea Route (NSR) linking Asia and Europe, shortening transport channel between China and the European Union (EU). This has a significant impact on the China-EU bilateral trade which is analyzed in the present study. We present a framework based on a general equilibrium model for analyzing the impact of the NSR on the trade and the economies of China and the EU. Different fuel cost scenarios, consisting of fuel prices and sailing speeds on ice, are also considered. Specifically, we measure the changes in shipping costs between China and the EU, brought about by NSR navigation. These are used as a basis to quantify changes in transport technology. The Global Trade Analysis Project (GTAP) model is used to predict the trade and economic impacts. The results show that the NSR can save 0.98% in shipping costs and generate an increase in the exports of China and the EU in the order of 14,986 and 8,228 million US dollars, respectively. Among these exports, the mining industry shows the fastest growth, while the electronics industry experiences the largest increase in trade volume. Our findings reveal the potential of the NSR as an alternative route and its positive impact on bilateral trade between China and the EU. The results can provide a basis for shipping companies and governments to make decisions regarding the use of Arctic routes.
The first-mile problem, which refers to the design of transport services that connect passengers to their nearby transit station, has attracted growing attention in recent years. In this paper we consider first-mile ride-sharing services and study the problem of optimally determining the fleet size and assigning vehicles to transport requests. We formulate the problem as a mixed-integer program and present a number of numerical experiments based on a small-scale system to analyse different configurations of the service, namely with and without fleet control (FC). Result shows that a configuration with FC is superior in terms of profits while service rates can be higher in a configuration without FC, depending on the revenue-sharing mechanism.
This paper presents the flexible containership loading problem for seaport container terminals. The integrated management of loading operations, planning of the transport vehicles to use and their scheduling is what we define as the Flexible Ship Loading Problem (FSLP). The flexibility comes from a cooperative agreement between the terminal operator and the liner shipping company, specifying that the terminal has the right to decide which specific container to load for each slot obeying the class-based stowage plan received from the liner. We formulate a mathematical model for the problem. Then we present various modelling enhancements and a mathematical model to obtain strong lower bounds. We also propose a heuristic algorithm to solve the problem. It is shown that enhancements improve the performance of formulation significantly, and the heuristic efficiently generates high-quality solutions. Results also point out that substantial cost savings can be achieved by integrating the ship loading operations.
This study introduces a state-of-the-art volatility forecasting method for container shipping freight rates. Over the last decade, the container shipping industry has become very unpredictable. The demolition of the shipping conferences system in 2008 for all trades calling a port in the European Union (EU) and the global financial crisis in 2009 have affected the container shipping freight market adversely towards a depressive and non-stable market environment with heavily fluctuating freight rate movements. At the same time, the approaches of forecasting container freight rates using econometric and time series modelling have been rather limited. Therefore, in this paper, we discuss contemporary container freight rate dynamics in an attempt to forecast for the Far East to Northern Europe trade lane. Methodology-wise, we employ autoregressive integrated moving average (ARIMA) as well as the combination of ARIMA and autoregressive conditional heteroscedasticity (ARCH) model, which we call ARIMARCH. We observe that ARIMARCH model provides comparatively better results than the existing freight rate forecasting models while performing short-term forecasts on a weekly as well as monthly level. We also observe remarkable influence of recurrent general rate increases on the container freight rate volatility.
This paper investigates the simultaneous optimization problem of routing and sailing speed in the context of full-shipload tramp shipping. In this problem, a set of cargoes can be transported from their load to discharge ports by a fleet of heterogeneous ships of different speed ranges and load-dependent fuel consumption. The objective is to determine which orders to serve and to find the optimal route for each ship and the optimal sailing speed on each leg of the route so that the total profit is maximized. The problem originated from a real-life challenge faced by a Danish tramp shipping company in the tanker business. To solve the problem, a three-index mixed integer linear programming formulation as well as a set packing formulation is presented. A novel Branch-and-Price algorithm with efficient data preprocessing and heuristic column generation is proposed. The computational results on the test instances generated from real-life data show that the heuristic provides optimal solutions for small test instances and near-optimal solutions for larger test instances in a short running time. The effects of speed optimization and the sensitivity of the solutions to the fuel price change are analyzed. It is shown that speed optimization can improve the total profit by 16% on average and the fuel price has a significant effect on the average sailing speed and total profit.
Green Liner Shipping Network Design refers to the problems in green logistics related to the design of maritime services in liner shipping with a focus on reducing the environmental impact. This chapter discusses how to more efficiently plan the vessel services with the use of mathematical optimization models. A brief introduction to the main characteristics of Liner Shipping Network Design is given, as well as the different variants and assumptions that can be considered when defining this problem. The chapter also includes an overview of the algorithms and approaches that have been presented in the literature to design such networks.
GreenHopper is the first Danish zero-emission ferry developed as a test platform for autonomous waterborne navigation technologies. The paper presents technology development within the innovation project ShippingLab Autonomy, which led to the commissioning of GreenHopper at Limfjorden (DK) in December 2022. The technology research resulted in a holistic system architecture for surface vessel autonomy, based on distribution of functionality and responsibility on software modules, similar to the structure observed in the International Maritime Organization (IMO) Seafarers Training Certification and Watch-keeping (STCW) regulatory framework. The paper shows how this approach results in an architecture that supports safe behaviours of individual modules and of autonomous navigation at a system level. The paper presents the individual modules, specific features and benefits. Elements of the regulatory framework are highlighted to poise technology approval by maritime authorities. The paper reflects on lessons learned, discusses continued technology validation in dedicated operational scenarios.
Manufacturing companies who ship goods globally often rely on external Logistics Service Providers (LSPs) to manage the containerization and transportation of their freight. Those LSPs are usually required to follow rules when deciding how to mix the goods in the containers, which complicates the planning task. In this paper, we study such a freight containerization problem with a specific type of cargo mixing requirements recurrently faced by an international LSP. We show that this problem can be formulated as a Multi-Class Constrained Variable Size Bin Packing Problem: given a set of items that all have a size and a fixed number of classes for which they can take certain values, the objective is to pack the items in a minimum-cost set of bins while ensuring that the size capacity and maximum number of distinct values per class are not exceeded in any of the bins. We propose two adapted and one novel greedy heuristics, as well as an Adaptive Large Neighborhood Search (ALNS) metaheuristic, to find feasible solutions to the problem. We also provide a pattern-based formulation that is used to obtain lower bounds using a Column Generation approach. Using three extensive datasets, including a novel one with up to 1000 items and 5 classes reflecting real industrial cases, we show that the novel greedy heuristic outperforms the adaptations of the existing ones and that our ALNS yields significantly better solutions than a commercial solver within a mandatory 5-minute time limit. Practical insights are given about the solutions for the industrial benchmark.