The European Union (EU) transport policy recognizes the importance of the waterborne transport systems as key elements for sustainable growth in Europe. By 2030, 30% of total road freight over 300 km should shift to rail or waterborne transport, and more than 50% by 2050. Thus far, this ambition has failed but there have been several project initiatives within the EU to address these issues. In one of these projects, we consider a new waterborne transport system for Europe that is green, robust, flexible, more automated and autonomous, and able to connect both rural and urban terminals. The purpose of this paper is to describe work and preliminary results from this project. To that effect, and in order to assess any solutions contemplated, a comprehensive set of Key Performance Indicators (KPIs) has been defined, and three specific use cases within Europe are examined and evaluated according to these KPIs. KPIs represent the criteria under which the set of solutions developed are evaluated, and also compared to non-autonomous solutions. They are grouped under economic, environmental and social KPIs. KPIs have been selected after a consultation process involving project partners and external Advisory Group members. Links to EU transport and other regulatory action are also discussed.
The European Union (EU) transport policy recognizes the importance of the waterborne transport systems as key elements for sustainable growth in Europe. By 2030, 30% of total road freight over 300 km should shift to rail or waterborne transport, and more than 50% by 2050. Thus far, this ambition has failed but there have been several project initiatives within the EU to address these issues. In one of these projects, we consider a new waterborne transport system for Europe that is green, robust, flexible, more automated and autonomous, and able to connect both rural and urban terminals. The purpose of this paper is to describe work and preliminary results from this project. To that effect, and in order to assess any solutions contemplated, a comprehensive set of Key Performance Indicators (KPIs) has been defined, and three specific use cases within Europe are examined and evaluated according to these KPIs. KPIs represent the criteria under which the set of solutions developed are evaluated, and also compared to non-autonomous solutions. They are grouped under economic, environmental and social KPIs. KPIs have been selected after a consultation process involving project partners and external Advisory Group members. Links to EU transport and other regulatory action are also discussed.
We solve a central problem in the liner shipping industry called the liner shipping fleet repositioning problem (LSFRP). The LSFRP poses a large financial burden on liner shipping firms. During repositioning, vessels are moved between routes in a liner shipping network. Liner carriers wish to reposition vessels as cheaply as possible without disrupting cargo flows. The LSFRP is characterized by chains of interacting activities with a multicommodity flow over paths defined by the activities chosen. Despite its industrial importance, the LSFRP has received little attention in the literature. We introduce a novel mathematical model and a simulated annealing algorithm for the LSFRP with cargo flows that makes use of a carefully constructed graph; we evaluate these approaches using real-world data from our industrial collaborator. Additionally, we compare the performance of our approach against an actual repositioning scenario, one of many undertaken by our industrial collaborator in 2011. Our simulated annealing algorithm is able to increase the profit from $18.1 to $31.8 million using only a few minutes of CPU time. This shows that our algorithm could be used in a decision support system to solve the LSFRP.
Discusses the challenges of raising finance to build and convert low- and zero-emission ships as required by international law and policy to mitigate climate change.
To evaluate the transportation time reliability of the maritime transportation network for China’s crude oil imports under node capacity variations resulting from extreme events, a framework incorporating bi-level programming and a Monte Carlo simulation is proposed in this paper. Under this framework, the imported crude oil volume from each source country is considered to be a decision variable, and may change in correspondence to node capacity variations. The evaluation results illustrate that when strait or canal nodes were subject to capacity variations, the network transportation time reliability was relatively low. Conversely, the transportation time reliability was relatively high when port nodes were under capacity variations. In addition, the Taiwan Strait, the Strait of Hormuz, and the Strait of Malacca were identified as vulnerable nodes according to the transportation time reliability results. These results can assist government decision-makers and tanker company strategic planners to better plan crude oil import and transportation strategies.
The purpose of this paper is to investigate a multiple ship routing and speed optimization problem under time, cost and environmental objectives. A branch and price algorithm as well as a constraint programming model are developed that consider (a) fuel consumption as a function of payload, (b) fuel price as an explicit input, (c) freight rate as an input, and (d) in-transit cargo inventory costs. The alternative objective functions are minimum total trip duration, minimum total cost and minimum emissions. Computational experience with the algorithm is reported on a variety of scenarios.
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.
This paper presents a detailed BC, NOx and SO2 emission inventory for ships in the Arctic in 2012 based on satellite AIS data, ship engine power functions and technology stratified emission factors. Emission projections are presented for the years 2020, 2030 and 2050. Furthermore, the BC, SO2 and O3 concentrations and the deposition of BC are calculated for 2012 and for two arctic shipping scenarios – with or without arctic diversion routes due to a possible polar sea ice extent in the future.
In 2012, the largest shares of Arctic ships emissions are calculated for fishing ships (45% for BC, 38% for NOx, 23% for SO2) followed by passenger ships (20%, 17%, 25%), tankers (9%, 13%, 15%), general cargo (8%, 11%, 12%) and container ships (5%, 7%, 8%). In 2050, without arctic diversion routes, the total emissions of BC, NOx and SO2 are expected to change by +16%, −32% and −63%, respectively, compared to 2012. The results for fishing ships are the least certain, caused by a less precise engine power – sailing speed relation.
The calculated BC, SO2, and O3 surface concentrations and BC deposition contributions from ships are low as a mean for the whole Arctic in 2012, but locally BC additional contributions reach up to 20% around Iceland, and high additional contributions (100–300%) are calculated in some sea areas for SO2. In 2050, the arctic diversion routes highly influence the calculated surface concentrations and the deposition of BC in the Arctic. During summertime navigation contributions become very visible for BC (>80%) and SO2 (>1000%) along the arctic diversion routes, while the O3 (>10%) and BC deposition (>5%) additional contributions, respectively, get highest over the ocean east of Greenland and in the High Arctic.
The geospatial ship type specific emission results presented in this paper have increased the accuracy of the emission inventories for ships in the Arctic. The methodology can be used to estimate shipping emissions in other regions of the world, and hence may serve as an input for other researchers and policy makers working in this field.
In order to enhance sustainability in maritime shipping, shipping companies spend good efforts in improving the operational energy efficiency of existing ships. Accurate fuel consumption prediction model is a prerequisite of such operational improvements. Existing grey-box models (GBMs) are found with significant performance potential for ship fuel consumption prediction, although having a limitation of separating weather directions. Aiming to overcome this limitation, we propose a novel genetic algorithm-based GBM (GA-based GBM), where ship fuel consumption is modelled in a procedure based on basic principles of ship propulsion and the unknown parameters in this model are estimated with a GA-based procedure. Real ship operation data from a crude oil tanker over a 7-year sailing period are used to demonstrate the accuracy and reliability of the proposed model. To highlight the contribution of this work, we compare the proposed model against the latest GBM. The results show that the fitting performance of the proposed model is remarkably better, especially for oblique weather directions. The proposed model can be employed as a basis of ship energy efficiency management programs to reduce fuel consumption and greenhouse gas (GHG) emissions of a ship. This is beneficial to achieve the goal of sustainable shipping.