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.
This paper explores the potential of using green, autonomous ships in revitalizing inland shipping in Europe against the backdrop of declining market share and the dominance of "economy-of-scale" in waterborne freight transportation. It assesses the economic and environmental viability of converting freight from road to waterborne modalities in broader business ecosystems, specifically along the Rotterdam-Ghent corridor. The analysis leverages operational and commercial insights from logistics firms, ports and terminal operators, combined with data on European goods flows by road, and accounts for operational, financial and environmental variables including realistic scenario building and ecosystem implications. Findings indicate that inland shipping in general and green, autonomous shipping in particular offer both economically and environmentally viable alternatives to road transport. The study calls for further research into green, autonomous ships from an ecosystem perspective as a potential solution to current challenges in sustainable freight transportation.
Global climate change, which is largely attributed to human activity, is one of the foremost challenges of the 21st century. In recent times, there have been notable alterations in the Earth's climate, resulting in profound impacts on ecosystems and biodiversity. These alterations are caused by greenhouse gas, such as carbon dioxide, methane, and nitrous oxide. Greenhouse gas emissions are caused by practices such as deforestation, industrial operations, and the combustion of fossil fuels in vehicles, vessels, aircraft, and manufacturing facilities. The maritime and aviation industry is currently responsible for approximately 6% of global greenhouse gas emissions. Due to logistical and economic constraints, these industries are heavily reliant on liquid fuels, making direct electrification options unavailable for large parts of these sectors. As a result, these sectors are considered ‘hard to abate’. Understanding the future climate mitigation challenges associated with the maritime and aviation sectors is crucial in shaping effective policy measures, avoiding stranded assets, and preserving the chance to meet Paris Agreement-compatible emission reduction pathways.
This thesis identifies three main challenges and proposes modelling approaches to address them when modelling decarbonization pathways for the aviation and maritime sectors. From these challenges, research gaps have been identified that this PhD thesis aims to fill. Three models have been developed for the thesis: a maritime optimization model, a maritime demand model, and an aviation demand model. The modelling landscape and methodology vary across models, ranging from econometrics and data science to mathematical optimization.
To overcome the challenges and fill in the research gaps, three corresponding modelling approaches have been successfully applied:
1. Developing a holistic decarbonization modelling landscape. This includes life-cycle representations of technology costs and emissions, the upscaling of bottleneck technologies, the availability of sustainable biomass, and consideration of competing demand from other industries, as well as representations of policy levers such as carbon pricing or improvements to fuel efficiency.
2. Developing demand models that interpret the underlying scenario narrative consistently (SSP framework).
3. Improving the representation of technological learning for low-carbon technologies in energy system models.
The findings acquired by applying these three modelling approaches are valuable for energy modellers, climate scientists, and policymakers and offer unique insights into the inherent system dynamics associated with decarbonization of hard-to-abate sectors. Utilizing this modelling landscape reveals that current decarbonization efforts for hard-to-abate sectors are insufficient.
The maritime industry is a crucial hard-to-abate sector that is expected to depend on high-energy density renewable liquid fuels in the future. Traditionally, decarbonization pathways have been assessed assuming exogenous cost trajectories for renewable liquid fuels based on an exogenous learning curve. While past studies have looked at the impact of endogenizing learning curves for a specific technology utilizing linear approximation, a fully endogenous direct non-linear implementation of learning curves in a detailed sectoral model (maritime industry) that explores dynamics concerning sensitive parameters does not yet exist. Here, we apply an open-source optimization model for decarbonizing the maritime industry and further develop the model by encompassing a nonconvex mixed-integer quadratically constrained programming approach to analyze the impact of endogenized learning curves for renewable fuel costs following an experience curve approach. We find that global greenhouse gas emissions are significantly lower (up to 25% over a 30 year horizon) when utilizing endogenously modeled prices for renewable fuels compared to commonly used exogenous learning frameworks. Furthermore, we find that conventional modeling approaches overestimate the cost of climate mitigation, which can have significant policy implication related to carbon pricing and fuel efficiency requirements. In a broader context, this emphasizes the potential opportunities that can be achieved if policymakers and companies accelerate investments that drive down the costs of renewable technologies efficiently and thus trigger endogenous experience-based learning in real life.
Maritime transportation is an essential pillar of modern societies, serving as the backbone of global trade. The shipping industry relies heavily on fossil fuels, significantly impacting the environment and contributing to climate change. The International Maritime Organization (IMO) has introduced a strategy to reduce greenhouse gas emissions from international shipping and decarbonize the industry to combat this issue. This strategy aims to accomplish energy efficiency gains, transition to alternative fuels, and implement market-based measures.
Various energy efficiency indicators are in use to monitor the performance of ships, both from technical and operational perspectives. Building upon previous research that identified shortcomings in these indicators, this thesis investigates alternative methods of assessing the energy efficiency of ships. Emphasizing the importance of a benchmarking tool, the primary objective of this thesis is to contribute to the policy debate on reducing emissions in international shipping by developing a comprehensive carbon intensity indicator.
The thesis comprises four articles addressing various approaches to monitoring ship carbon emissions. The first article focuses on the influence of weather conditions on a ship’s energy efficiency, thereby contributing to the ongoing discussion on weather correction factors. Using model-based machine learning techniques, this article illustrates the diverse sea conditions encountered, their impact on energy efficiency, and the necessity of accounting for this diversity through multiple correction factors.
The second and third articles introduce and develop the concept of operational cycles for maritime transportation, drawing inspiration from the driving cycles employed in the automotive industry. The second article describes the process of generating operational cycles for the maritime sector as a novel concept. It validates this concept using real-world data obtained from a fleet of container ships. Building upon this foundation, the third article extends the concept by elaborating more comprehensive cycles that better represent real-world indicators.
The fourth article explores voluntary reporting frameworks in the shipping industry. It focuses on the Clean Cargo case and investigates the needs and interests of its members regarding this private initiative and related reporting framework. The discussion revolves around the role of these voluntary frameworks as complementary approaches to regulatory frameworks towards maritime decarbonization.
Based on the methodology developments and analysis through the thesis, the following key findings and recommendations are presented:
• The weather impact on ships’ fuel consumption prevents an accurate and real assessment of ships’ efficiency. Multiple weather correction factors for energy efficiency indicators introduce a novel approach.
• Inspired by the automotive industry, maritime operational cycles improve the assessment of technical and operational aspects of a ship’s energy efficiency. The cycles reduce the variability inherent to energy
efficiency indicators and are suitable as benchmarking tools.
• Although the IMO regulatory framework remains at the core of the maritime decarbonization strategy, regional regulatory frameworks and private initiatives have demonstrated their capacity to enhance industry
practices and facilitate regulatory developments.
This thesis contributes to enhancing carbon emissions monitoring in the maritime industry by introducing new methodologies and assessments. The resulting proposals are designed to enrich ongoing discussions within the IMO and complement the existing regulatory frameworks.
This report provides public recommendations for waterborne transport to West Coast of Norway, with the focus on the specified transport system in use case A in the AEGIS project. The complete background for this report is only achieved by reading all the other deliverables related to the use case, however these are confidential. Therefore, it is recommended to read the ICMASS 2022 paper "Development of an advanced, efficient and green intermodal system with autonomous inland and short sea shipping – AEGIS"1 to get a better overview of the project and specifically use case A. The transport system of use case A consists of mother vessel(s) together with one or more daughter vessels, connected by several ports. The mother vessel transports containers from Rotterdam via Hitra Kysthavn and into ports in the Trondheimsfjorden. The rationale for this use case is to enable a more flexible and cost-efficient waterborne transport solution for fjords and smaller ports.
Studies have indicated that transportation noise is associated with higher cardiovascular mortality, whereas evidence of noise as a risk factor for respiratory and cancer mortality is scarce and inconclusive. Also, knowledge on effects of low-level noise on mortality is very limited. We aimed to investigate associations between road and railway noise and natural-cause and cause-specific mortality in the Danish population. We estimated address-specific road and railway noise at the most (LdenMax) and least (LdenMin) exposed façades for all residential addresses in Denmark from 1990 to 2017 using high-quality exposure models. Using these data, we calculated 10-year time-weighted mean noise exposure for 2.6 million Danes aged >50 years, of whom 600,492 died from natural causes during a mean follow-up of 11.7 years. We analyzed data using Cox proportional hazards models with adjustment for individual and area-level sociodemographic variables and air pollution (PM2.5 and NO2). We found that a 10-year mean exposure to road LdenMax and road LdenMin per 10 dB were associated with hazard ratios (95% confidence intervals) of, respectively, 1.09 (1.09; 1.10) and 1.10 (1.10; 1.11) for natural-cause mortality, 1.09 (1.08; 1.10) and 1.09 (1.08; 1.10) for cardiovascular mortality, 1.13 (1.12; 1.14) and 1.17 (1.16; 1.19) for respiratory mortality and 1.03 (1.02; 1.03) and 1.06 (1.05; 1.07) for cancer mortality. For LdenMax, the associations followed linear exposure-response relationships from 35 dB to 60–<65 dB, after which the function levelled off. For LdenMin, exposure-response relationships were linear from 35 dB and up, with some levelling off at high noise levels for natural-cause and cardiovascular mortality. Railway noise did not seem associated with higher mortality in an exposure-response dependent manner. In conclusion, road traffic noise was associated with higher mortality and the increase in risk started well below the current World Health Organization guideline limit for road traffic noise of 53 dB.
The Belt and Road Initiative (BRI) entails investments to improve overland (rail) transport between Europe and China. This paper introduces a microscopic Multi-Commodity Flow Service Selection Problem for freight transport under the BRI and provides a decision tool for shippers to make door-to-door service plans. The minimizing objective function considers transportation costs, in-transit inventory costs, and carbon emissions. A series of sampled data of each provincial region of China are collected from Chinese multimodal transport operators. Results show that inland regions are strongly attracted to the rail mode for shipments to Europe. However, the “last mile” (including “first mile”) transport from the shipper to the long-haul transport terminal strongly influences this choice, and carbon emissions are strongly influenced by the available last mile transport links. Under the dual impact of in-transit inventory and carbon emission costs, regions that prefer rail to maritime are much further east than suggested by previous literature.
The European maritime transport policy recognizes the importance of the waterborne transport systems as key elements for sustainable growth in Europe. A major goal is to transfer more than 50% of road transport to rail or waterways within 2050. To meet this challenge waterway transport needs to get more attractive and overcome its disadvantages. Therefore, it is necessary to develop new knowledge and technology and find a completely new approach to short sea and inland waterways shipping. A key element in this is automation of ships, ports and administrative tasks aligned to requirements of different European regions. One main goal in the AEGIS project is to increase the efficiency of the waterways transport with the use of higher degrees of automation corresponding with new and smaller ship types to reduce costs and secure higher frequency by feeders and provide multimodal green logistics solutions combining short sea shipping with rail and road transport.
In this study, the periodic train timetabling problem is formulated using a time-space graph formulation that exploits the properties of a symmetric timetable. Three solution methods are proposed and compared where solutions are built by what we define as a dive-and-cut-and-price procedure. An LP relaxed version of the problem with a subset of constraints is solved using column generation where each column corresponds to the train paths of a line. Violated constraints are added by separation and a heuristic process is applied to help to find integer solutions. The passenger travel time is computed based on a solution timetable and Benders’ optimality cuts are generated allowing the method to integrate the routing of the passengers. We propose two large neighborhood search methods where the solution is iteratively destroyed and repaired into a new one and one random iterative method. The problem is tested on the morning rush hour period of the Regional and InterCity train network of Zealand, Denmark. The solution approaches show robust performance in a variety of scenarios, being able to find good quality solutions in terms of travel time and path length relatively fast. The inclusion of the proposed Benders’ cuts provide stronger relaxations to the problem. In addition, the graph formulation covers different real-life constraints and has the potential to easily be extended to accommodate more constraints.