B. Atasoy
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1
Corridor Fuel Demand and Hydrogen Bunkering for IWT
OpenTNSim energy simulation with lock modelling for the Rotterdam-Antwerp corridor
In 2050 the European Union must be climate neutral according to the Climate Law. This puts pressure on inland waterway transport (IWT) to adopt zero-emission alternative fuels, of which hydrogen is a promising option. However, its large-scale adoption faces major uncertainties and a bunkering infrastructure does not yet exist. The main challenge for hydrogen implementation in IWT lies not in propulsion technology, but in establishing infrastructure to deliver hydrogen where and when it is needed. Since this demand differs per corridor, a corridor-specific approach is required.
Current literature estimates IWT hydrogen demand in different ways: top-down scenarios, or simplified demand estimates that focus on practical constraints. OpenTNSim is an open-source Python package designed for simulation of inland waterways. It models vessels on a network of nodes and edges, and calculates resistance, power, and energy to estimate fuel consumption. While prior OpenTNSim studies address emissions, speed optimisation and bunkering location modelling, none combine detailed lock representation with fuel estimation on a physical corridor. This thesis addresses that gap by integrating OpenTNSim energy simulation with an updated lock modelling approach and translating simulated demand into indicative bunkering capacity.
The Rotterdam-Antwerp corridor contains multiple locks, such as Volkerak, Krammer, Hansweert and Kreekrak. At the locks, vessels experience delays due to levelling and queuing. Locks are identified as emission hotspots, which implies increased fuel consumption at these locations. However, the influence of locks on fuel consumption has not yet been quantified.
Model assumptions are based on data analysis regarding vessel, waterway, and lock characteristics using AIS, IVS and FIS data for the period 1-7 September 2025. The modelling approach combines two OpenTNSim components: the energy and lock module. The energy module determines resistance, power and fuel consumption based on vessel dimensions, sailing speed and fairway characteristics. Existing OpenTNSim lock models are based on simulated traffic interactions or AIS speed trajectories, which are not suitable for controlled variation of lock conditions. Therefore, an extension for the OpenTNSim lock model is developed.
The results show that increasing shallow water distance leads to higher energy consumption. The shallow-water sections increase the energy intensity by 1.7-8.3\%, depending on the route and loading condition. Loaded vessels show consistently higher consumption than unloaded vessels. The results also show that waiting times and berthing power influence energy consumption at the local level. However, when analysed at the route level, the additional energy consumption due to locks remains limited, contributing to 0.56-0.93\% of the total energy consumption per trip. This corresponds to 29.4-53.2 kWh under the tested assumptions.
The vessel-level results are scaled to corridor-level hydrogen demand by combining eight representative cases with observed route choice and loading ratios. This results in a total hydrogen demand of approximately 89,000 kg for a representative week of 251 vessel trips. The maximum hydrogen consumption is 437 kg for a loaded M8 vessel sailing on the Nieuwe Maas + Zuid-Beveland route. This remains below 500 kg, which is in line with the capacity of standardised hydrogen tanktainers. This suggests that vessels would require one to two containers per trip, which is consistent with current practice.
In conclusion, this thesis demonstrates that hydrogen demand for inland waterway transport can be simulated using a bottom-up corridor approach that integrates energy and lock modelling. The results indicate that hydrogen demand along the Rotterdam-Antwerp corridor is compatible with a container-based bunkering concept, providing a practical direction for the development of hydrogen infrastructure in inland navigation.
...
Current literature estimates IWT hydrogen demand in different ways: top-down scenarios, or simplified demand estimates that focus on practical constraints. OpenTNSim is an open-source Python package designed for simulation of inland waterways. It models vessels on a network of nodes and edges, and calculates resistance, power, and energy to estimate fuel consumption. While prior OpenTNSim studies address emissions, speed optimisation and bunkering location modelling, none combine detailed lock representation with fuel estimation on a physical corridor. This thesis addresses that gap by integrating OpenTNSim energy simulation with an updated lock modelling approach and translating simulated demand into indicative bunkering capacity.
The Rotterdam-Antwerp corridor contains multiple locks, such as Volkerak, Krammer, Hansweert and Kreekrak. At the locks, vessels experience delays due to levelling and queuing. Locks are identified as emission hotspots, which implies increased fuel consumption at these locations. However, the influence of locks on fuel consumption has not yet been quantified.
Model assumptions are based on data analysis regarding vessel, waterway, and lock characteristics using AIS, IVS and FIS data for the period 1-7 September 2025. The modelling approach combines two OpenTNSim components: the energy and lock module. The energy module determines resistance, power and fuel consumption based on vessel dimensions, sailing speed and fairway characteristics. Existing OpenTNSim lock models are based on simulated traffic interactions or AIS speed trajectories, which are not suitable for controlled variation of lock conditions. Therefore, an extension for the OpenTNSim lock model is developed.
The results show that increasing shallow water distance leads to higher energy consumption. The shallow-water sections increase the energy intensity by 1.7-8.3\%, depending on the route and loading condition. Loaded vessels show consistently higher consumption than unloaded vessels. The results also show that waiting times and berthing power influence energy consumption at the local level. However, when analysed at the route level, the additional energy consumption due to locks remains limited, contributing to 0.56-0.93\% of the total energy consumption per trip. This corresponds to 29.4-53.2 kWh under the tested assumptions.
The vessel-level results are scaled to corridor-level hydrogen demand by combining eight representative cases with observed route choice and loading ratios. This results in a total hydrogen demand of approximately 89,000 kg for a representative week of 251 vessel trips. The maximum hydrogen consumption is 437 kg for a loaded M8 vessel sailing on the Nieuwe Maas + Zuid-Beveland route. This remains below 500 kg, which is in line with the capacity of standardised hydrogen tanktainers. This suggests that vessels would require one to two containers per trip, which is consistent with current practice.
In conclusion, this thesis demonstrates that hydrogen demand for inland waterway transport can be simulated using a bottom-up corridor approach that integrates energy and lock modelling. The results indicate that hydrogen demand along the Rotterdam-Antwerp corridor is compatible with a container-based bunkering concept, providing a practical direction for the development of hydrogen infrastructure in inland navigation.
...
In 2050 the European Union must be climate neutral according to the Climate Law. This puts pressure on inland waterway transport (IWT) to adopt zero-emission alternative fuels, of which hydrogen is a promising option. However, its large-scale adoption faces major uncertainties and a bunkering infrastructure does not yet exist. The main challenge for hydrogen implementation in IWT lies not in propulsion technology, but in establishing infrastructure to deliver hydrogen where and when it is needed. Since this demand differs per corridor, a corridor-specific approach is required.
Current literature estimates IWT hydrogen demand in different ways: top-down scenarios, or simplified demand estimates that focus on practical constraints. OpenTNSim is an open-source Python package designed for simulation of inland waterways. It models vessels on a network of nodes and edges, and calculates resistance, power, and energy to estimate fuel consumption. While prior OpenTNSim studies address emissions, speed optimisation and bunkering location modelling, none combine detailed lock representation with fuel estimation on a physical corridor. This thesis addresses that gap by integrating OpenTNSim energy simulation with an updated lock modelling approach and translating simulated demand into indicative bunkering capacity.
The Rotterdam-Antwerp corridor contains multiple locks, such as Volkerak, Krammer, Hansweert and Kreekrak. At the locks, vessels experience delays due to levelling and queuing. Locks are identified as emission hotspots, which implies increased fuel consumption at these locations. However, the influence of locks on fuel consumption has not yet been quantified.
Model assumptions are based on data analysis regarding vessel, waterway, and lock characteristics using AIS, IVS and FIS data for the period 1-7 September 2025. The modelling approach combines two OpenTNSim components: the energy and lock module. The energy module determines resistance, power and fuel consumption based on vessel dimensions, sailing speed and fairway characteristics. Existing OpenTNSim lock models are based on simulated traffic interactions or AIS speed trajectories, which are not suitable for controlled variation of lock conditions. Therefore, an extension for the OpenTNSim lock model is developed.
The results show that increasing shallow water distance leads to higher energy consumption. The shallow-water sections increase the energy intensity by 1.7-8.3\%, depending on the route and loading condition. Loaded vessels show consistently higher consumption than unloaded vessels. The results also show that waiting times and berthing power influence energy consumption at the local level. However, when analysed at the route level, the additional energy consumption due to locks remains limited, contributing to 0.56-0.93\% of the total energy consumption per trip. This corresponds to 29.4-53.2 kWh under the tested assumptions.
The vessel-level results are scaled to corridor-level hydrogen demand by combining eight representative cases with observed route choice and loading ratios. This results in a total hydrogen demand of approximately 89,000 kg for a representative week of 251 vessel trips. The maximum hydrogen consumption is 437 kg for a loaded M8 vessel sailing on the Nieuwe Maas + Zuid-Beveland route. This remains below 500 kg, which is in line with the capacity of standardised hydrogen tanktainers. This suggests that vessels would require one to two containers per trip, which is consistent with current practice.
In conclusion, this thesis demonstrates that hydrogen demand for inland waterway transport can be simulated using a bottom-up corridor approach that integrates energy and lock modelling. The results indicate that hydrogen demand along the Rotterdam-Antwerp corridor is compatible with a container-based bunkering concept, providing a practical direction for the development of hydrogen infrastructure in inland navigation.
Current literature estimates IWT hydrogen demand in different ways: top-down scenarios, or simplified demand estimates that focus on practical constraints. OpenTNSim is an open-source Python package designed for simulation of inland waterways. It models vessels on a network of nodes and edges, and calculates resistance, power, and energy to estimate fuel consumption. While prior OpenTNSim studies address emissions, speed optimisation and bunkering location modelling, none combine detailed lock representation with fuel estimation on a physical corridor. This thesis addresses that gap by integrating OpenTNSim energy simulation with an updated lock modelling approach and translating simulated demand into indicative bunkering capacity.
The Rotterdam-Antwerp corridor contains multiple locks, such as Volkerak, Krammer, Hansweert and Kreekrak. At the locks, vessels experience delays due to levelling and queuing. Locks are identified as emission hotspots, which implies increased fuel consumption at these locations. However, the influence of locks on fuel consumption has not yet been quantified.
Model assumptions are based on data analysis regarding vessel, waterway, and lock characteristics using AIS, IVS and FIS data for the period 1-7 September 2025. The modelling approach combines two OpenTNSim components: the energy and lock module. The energy module determines resistance, power and fuel consumption based on vessel dimensions, sailing speed and fairway characteristics. Existing OpenTNSim lock models are based on simulated traffic interactions or AIS speed trajectories, which are not suitable for controlled variation of lock conditions. Therefore, an extension for the OpenTNSim lock model is developed.
The results show that increasing shallow water distance leads to higher energy consumption. The shallow-water sections increase the energy intensity by 1.7-8.3\%, depending on the route and loading condition. Loaded vessels show consistently higher consumption than unloaded vessels. The results also show that waiting times and berthing power influence energy consumption at the local level. However, when analysed at the route level, the additional energy consumption due to locks remains limited, contributing to 0.56-0.93\% of the total energy consumption per trip. This corresponds to 29.4-53.2 kWh under the tested assumptions.
The vessel-level results are scaled to corridor-level hydrogen demand by combining eight representative cases with observed route choice and loading ratios. This results in a total hydrogen demand of approximately 89,000 kg for a representative week of 251 vessel trips. The maximum hydrogen consumption is 437 kg for a loaded M8 vessel sailing on the Nieuwe Maas + Zuid-Beveland route. This remains below 500 kg, which is in line with the capacity of standardised hydrogen tanktainers. This suggests that vessels would require one to two containers per trip, which is consistent with current practice.
In conclusion, this thesis demonstrates that hydrogen demand for inland waterway transport can be simulated using a bottom-up corridor approach that integrates energy and lock modelling. The results indicate that hydrogen demand along the Rotterdam-Antwerp corridor is compatible with a container-based bunkering concept, providing a practical direction for the development of hydrogen infrastructure in inland navigation.
Optimising Vehicle Routing for Skip Containers
A Waste Collection Case Study with Container Reuse and Stacking using LNS
Master thesis
(2026)
-
C.M.W. Aalders, B. Atasoy, S. Fazi, J. Duran Micco, K.M. Hauge, K. Van Duurling
Growing volumes of construction and demolition waste create increasing pressure on waste logistics systems. Skip containers are one of the main container types used for this type of waste, yet existing routing research still focuses mainly on large rollon-rolloff (RoRo) containers. As a result, the operational characteristics of smaller chain-lift skip containers remain underexplored. In particular, the possibility of stacking empty containers on a vehicle and directly reusing an empty container between customers has received limited attention in the literature. This thesis addresses this gap by studying how vehicle routing for skip container waste collection can be optimised under container reuse and stacking feasibility constraints.
First, a mixed-integer linear programming (MILP) benchmark model is developed to represent standard skip container operations under simplified assumptions. Second, an extended Large Neighbourhood Search (LNS) metaheuristic algorithm is proposed to incorporate more detailed stacking rules, direct container reuse, and multiple storage and disposal location options. The benchmark and metaheuristic models are evaluated using operational and algorithmic key performance indicators.
The validation experiments showed that the LNS algorithm generated solutions within an average deviation below 3% from the benchmark while requiring substantially shorter runtimes. Furthermore, the results show that exact optimisation becomes less practical as the number of customers increases. Across the larger experiments, vehicle configurations with limited full container capacity consistently performed worst, indicating that a primary operational bottleneck lies in the number of available positions for transporting full containers rather than in stacking height alone. In addition, reuse reduced total operational time and improved productivity mainly through reduced handling time, although the number of vehicles required remained unchanged. These findings show that detailed stacking constraints and direct container reuse can be incorporated successfully into skip container routing models and that these extensions improve the practical relevance of optimisation for chain-lift skip operations.
...
First, a mixed-integer linear programming (MILP) benchmark model is developed to represent standard skip container operations under simplified assumptions. Second, an extended Large Neighbourhood Search (LNS) metaheuristic algorithm is proposed to incorporate more detailed stacking rules, direct container reuse, and multiple storage and disposal location options. The benchmark and metaheuristic models are evaluated using operational and algorithmic key performance indicators.
The validation experiments showed that the LNS algorithm generated solutions within an average deviation below 3% from the benchmark while requiring substantially shorter runtimes. Furthermore, the results show that exact optimisation becomes less practical as the number of customers increases. Across the larger experiments, vehicle configurations with limited full container capacity consistently performed worst, indicating that a primary operational bottleneck lies in the number of available positions for transporting full containers rather than in stacking height alone. In addition, reuse reduced total operational time and improved productivity mainly through reduced handling time, although the number of vehicles required remained unchanged. These findings show that detailed stacking constraints and direct container reuse can be incorporated successfully into skip container routing models and that these extensions improve the practical relevance of optimisation for chain-lift skip operations.
...
Growing volumes of construction and demolition waste create increasing pressure on waste logistics systems. Skip containers are one of the main container types used for this type of waste, yet existing routing research still focuses mainly on large rollon-rolloff (RoRo) containers. As a result, the operational characteristics of smaller chain-lift skip containers remain underexplored. In particular, the possibility of stacking empty containers on a vehicle and directly reusing an empty container between customers has received limited attention in the literature. This thesis addresses this gap by studying how vehicle routing for skip container waste collection can be optimised under container reuse and stacking feasibility constraints.
First, a mixed-integer linear programming (MILP) benchmark model is developed to represent standard skip container operations under simplified assumptions. Second, an extended Large Neighbourhood Search (LNS) metaheuristic algorithm is proposed to incorporate more detailed stacking rules, direct container reuse, and multiple storage and disposal location options. The benchmark and metaheuristic models are evaluated using operational and algorithmic key performance indicators.
The validation experiments showed that the LNS algorithm generated solutions within an average deviation below 3% from the benchmark while requiring substantially shorter runtimes. Furthermore, the results show that exact optimisation becomes less practical as the number of customers increases. Across the larger experiments, vehicle configurations with limited full container capacity consistently performed worst, indicating that a primary operational bottleneck lies in the number of available positions for transporting full containers rather than in stacking height alone. In addition, reuse reduced total operational time and improved productivity mainly through reduced handling time, although the number of vehicles required remained unchanged. These findings show that detailed stacking constraints and direct container reuse can be incorporated successfully into skip container routing models and that these extensions improve the practical relevance of optimisation for chain-lift skip operations.
First, a mixed-integer linear programming (MILP) benchmark model is developed to represent standard skip container operations under simplified assumptions. Second, an extended Large Neighbourhood Search (LNS) metaheuristic algorithm is proposed to incorporate more detailed stacking rules, direct container reuse, and multiple storage and disposal location options. The benchmark and metaheuristic models are evaluated using operational and algorithmic key performance indicators.
The validation experiments showed that the LNS algorithm generated solutions within an average deviation below 3% from the benchmark while requiring substantially shorter runtimes. Furthermore, the results show that exact optimisation becomes less practical as the number of customers increases. Across the larger experiments, vehicle configurations with limited full container capacity consistently performed worst, indicating that a primary operational bottleneck lies in the number of available positions for transporting full containers rather than in stacking height alone. In addition, reuse reduced total operational time and improved productivity mainly through reduced handling time, although the number of vehicles required remained unchanged. These findings show that detailed stacking constraints and direct container reuse can be incorporated successfully into skip container routing models and that these extensions improve the practical relevance of optimisation for chain-lift skip operations.
Online ULD Build-Up Scheduling
An integrated simulation-optimization approach for evaluating air cargo terminal robustness to external disturbances
Air cargo terminals must be able to maintain effective cargo preparation and allocation, even when faced with external disturbances such as delays in arrivals or departures. Unit Load Device (ULD) build-up is especially vulnerable to such disturbances because it depends on gradually arriving cargo, limited workspace capacity, and strict flight deadlines. This paper develops an online ULD build-up scheduling model with gradual look-ahead, and evaluates its contribution to air cargo terminal robustness using an embedded simulation-optimization framework. A Discrete-Event Simulation (DES) model represents terminal operations and disturbance propagation, while a time-indexed mixed-integer programming model generates rolling-horizon build-up schedules using updated terminal state information and, when available, estimated knowledge on future disturbances. The online scheduling model is benchmarked against three other configurations under different disturbance scenarios. Results show that online scheduling improves the invariance of a terminal to external disturbances, with the strongest evidence coming from full system response tests. Schedule adherence within the terminal only decreased with 0.05 to 0.35 percentage points when using the online scheduler, compared to 5.28 to 8.44 points for the current representation. The main robustness driver is the periodic re-optimization using terminal state information, rather than the gradual look-ahead mechanism on its own. The results produced can be greatly improved by using a large optimization window for the online scheduling model.
...
Air cargo terminals must be able to maintain effective cargo preparation and allocation, even when faced with external disturbances such as delays in arrivals or departures. Unit Load Device (ULD) build-up is especially vulnerable to such disturbances because it depends on gradually arriving cargo, limited workspace capacity, and strict flight deadlines. This paper develops an online ULD build-up scheduling model with gradual look-ahead, and evaluates its contribution to air cargo terminal robustness using an embedded simulation-optimization framework. A Discrete-Event Simulation (DES) model represents terminal operations and disturbance propagation, while a time-indexed mixed-integer programming model generates rolling-horizon build-up schedules using updated terminal state information and, when available, estimated knowledge on future disturbances. The online scheduling model is benchmarked against three other configurations under different disturbance scenarios. Results show that online scheduling improves the invariance of a terminal to external disturbances, with the strongest evidence coming from full system response tests. Schedule adherence within the terminal only decreased with 0.05 to 0.35 percentage points when using the online scheduler, compared to 5.28 to 8.44 points for the current representation. The main robustness driver is the periodic re-optimization using terminal state information, rather than the gradual look-ahead mechanism on its own. The results produced can be greatly improved by using a large optimization window for the online scheduling model.
Despite its critical role in maintaining container availability, inland empty container repositioning (ECR) is often modelled using stylised assumptions that abstract from the economic and contractual mechanisms driving decisions in practice. This paper empirically evaluates a realistic, multi-period inland ECR decision-support model that explicitly incorporates heterogeneous operators, minimum TEU-equivalent pricing, third-party handling fees, and port evacuation constraints.
Using real operational data, the model is assessed through a historical comparison with manual planning, operational testing in a weekly planning environment, and a port congestion scenario. The results show that cost reductions are achieved not by suppressing repositioning activity, but by improving coordination, timing, and consolidation across depots and operators.
Operational testing confirms that the model supports planner judgement by making cost–service trade-offs transparent and actionable. The congestion scenario further demonstrates how port constraints shift cost pressure from transport to inland storage, revealing vulnerabilities that are obscured by aggregate performance indicators.
Overall, the findings highlight the importance of economic realism for meaningful evaluation of inland ECR efficiency and resilience. ...
Using real operational data, the model is assessed through a historical comparison with manual planning, operational testing in a weekly planning environment, and a port congestion scenario. The results show that cost reductions are achieved not by suppressing repositioning activity, but by improving coordination, timing, and consolidation across depots and operators.
Operational testing confirms that the model supports planner judgement by making cost–service trade-offs transparent and actionable. The congestion scenario further demonstrates how port constraints shift cost pressure from transport to inland storage, revealing vulnerabilities that are obscured by aggregate performance indicators.
Overall, the findings highlight the importance of economic realism for meaningful evaluation of inland ECR efficiency and resilience. ...
Despite its critical role in maintaining container availability, inland empty container repositioning (ECR) is often modelled using stylised assumptions that abstract from the economic and contractual mechanisms driving decisions in practice. This paper empirically evaluates a realistic, multi-period inland ECR decision-support model that explicitly incorporates heterogeneous operators, minimum TEU-equivalent pricing, third-party handling fees, and port evacuation constraints.
Using real operational data, the model is assessed through a historical comparison with manual planning, operational testing in a weekly planning environment, and a port congestion scenario. The results show that cost reductions are achieved not by suppressing repositioning activity, but by improving coordination, timing, and consolidation across depots and operators.
Operational testing confirms that the model supports planner judgement by making cost–service trade-offs transparent and actionable. The congestion scenario further demonstrates how port constraints shift cost pressure from transport to inland storage, revealing vulnerabilities that are obscured by aggregate performance indicators.
Overall, the findings highlight the importance of economic realism for meaningful evaluation of inland ECR efficiency and resilience.
Using real operational data, the model is assessed through a historical comparison with manual planning, operational testing in a weekly planning environment, and a port congestion scenario. The results show that cost reductions are achieved not by suppressing repositioning activity, but by improving coordination, timing, and consolidation across depots and operators.
Operational testing confirms that the model supports planner judgement by making cost–service trade-offs transparent and actionable. The congestion scenario further demonstrates how port constraints shift cost pressure from transport to inland storage, revealing vulnerabilities that are obscured by aggregate performance indicators.
Overall, the findings highlight the importance of economic realism for meaningful evaluation of inland ECR efficiency and resilience.
Optimisation of Residential Waste Collection
Balancing Travel Time and Visual Attractiveness in Side Loader Routes
Residential waste collection with side loaders requires double traversal because vehicles service only one kerbside per pass. Consequently, routes optimised for efficiency often result in fragmented service of areas, long time intervals between visiting opposite kerbsides, and complex route shapes that are unintuitive to execute. Therefore, study developed a single-route optimisation framework that balances travel time efficiency with visual attractiveness under side loader operating constraints, including traffic directions, double traversal, and U-turn restrictions. The problem was formulated as an asymmetric travelling salesman problem (ATSP) on a directed network, where nodes represent directed kerbside street segments and travel times are computed as the fastest paths on the underlying road network. Visual attractiveness was quantified using complementary metrics for regional compactness, local compactness, and route complexity. These metrics were defined on an abstract network using straight-line connections to maintain computational tractability. They were combined with travel time in a weighted sum objective and optimised using simulated annealing. This enables a configurable trade-off between visual attractiveness and efficiency. The framework was evaluated on 12 municipal case studies provided by AMCS. Compared to the travel-time-optimal routes generated by AMCS, the framework consistently reduced neighbourhood fragmentation, revisit spans between opposite kerbsides, and intra-route crossings, while moderately increasing total travel time and directed Euclidean distance. A sensitivity analysis showed that uniformly scaling the visual attractiveness weights controls the trade-off between visual attractiveness and efficiency. However, very small scaling factors produced less consistent improvements in visual attractiveness, whereas larger factors yielded diminishing improvements while travel time increased substantially. Overall, the results suggest that visual attractiveness can be formalised on an abstract network and integrated into a configurable optimisation framework to produce visually attractive side loader routes on the road network.
...
Residential waste collection with side loaders requires double traversal because vehicles service only one kerbside per pass. Consequently, routes optimised for efficiency often result in fragmented service of areas, long time intervals between visiting opposite kerbsides, and complex route shapes that are unintuitive to execute. Therefore, study developed a single-route optimisation framework that balances travel time efficiency with visual attractiveness under side loader operating constraints, including traffic directions, double traversal, and U-turn restrictions. The problem was formulated as an asymmetric travelling salesman problem (ATSP) on a directed network, where nodes represent directed kerbside street segments and travel times are computed as the fastest paths on the underlying road network. Visual attractiveness was quantified using complementary metrics for regional compactness, local compactness, and route complexity. These metrics were defined on an abstract network using straight-line connections to maintain computational tractability. They were combined with travel time in a weighted sum objective and optimised using simulated annealing. This enables a configurable trade-off between visual attractiveness and efficiency. The framework was evaluated on 12 municipal case studies provided by AMCS. Compared to the travel-time-optimal routes generated by AMCS, the framework consistently reduced neighbourhood fragmentation, revisit spans between opposite kerbsides, and intra-route crossings, while moderately increasing total travel time and directed Euclidean distance. A sensitivity analysis showed that uniformly scaling the visual attractiveness weights controls the trade-off between visual attractiveness and efficiency. However, very small scaling factors produced less consistent improvements in visual attractiveness, whereas larger factors yielded diminishing improvements while travel time increased substantially. Overall, the results suggest that visual attractiveness can be formalised on an abstract network and integrated into a configurable optimisation framework to produce visually attractive side loader routes on the road network.
This thesis explores the analysis, periodicity, and scalable modelling of Max-Min-Plus-Scaling systems, a versatile approach to modelling Discrete Event systems. Unlike traditional continuous-time or discrete-time systems that evolve through differential or difference equations, DE systems progress through discrete events. MMPS systems rely only on maximisation, minimisation, addition, and scaling, making them highly suitable for modelling processes with synchronisation and/or competition such as energy delivery, transportation, and manufacturing.
The work is divided into three main segments. First, a new Mixed-Integer Linear Program ming based method is developed for analysing growth rates and fixed points of general implicit MMPS systems. This extends an existing MILP formulation for homogeneous and non-expansive explicit MMPS systems, introducing adaptations for general implicit cases.
A dedicated preprocessing step and search strategy are introduced, resulting in an analysis method that significantly reduces computational requirements. Secondly, the dynamical and stability behaviour of periodic MMPS systems with periods greater than one is examined.
A new canonical form is proposed, enabling the use of existing analysis tools on periodic systems, along with a method for determining the stability of periodic orbits. Thirdly, a modelling framework for transportation systems is introduced, featuring a connectable, node based toolbox and an algorithm that transforms high-level system descriptions into sets of equations.
All developed methods, theories, and tools are demonstrated on a real-world 4-node transportation system. The results confirm the efficiency of the new MILP approach, reveal periodic behaviour and stable periodic orbits, and highlight fixed points, all within the proposed transportation network framework. ...
The work is divided into three main segments. First, a new Mixed-Integer Linear Program ming based method is developed for analysing growth rates and fixed points of general implicit MMPS systems. This extends an existing MILP formulation for homogeneous and non-expansive explicit MMPS systems, introducing adaptations for general implicit cases.
A dedicated preprocessing step and search strategy are introduced, resulting in an analysis method that significantly reduces computational requirements. Secondly, the dynamical and stability behaviour of periodic MMPS systems with periods greater than one is examined.
A new canonical form is proposed, enabling the use of existing analysis tools on periodic systems, along with a method for determining the stability of periodic orbits. Thirdly, a modelling framework for transportation systems is introduced, featuring a connectable, node based toolbox and an algorithm that transforms high-level system descriptions into sets of equations.
All developed methods, theories, and tools are demonstrated on a real-world 4-node transportation system. The results confirm the efficiency of the new MILP approach, reveal periodic behaviour and stable periodic orbits, and highlight fixed points, all within the proposed transportation network framework. ...
This thesis explores the analysis, periodicity, and scalable modelling of Max-Min-Plus-Scaling systems, a versatile approach to modelling Discrete Event systems. Unlike traditional continuous-time or discrete-time systems that evolve through differential or difference equations, DE systems progress through discrete events. MMPS systems rely only on maximisation, minimisation, addition, and scaling, making them highly suitable for modelling processes with synchronisation and/or competition such as energy delivery, transportation, and manufacturing.
The work is divided into three main segments. First, a new Mixed-Integer Linear Program ming based method is developed for analysing growth rates and fixed points of general implicit MMPS systems. This extends an existing MILP formulation for homogeneous and non-expansive explicit MMPS systems, introducing adaptations for general implicit cases.
A dedicated preprocessing step and search strategy are introduced, resulting in an analysis method that significantly reduces computational requirements. Secondly, the dynamical and stability behaviour of periodic MMPS systems with periods greater than one is examined.
A new canonical form is proposed, enabling the use of existing analysis tools on periodic systems, along with a method for determining the stability of periodic orbits. Thirdly, a modelling framework for transportation systems is introduced, featuring a connectable, node based toolbox and an algorithm that transforms high-level system descriptions into sets of equations.
All developed methods, theories, and tools are demonstrated on a real-world 4-node transportation system. The results confirm the efficiency of the new MILP approach, reveal periodic behaviour and stable periodic orbits, and highlight fixed points, all within the proposed transportation network framework.
The work is divided into three main segments. First, a new Mixed-Integer Linear Program ming based method is developed for analysing growth rates and fixed points of general implicit MMPS systems. This extends an existing MILP formulation for homogeneous and non-expansive explicit MMPS systems, introducing adaptations for general implicit cases.
A dedicated preprocessing step and search strategy are introduced, resulting in an analysis method that significantly reduces computational requirements. Secondly, the dynamical and stability behaviour of periodic MMPS systems with periods greater than one is examined.
A new canonical form is proposed, enabling the use of existing analysis tools on periodic systems, along with a method for determining the stability of periodic orbits. Thirdly, a modelling framework for transportation systems is introduced, featuring a connectable, node based toolbox and an algorithm that transforms high-level system descriptions into sets of equations.
All developed methods, theories, and tools are demonstrated on a real-world 4-node transportation system. The results confirm the efficiency of the new MILP approach, reveal periodic behaviour and stable periodic orbits, and highlight fixed points, all within the proposed transportation network framework.
Towards Dynamic Inverse Optimization
A Data Aggregation Approach
Data-driven Inverse Optimization (IO) is a form of Supervised Learning where it is assumed that the output data is found by means of an optimization problem that depends on the input data. IO uses this data to approximate the optimization problem as best as possible. In the case where one wants to emulate an expert operating in a dynamic environment, the dataset obtained by measuring the expert is often contained in a small, optimal part of the total state space of the environment. When a model trained on this data finds itself in a different part of the state space, it can behave erratically.
In this thesis, we will combine Inverse Optimization with the active learning method of Dataset Aggregation (DAgger) to test if this improves model performance in dynamic settings. DAgger is an iterative process where the system is steered by the learner, creating new input data for the expert to find the best actions. This new data is then used to train a new model.
Furthermore, we propose a new algorithm, fast-DAgger, that should converge faster than the DAgger algorithm, at the possible cost of performance in the final model.
IO models trained with the DAgger and fast-DAgger algorithms are tested and compared to IO models trained on static datasets. This is done for two case studies: the Dynamic Vehicle Routing Problem as proposed by the EURO meets Neurips 2022 Vehicle Routing Competition, and the game of Tetris.
Results show the potential of combining IO with DAgger. However, DAgger is not always better than training with a static dataset. DAgger can only be helpful when the static training data is limited to a part of the total state space and when this data does not generalize well to the total state space. The fast-DAgger algorithm did not show a significant speed-up compared to the normal DAgger algorithm in the case studies. However, this is very dependent on the specifics of the model and the hyperparameters of the DAgger algorithm.
...
In this thesis, we will combine Inverse Optimization with the active learning method of Dataset Aggregation (DAgger) to test if this improves model performance in dynamic settings. DAgger is an iterative process where the system is steered by the learner, creating new input data for the expert to find the best actions. This new data is then used to train a new model.
Furthermore, we propose a new algorithm, fast-DAgger, that should converge faster than the DAgger algorithm, at the possible cost of performance in the final model.
IO models trained with the DAgger and fast-DAgger algorithms are tested and compared to IO models trained on static datasets. This is done for two case studies: the Dynamic Vehicle Routing Problem as proposed by the EURO meets Neurips 2022 Vehicle Routing Competition, and the game of Tetris.
Results show the potential of combining IO with DAgger. However, DAgger is not always better than training with a static dataset. DAgger can only be helpful when the static training data is limited to a part of the total state space and when this data does not generalize well to the total state space. The fast-DAgger algorithm did not show a significant speed-up compared to the normal DAgger algorithm in the case studies. However, this is very dependent on the specifics of the model and the hyperparameters of the DAgger algorithm.
...
Data-driven Inverse Optimization (IO) is a form of Supervised Learning where it is assumed that the output data is found by means of an optimization problem that depends on the input data. IO uses this data to approximate the optimization problem as best as possible. In the case where one wants to emulate an expert operating in a dynamic environment, the dataset obtained by measuring the expert is often contained in a small, optimal part of the total state space of the environment. When a model trained on this data finds itself in a different part of the state space, it can behave erratically.
In this thesis, we will combine Inverse Optimization with the active learning method of Dataset Aggregation (DAgger) to test if this improves model performance in dynamic settings. DAgger is an iterative process where the system is steered by the learner, creating new input data for the expert to find the best actions. This new data is then used to train a new model.
Furthermore, we propose a new algorithm, fast-DAgger, that should converge faster than the DAgger algorithm, at the possible cost of performance in the final model.
IO models trained with the DAgger and fast-DAgger algorithms are tested and compared to IO models trained on static datasets. This is done for two case studies: the Dynamic Vehicle Routing Problem as proposed by the EURO meets Neurips 2022 Vehicle Routing Competition, and the game of Tetris.
Results show the potential of combining IO with DAgger. However, DAgger is not always better than training with a static dataset. DAgger can only be helpful when the static training data is limited to a part of the total state space and when this data does not generalize well to the total state space. The fast-DAgger algorithm did not show a significant speed-up compared to the normal DAgger algorithm in the case studies. However, this is very dependent on the specifics of the model and the hyperparameters of the DAgger algorithm.
In this thesis, we will combine Inverse Optimization with the active learning method of Dataset Aggregation (DAgger) to test if this improves model performance in dynamic settings. DAgger is an iterative process where the system is steered by the learner, creating new input data for the expert to find the best actions. This new data is then used to train a new model.
Furthermore, we propose a new algorithm, fast-DAgger, that should converge faster than the DAgger algorithm, at the possible cost of performance in the final model.
IO models trained with the DAgger and fast-DAgger algorithms are tested and compared to IO models trained on static datasets. This is done for two case studies: the Dynamic Vehicle Routing Problem as proposed by the EURO meets Neurips 2022 Vehicle Routing Competition, and the game of Tetris.
Results show the potential of combining IO with DAgger. However, DAgger is not always better than training with a static dataset. DAgger can only be helpful when the static training data is limited to a part of the total state space and when this data does not generalize well to the total state space. The fast-DAgger algorithm did not show a significant speed-up compared to the normal DAgger algorithm in the case studies. However, this is very dependent on the specifics of the model and the hyperparameters of the DAgger algorithm.
Machine Learning for Multimodal Freight Chain Modelling
Case Study of NEAC’s Mode Chain Builder System
Freight transport plays a critical role in supporting global trade, with multimodal transport systems modelling gaining importance due to their potential to optimize efficiency and sustainability. Accurately modeling these multimodal freight chains is essential for infrastructure planning and policy-making. Yet, it remains a persistent challenge due to fragmented datasets, limited granularity, and the absence of observed multimodal chain-level data. Traditional modeling approaches, particularly heuristic-based methods, often struggle to incorporate real-world operational constraints such as port selection logic and cargo handling requirements. Moreover, these models are typically inflexible and computationally intensive.
This research seeks to develop an adaptive multimodal freight chain model that addresses these limitations. Specifically, it introduces a practical path construction framework that integrates port selection based on geographic and functional suitability, aligning cargo handling requirements with port capabilities during the construction of mode chains. This research also tries to address a gap in the literature by applying machine learning to the estimation of multimodal freight flows, a domain traditionally dominated by heuristic and optimization-based methods. To estimate freight demand distribution across the generated chains, this study explores the use of machine learning, particularly the Expectation-Maximization (EM) algorithm, to leverage the abundant but often unstructured transport data available. The EM model enables demand share prediction without relying on labeled training data, reducing the calibration burden and enhancing model responsiveness to observed transport flows.
The proposed modeling framework is applied to a case study based on the NEAC Mode Chain Builder system for inter-country freight movements between the Netherlands and Belgium, two countries with high multimodal connectivity and the largest ports in Europe. The results demonstrate the model’s ability to generate valid mode chain alternatives and to significantly reduce deviations between predicted and observed freight flows, particularly for sea and rail segments. While some deviation increases occur in other segments, these are outweighed by the overall improvement in prediction accuracy. The EM model also shows stable convergence behavior, confirming its potential under data-limited conditions. However, residual deviations suggest that external factors, such as data incompleteness or behavioral uncertainties, still limit full accuracy.
This study highlights the potential of combining graph search algorithms with unsupervised learning to enhance multimodal freight chain modeling, especially under data-constrained conditions. It contributes both a methodological and practical solution for building data-driven multimodal freight transport models that better reflect operational realities and observed empirical data that can be used to improve the freight transport planning and decision-making process.
...
This research seeks to develop an adaptive multimodal freight chain model that addresses these limitations. Specifically, it introduces a practical path construction framework that integrates port selection based on geographic and functional suitability, aligning cargo handling requirements with port capabilities during the construction of mode chains. This research also tries to address a gap in the literature by applying machine learning to the estimation of multimodal freight flows, a domain traditionally dominated by heuristic and optimization-based methods. To estimate freight demand distribution across the generated chains, this study explores the use of machine learning, particularly the Expectation-Maximization (EM) algorithm, to leverage the abundant but often unstructured transport data available. The EM model enables demand share prediction without relying on labeled training data, reducing the calibration burden and enhancing model responsiveness to observed transport flows.
The proposed modeling framework is applied to a case study based on the NEAC Mode Chain Builder system for inter-country freight movements between the Netherlands and Belgium, two countries with high multimodal connectivity and the largest ports in Europe. The results demonstrate the model’s ability to generate valid mode chain alternatives and to significantly reduce deviations between predicted and observed freight flows, particularly for sea and rail segments. While some deviation increases occur in other segments, these are outweighed by the overall improvement in prediction accuracy. The EM model also shows stable convergence behavior, confirming its potential under data-limited conditions. However, residual deviations suggest that external factors, such as data incompleteness or behavioral uncertainties, still limit full accuracy.
This study highlights the potential of combining graph search algorithms with unsupervised learning to enhance multimodal freight chain modeling, especially under data-constrained conditions. It contributes both a methodological and practical solution for building data-driven multimodal freight transport models that better reflect operational realities and observed empirical data that can be used to improve the freight transport planning and decision-making process.
...
Freight transport plays a critical role in supporting global trade, with multimodal transport systems modelling gaining importance due to their potential to optimize efficiency and sustainability. Accurately modeling these multimodal freight chains is essential for infrastructure planning and policy-making. Yet, it remains a persistent challenge due to fragmented datasets, limited granularity, and the absence of observed multimodal chain-level data. Traditional modeling approaches, particularly heuristic-based methods, often struggle to incorporate real-world operational constraints such as port selection logic and cargo handling requirements. Moreover, these models are typically inflexible and computationally intensive.
This research seeks to develop an adaptive multimodal freight chain model that addresses these limitations. Specifically, it introduces a practical path construction framework that integrates port selection based on geographic and functional suitability, aligning cargo handling requirements with port capabilities during the construction of mode chains. This research also tries to address a gap in the literature by applying machine learning to the estimation of multimodal freight flows, a domain traditionally dominated by heuristic and optimization-based methods. To estimate freight demand distribution across the generated chains, this study explores the use of machine learning, particularly the Expectation-Maximization (EM) algorithm, to leverage the abundant but often unstructured transport data available. The EM model enables demand share prediction without relying on labeled training data, reducing the calibration burden and enhancing model responsiveness to observed transport flows.
The proposed modeling framework is applied to a case study based on the NEAC Mode Chain Builder system for inter-country freight movements between the Netherlands and Belgium, two countries with high multimodal connectivity and the largest ports in Europe. The results demonstrate the model’s ability to generate valid mode chain alternatives and to significantly reduce deviations between predicted and observed freight flows, particularly for sea and rail segments. While some deviation increases occur in other segments, these are outweighed by the overall improvement in prediction accuracy. The EM model also shows stable convergence behavior, confirming its potential under data-limited conditions. However, residual deviations suggest that external factors, such as data incompleteness or behavioral uncertainties, still limit full accuracy.
This study highlights the potential of combining graph search algorithms with unsupervised learning to enhance multimodal freight chain modeling, especially under data-constrained conditions. It contributes both a methodological and practical solution for building data-driven multimodal freight transport models that better reflect operational realities and observed empirical data that can be used to improve the freight transport planning and decision-making process.
This research seeks to develop an adaptive multimodal freight chain model that addresses these limitations. Specifically, it introduces a practical path construction framework that integrates port selection based on geographic and functional suitability, aligning cargo handling requirements with port capabilities during the construction of mode chains. This research also tries to address a gap in the literature by applying machine learning to the estimation of multimodal freight flows, a domain traditionally dominated by heuristic and optimization-based methods. To estimate freight demand distribution across the generated chains, this study explores the use of machine learning, particularly the Expectation-Maximization (EM) algorithm, to leverage the abundant but often unstructured transport data available. The EM model enables demand share prediction without relying on labeled training data, reducing the calibration burden and enhancing model responsiveness to observed transport flows.
The proposed modeling framework is applied to a case study based on the NEAC Mode Chain Builder system for inter-country freight movements between the Netherlands and Belgium, two countries with high multimodal connectivity and the largest ports in Europe. The results demonstrate the model’s ability to generate valid mode chain alternatives and to significantly reduce deviations between predicted and observed freight flows, particularly for sea and rail segments. While some deviation increases occur in other segments, these are outweighed by the overall improvement in prediction accuracy. The EM model also shows stable convergence behavior, confirming its potential under data-limited conditions. However, residual deviations suggest that external factors, such as data incompleteness or behavioral uncertainties, still limit full accuracy.
This study highlights the potential of combining graph search algorithms with unsupervised learning to enhance multimodal freight chain modeling, especially under data-constrained conditions. It contributes both a methodological and practical solution for building data-driven multimodal freight transport models that better reflect operational realities and observed empirical data that can be used to improve the freight transport planning and decision-making process.
We present Sadcher, a real-time task assignment framework for heterogeneous multi-robot teams that incorporates dynamic coalition formation and task precedence constraints. Sadcher is trained through Imitation Learning and combines graph attention and transformers to predict assignment rewards between robots and tasks. Based on the predicted rewards, a relaxed bipartite matching step generates high-quality schedules with feasibility guarantees. We explicitly model robot and task positions, task durations, and robots’ remaining processing times, enabling advanced temporal and spatial reasoning and generalization to environments with different spatiotemporal distributions compared to training. Trained on optimally solved small-scale instances, our method can scale to larger task sets and team sizes. Sadcher outperforms other learning-based and heuristic baselines on randomized, unseen prob-
lems for small and medium-sized teams with computation times suitable for real-time operation. Wealso explore sampling-based variants and evaluate scalability across robot and task counts. To address performance limitations identified for large-scale problem instances, we experiment with using Reinforcement Learning (RL) to fine-tune the imitation-learned model. Both discrete and continuous RL formulations are explored, leveraging Proximal Policy Optimization (PPO). This allows for training on larger problem instances, for which optimal solutions for IL are infeasible to obtain. Our RL experiments provide insights into the comparative advantages and trade-offs of IL and RL methods. In addition, we release our dataset of 250,000 optimal schedules to facilitate future research. We include a detailed description of the instance generation and the Mixed-Integer Linear Programming (MILP) formulation used to solve them optimally. Furthermore, this thesis contributes a lightweight
simulation environment with visualization tools for benchmarking different task assignment algorithms. ...
lems for small and medium-sized teams with computation times suitable for real-time operation. Wealso explore sampling-based variants and evaluate scalability across robot and task counts. To address performance limitations identified for large-scale problem instances, we experiment with using Reinforcement Learning (RL) to fine-tune the imitation-learned model. Both discrete and continuous RL formulations are explored, leveraging Proximal Policy Optimization (PPO). This allows for training on larger problem instances, for which optimal solutions for IL are infeasible to obtain. Our RL experiments provide insights into the comparative advantages and trade-offs of IL and RL methods. In addition, we release our dataset of 250,000 optimal schedules to facilitate future research. We include a detailed description of the instance generation and the Mixed-Integer Linear Programming (MILP) formulation used to solve them optimally. Furthermore, this thesis contributes a lightweight
simulation environment with visualization tools for benchmarking different task assignment algorithms. ...
We present Sadcher, a real-time task assignment framework for heterogeneous multi-robot teams that incorporates dynamic coalition formation and task precedence constraints. Sadcher is trained through Imitation Learning and combines graph attention and transformers to predict assignment rewards between robots and tasks. Based on the predicted rewards, a relaxed bipartite matching step generates high-quality schedules with feasibility guarantees. We explicitly model robot and task positions, task durations, and robots’ remaining processing times, enabling advanced temporal and spatial reasoning and generalization to environments with different spatiotemporal distributions compared to training. Trained on optimally solved small-scale instances, our method can scale to larger task sets and team sizes. Sadcher outperforms other learning-based and heuristic baselines on randomized, unseen prob-
lems for small and medium-sized teams with computation times suitable for real-time operation. Wealso explore sampling-based variants and evaluate scalability across robot and task counts. To address performance limitations identified for large-scale problem instances, we experiment with using Reinforcement Learning (RL) to fine-tune the imitation-learned model. Both discrete and continuous RL formulations are explored, leveraging Proximal Policy Optimization (PPO). This allows for training on larger problem instances, for which optimal solutions for IL are infeasible to obtain. Our RL experiments provide insights into the comparative advantages and trade-offs of IL and RL methods. In addition, we release our dataset of 250,000 optimal schedules to facilitate future research. We include a detailed description of the instance generation and the Mixed-Integer Linear Programming (MILP) formulation used to solve them optimally. Furthermore, this thesis contributes a lightweight
simulation environment with visualization tools for benchmarking different task assignment algorithms.
lems for small and medium-sized teams with computation times suitable for real-time operation. Wealso explore sampling-based variants and evaluate scalability across robot and task counts. To address performance limitations identified for large-scale problem instances, we experiment with using Reinforcement Learning (RL) to fine-tune the imitation-learned model. Both discrete and continuous RL formulations are explored, leveraging Proximal Policy Optimization (PPO). This allows for training on larger problem instances, for which optimal solutions for IL are infeasible to obtain. Our RL experiments provide insights into the comparative advantages and trade-offs of IL and RL methods. In addition, we release our dataset of 250,000 optimal schedules to facilitate future research. We include a detailed description of the instance generation and the Mixed-Integer Linear Programming (MILP) formulation used to solve them optimally. Furthermore, this thesis contributes a lightweight
simulation environment with visualization tools for benchmarking different task assignment algorithms.
Value of Transport Flexibility under Supply Uncertainty in OCCUS Supply Chains
A Real Options approach
This research investigates the value of transport mode flexibility in OCCUS supply chains, particularly under uncertain CO2 supply during the early phases of CCUS development. This study aims to develop a strategic decision support model that quantifies the economic and environmental benefits of transport flexibility within the supply chain.
The literature review provides two key insights. First, it identifies the essential steps in the CO2 sup- ply chain: CO2 is captured onboard ships, temporarily stored onboard in solvent, and transported to onshore facilities for regeneration and liquefaction, after which the liquefied CO2 is transported to per- manent underground storage. Second, the review reveals that no comprehensive studies currently model the full OCCUS supply chain while incorporating uncertainty in CO2 supply. Consequently, no established approaches exist to address transport flexibility under such uncertainty within this context. However, real options analysis has been successfully applied in land-based CCUS projects to value in- vestment and operational flexibility under uncertainty. Building on this proven methodology, the present research adopts a real options approach to quantify the value of the option to switch between transport modes.
This research applies the developed real options model to a case study centered on the Port of Rotter- dam. The supply chain model follows the Value Maritime approach: CO2 is captured onboard ships, stored in CO2 -rich solvent and offloaded at the Maasvlakte terminal. Transport from the port to the re- generation and liquefaction facility is done with containerized trucks, with the option to switch to barges. Two barge types are considered: the smaller CEMT-IVa and the larger CEMT-Va or a combination of the two. The LCO2 is subsequently transported by truck to an underground storage site. The model evaluates scenarios under both a fixed average CO2 price (€136/t) and a variable CO2 price increasing over time based on market forecasts.
The results indicate that while the average CO2 price is insufficient to achieve economic viability at any time and outcome, incorporating the option to switch from truck to barge transport adds value if CO2 supply grows. The option to switch to the smaller CEMT-IVa (€630345) barge shows greater economic benefits compared to the larger CEMT-Va (€131555), mainly due to its better alignment with expected supply volumes during the early implementation phase. The combined switching option (€632010) only yields a marginal additional value, as the larger barge is only required at the highest and least probable supply scenario.
Under the variable CO2 price scenario, the truck-only strategy reaches a positive total value by 2031 with a 30% probability. Introducing the option to switch to the smaller CEMT-IVa barge accelerates this to 2029 with a 55% probability, reflecting earlier and more frequent switching. The larger CEMT-Va barge lags behind, with switching and positive value only occurring from 2030 onward and at a lower 11% probability, indicating less frequent and delayed use.
The break-even price for the truck-only transport strategy is €184.21/tCO2 . The inclusion of switch- ing options reduces this threshold across all configurations: the CEMT-IVa barge option achieves a 4.35% reduction to €176.19/tCO2 , while the CEMT-Va barge provides a modest 0.92% reduction to €182.52/tCO2 . The combined strategy yields the largest reduction of 4.36%, lowering the break-even price to €176.17/tCO2 . ...
The literature review provides two key insights. First, it identifies the essential steps in the CO2 sup- ply chain: CO2 is captured onboard ships, temporarily stored onboard in solvent, and transported to onshore facilities for regeneration and liquefaction, after which the liquefied CO2 is transported to per- manent underground storage. Second, the review reveals that no comprehensive studies currently model the full OCCUS supply chain while incorporating uncertainty in CO2 supply. Consequently, no established approaches exist to address transport flexibility under such uncertainty within this context. However, real options analysis has been successfully applied in land-based CCUS projects to value in- vestment and operational flexibility under uncertainty. Building on this proven methodology, the present research adopts a real options approach to quantify the value of the option to switch between transport modes.
This research applies the developed real options model to a case study centered on the Port of Rotter- dam. The supply chain model follows the Value Maritime approach: CO2 is captured onboard ships, stored in CO2 -rich solvent and offloaded at the Maasvlakte terminal. Transport from the port to the re- generation and liquefaction facility is done with containerized trucks, with the option to switch to barges. Two barge types are considered: the smaller CEMT-IVa and the larger CEMT-Va or a combination of the two. The LCO2 is subsequently transported by truck to an underground storage site. The model evaluates scenarios under both a fixed average CO2 price (€136/t) and a variable CO2 price increasing over time based on market forecasts.
The results indicate that while the average CO2 price is insufficient to achieve economic viability at any time and outcome, incorporating the option to switch from truck to barge transport adds value if CO2 supply grows. The option to switch to the smaller CEMT-IVa (€630345) barge shows greater economic benefits compared to the larger CEMT-Va (€131555), mainly due to its better alignment with expected supply volumes during the early implementation phase. The combined switching option (€632010) only yields a marginal additional value, as the larger barge is only required at the highest and least probable supply scenario.
Under the variable CO2 price scenario, the truck-only strategy reaches a positive total value by 2031 with a 30% probability. Introducing the option to switch to the smaller CEMT-IVa barge accelerates this to 2029 with a 55% probability, reflecting earlier and more frequent switching. The larger CEMT-Va barge lags behind, with switching and positive value only occurring from 2030 onward and at a lower 11% probability, indicating less frequent and delayed use.
The break-even price for the truck-only transport strategy is €184.21/tCO2 . The inclusion of switch- ing options reduces this threshold across all configurations: the CEMT-IVa barge option achieves a 4.35% reduction to €176.19/tCO2 , while the CEMT-Va barge provides a modest 0.92% reduction to €182.52/tCO2 . The combined strategy yields the largest reduction of 4.36%, lowering the break-even price to €176.17/tCO2 . ...
This research investigates the value of transport mode flexibility in OCCUS supply chains, particularly under uncertain CO2 supply during the early phases of CCUS development. This study aims to develop a strategic decision support model that quantifies the economic and environmental benefits of transport flexibility within the supply chain.
The literature review provides two key insights. First, it identifies the essential steps in the CO2 sup- ply chain: CO2 is captured onboard ships, temporarily stored onboard in solvent, and transported to onshore facilities for regeneration and liquefaction, after which the liquefied CO2 is transported to per- manent underground storage. Second, the review reveals that no comprehensive studies currently model the full OCCUS supply chain while incorporating uncertainty in CO2 supply. Consequently, no established approaches exist to address transport flexibility under such uncertainty within this context. However, real options analysis has been successfully applied in land-based CCUS projects to value in- vestment and operational flexibility under uncertainty. Building on this proven methodology, the present research adopts a real options approach to quantify the value of the option to switch between transport modes.
This research applies the developed real options model to a case study centered on the Port of Rotter- dam. The supply chain model follows the Value Maritime approach: CO2 is captured onboard ships, stored in CO2 -rich solvent and offloaded at the Maasvlakte terminal. Transport from the port to the re- generation and liquefaction facility is done with containerized trucks, with the option to switch to barges. Two barge types are considered: the smaller CEMT-IVa and the larger CEMT-Va or a combination of the two. The LCO2 is subsequently transported by truck to an underground storage site. The model evaluates scenarios under both a fixed average CO2 price (€136/t) and a variable CO2 price increasing over time based on market forecasts.
The results indicate that while the average CO2 price is insufficient to achieve economic viability at any time and outcome, incorporating the option to switch from truck to barge transport adds value if CO2 supply grows. The option to switch to the smaller CEMT-IVa (€630345) barge shows greater economic benefits compared to the larger CEMT-Va (€131555), mainly due to its better alignment with expected supply volumes during the early implementation phase. The combined switching option (€632010) only yields a marginal additional value, as the larger barge is only required at the highest and least probable supply scenario.
Under the variable CO2 price scenario, the truck-only strategy reaches a positive total value by 2031 with a 30% probability. Introducing the option to switch to the smaller CEMT-IVa barge accelerates this to 2029 with a 55% probability, reflecting earlier and more frequent switching. The larger CEMT-Va barge lags behind, with switching and positive value only occurring from 2030 onward and at a lower 11% probability, indicating less frequent and delayed use.
The break-even price for the truck-only transport strategy is €184.21/tCO2 . The inclusion of switch- ing options reduces this threshold across all configurations: the CEMT-IVa barge option achieves a 4.35% reduction to €176.19/tCO2 , while the CEMT-Va barge provides a modest 0.92% reduction to €182.52/tCO2 . The combined strategy yields the largest reduction of 4.36%, lowering the break-even price to €176.17/tCO2 .
The literature review provides two key insights. First, it identifies the essential steps in the CO2 sup- ply chain: CO2 is captured onboard ships, temporarily stored onboard in solvent, and transported to onshore facilities for regeneration and liquefaction, after which the liquefied CO2 is transported to per- manent underground storage. Second, the review reveals that no comprehensive studies currently model the full OCCUS supply chain while incorporating uncertainty in CO2 supply. Consequently, no established approaches exist to address transport flexibility under such uncertainty within this context. However, real options analysis has been successfully applied in land-based CCUS projects to value in- vestment and operational flexibility under uncertainty. Building on this proven methodology, the present research adopts a real options approach to quantify the value of the option to switch between transport modes.
This research applies the developed real options model to a case study centered on the Port of Rotter- dam. The supply chain model follows the Value Maritime approach: CO2 is captured onboard ships, stored in CO2 -rich solvent and offloaded at the Maasvlakte terminal. Transport from the port to the re- generation and liquefaction facility is done with containerized trucks, with the option to switch to barges. Two barge types are considered: the smaller CEMT-IVa and the larger CEMT-Va or a combination of the two. The LCO2 is subsequently transported by truck to an underground storage site. The model evaluates scenarios under both a fixed average CO2 price (€136/t) and a variable CO2 price increasing over time based on market forecasts.
The results indicate that while the average CO2 price is insufficient to achieve economic viability at any time and outcome, incorporating the option to switch from truck to barge transport adds value if CO2 supply grows. The option to switch to the smaller CEMT-IVa (€630345) barge shows greater economic benefits compared to the larger CEMT-Va (€131555), mainly due to its better alignment with expected supply volumes during the early implementation phase. The combined switching option (€632010) only yields a marginal additional value, as the larger barge is only required at the highest and least probable supply scenario.
Under the variable CO2 price scenario, the truck-only strategy reaches a positive total value by 2031 with a 30% probability. Introducing the option to switch to the smaller CEMT-IVa barge accelerates this to 2029 with a 55% probability, reflecting earlier and more frequent switching. The larger CEMT-Va barge lags behind, with switching and positive value only occurring from 2030 onward and at a lower 11% probability, indicating less frequent and delayed use.
The break-even price for the truck-only transport strategy is €184.21/tCO2 . The inclusion of switch- ing options reduces this threshold across all configurations: the CEMT-IVa barge option achieves a 4.35% reduction to €176.19/tCO2 , while the CEMT-Va barge provides a modest 0.92% reduction to €182.52/tCO2 . The combined strategy yields the largest reduction of 4.36%, lowering the break-even price to €176.17/tCO2 .
The introduction of the shipping container revolutionised global trade by significantly reducing handling costs, improving efficiency and enabling intermodal transportation. This development paved the way for the expansion of international trade and the development of highly interconnected global supply chains. Congestion, sustainability concerns, and vulnerability to disruption have become major obstacles. Recent examples of such obstacles are the COVID-19 pandemic and the blockage of the Suez Canal. Synchromodality has been identified as a potential solution for mitigating some of these concerns.
As a relatively new concept, synchromodality has mainly been studied at a theoretical level, focusing on its definition and potential. As the concept of synchromodality seems to gain attention from a broader public, more recent research has also focused on the more quantitative side. These quantitative studies primarily model the transport planning side of synchromodality. In these studies, some aspects in the supply chain have been overlooked so far, mainly the impact on critical infrastructure such as container terminals.
This research addresses this overlooked area in the existing academic landscape. Aspects such as the loading, unloading and stacking of containers are explicitly modelled in combination with synchromodal transport planning optimisation. This allows for an assessment of how synchromodality influences container terminal operations and how constraints and the dynamics at container terminals influence the transport planning. A multi-agent system approach is used as a framework for this model, as this presents a good option to model the different stakeholders involved in synchromodal transport.
The performance was analysed based on key performance metrics, including container relocation frequency, dwell times, and cost efficiency. The findings indicate that the integration of synchromodal transport planning with container terminal operations yields significant improvements. An iterative feedback loop between the transport planning agent and terminal agents facilitates more effective decision-making, leading to feasible transport planning, smoother operations, and improved resource utilisation.
Scenario analysis yielded further interesting results in terms of how a synchromodal planner would adapt to disruptions. The two most interesting findings are a decrease in the number of transshipments and a modal shift towards faster, more flexible, but also more expensive and more polluting transport modes.
In conclusion, this research demonstrates that the integration of synchromodal transport planning and container terminal operations improves the efficiency and adaptability of synchromodal logistics networks. Through these advances, this research contributes to ongoing efforts in the planning of synchromodal transport and the optimisation of container terminals, offering valuable information for both academia and industry.
...
As a relatively new concept, synchromodality has mainly been studied at a theoretical level, focusing on its definition and potential. As the concept of synchromodality seems to gain attention from a broader public, more recent research has also focused on the more quantitative side. These quantitative studies primarily model the transport planning side of synchromodality. In these studies, some aspects in the supply chain have been overlooked so far, mainly the impact on critical infrastructure such as container terminals.
This research addresses this overlooked area in the existing academic landscape. Aspects such as the loading, unloading and stacking of containers are explicitly modelled in combination with synchromodal transport planning optimisation. This allows for an assessment of how synchromodality influences container terminal operations and how constraints and the dynamics at container terminals influence the transport planning. A multi-agent system approach is used as a framework for this model, as this presents a good option to model the different stakeholders involved in synchromodal transport.
The performance was analysed based on key performance metrics, including container relocation frequency, dwell times, and cost efficiency. The findings indicate that the integration of synchromodal transport planning with container terminal operations yields significant improvements. An iterative feedback loop between the transport planning agent and terminal agents facilitates more effective decision-making, leading to feasible transport planning, smoother operations, and improved resource utilisation.
Scenario analysis yielded further interesting results in terms of how a synchromodal planner would adapt to disruptions. The two most interesting findings are a decrease in the number of transshipments and a modal shift towards faster, more flexible, but also more expensive and more polluting transport modes.
In conclusion, this research demonstrates that the integration of synchromodal transport planning and container terminal operations improves the efficiency and adaptability of synchromodal logistics networks. Through these advances, this research contributes to ongoing efforts in the planning of synchromodal transport and the optimisation of container terminals, offering valuable information for both academia and industry.
...
The introduction of the shipping container revolutionised global trade by significantly reducing handling costs, improving efficiency and enabling intermodal transportation. This development paved the way for the expansion of international trade and the development of highly interconnected global supply chains. Congestion, sustainability concerns, and vulnerability to disruption have become major obstacles. Recent examples of such obstacles are the COVID-19 pandemic and the blockage of the Suez Canal. Synchromodality has been identified as a potential solution for mitigating some of these concerns.
As a relatively new concept, synchromodality has mainly been studied at a theoretical level, focusing on its definition and potential. As the concept of synchromodality seems to gain attention from a broader public, more recent research has also focused on the more quantitative side. These quantitative studies primarily model the transport planning side of synchromodality. In these studies, some aspects in the supply chain have been overlooked so far, mainly the impact on critical infrastructure such as container terminals.
This research addresses this overlooked area in the existing academic landscape. Aspects such as the loading, unloading and stacking of containers are explicitly modelled in combination with synchromodal transport planning optimisation. This allows for an assessment of how synchromodality influences container terminal operations and how constraints and the dynamics at container terminals influence the transport planning. A multi-agent system approach is used as a framework for this model, as this presents a good option to model the different stakeholders involved in synchromodal transport.
The performance was analysed based on key performance metrics, including container relocation frequency, dwell times, and cost efficiency. The findings indicate that the integration of synchromodal transport planning with container terminal operations yields significant improvements. An iterative feedback loop between the transport planning agent and terminal agents facilitates more effective decision-making, leading to feasible transport planning, smoother operations, and improved resource utilisation.
Scenario analysis yielded further interesting results in terms of how a synchromodal planner would adapt to disruptions. The two most interesting findings are a decrease in the number of transshipments and a modal shift towards faster, more flexible, but also more expensive and more polluting transport modes.
In conclusion, this research demonstrates that the integration of synchromodal transport planning and container terminal operations improves the efficiency and adaptability of synchromodal logistics networks. Through these advances, this research contributes to ongoing efforts in the planning of synchromodal transport and the optimisation of container terminals, offering valuable information for both academia and industry.
As a relatively new concept, synchromodality has mainly been studied at a theoretical level, focusing on its definition and potential. As the concept of synchromodality seems to gain attention from a broader public, more recent research has also focused on the more quantitative side. These quantitative studies primarily model the transport planning side of synchromodality. In these studies, some aspects in the supply chain have been overlooked so far, mainly the impact on critical infrastructure such as container terminals.
This research addresses this overlooked area in the existing academic landscape. Aspects such as the loading, unloading and stacking of containers are explicitly modelled in combination with synchromodal transport planning optimisation. This allows for an assessment of how synchromodality influences container terminal operations and how constraints and the dynamics at container terminals influence the transport planning. A multi-agent system approach is used as a framework for this model, as this presents a good option to model the different stakeholders involved in synchromodal transport.
The performance was analysed based on key performance metrics, including container relocation frequency, dwell times, and cost efficiency. The findings indicate that the integration of synchromodal transport planning with container terminal operations yields significant improvements. An iterative feedback loop between the transport planning agent and terminal agents facilitates more effective decision-making, leading to feasible transport planning, smoother operations, and improved resource utilisation.
Scenario analysis yielded further interesting results in terms of how a synchromodal planner would adapt to disruptions. The two most interesting findings are a decrease in the number of transshipments and a modal shift towards faster, more flexible, but also more expensive and more polluting transport modes.
In conclusion, this research demonstrates that the integration of synchromodal transport planning and container terminal operations improves the efficiency and adaptability of synchromodal logistics networks. Through these advances, this research contributes to ongoing efforts in the planning of synchromodal transport and the optimisation of container terminals, offering valuable information for both academia and industry.
In this paper, we address the Blocking Flexible Job-Shop Scheduling Problem with Transportation and Time Windows (BFJSPT-TW), which combines assignment, blocking, and transport constraints under spatial feasibility.
Exact monolithic formulations rapidly become intractable as instance size increases. To overcome this, we propose a Logic-Based Benders Decomposition (LBBD) that integrates a disjunctive-graph relaxation into the master problem, providing informative lower bounds and accelerating convergence.
The subproblem is solved through Constraint Programming, ensuring temporal and blocking feasibility.
Computational experiments demonstrate that the proposed LBBD–DG achieves near-optimal solutions with up to 80 % stronger initial bounds and four times faster convergence than baseline models.
The method establishes a scalable near-exact framework for layout-aware scheduling that is capable of producing high quality schedules in minutes for very large problem instances. ...
Exact monolithic formulations rapidly become intractable as instance size increases. To overcome this, we propose a Logic-Based Benders Decomposition (LBBD) that integrates a disjunctive-graph relaxation into the master problem, providing informative lower bounds and accelerating convergence.
The subproblem is solved through Constraint Programming, ensuring temporal and blocking feasibility.
Computational experiments demonstrate that the proposed LBBD–DG achieves near-optimal solutions with up to 80 % stronger initial bounds and four times faster convergence than baseline models.
The method establishes a scalable near-exact framework for layout-aware scheduling that is capable of producing high quality schedules in minutes for very large problem instances. ...
In this paper, we address the Blocking Flexible Job-Shop Scheduling Problem with Transportation and Time Windows (BFJSPT-TW), which combines assignment, blocking, and transport constraints under spatial feasibility.
Exact monolithic formulations rapidly become intractable as instance size increases. To overcome this, we propose a Logic-Based Benders Decomposition (LBBD) that integrates a disjunctive-graph relaxation into the master problem, providing informative lower bounds and accelerating convergence.
The subproblem is solved through Constraint Programming, ensuring temporal and blocking feasibility.
Computational experiments demonstrate that the proposed LBBD–DG achieves near-optimal solutions with up to 80 % stronger initial bounds and four times faster convergence than baseline models.
The method establishes a scalable near-exact framework for layout-aware scheduling that is capable of producing high quality schedules in minutes for very large problem instances.
Exact monolithic formulations rapidly become intractable as instance size increases. To overcome this, we propose a Logic-Based Benders Decomposition (LBBD) that integrates a disjunctive-graph relaxation into the master problem, providing informative lower bounds and accelerating convergence.
The subproblem is solved through Constraint Programming, ensuring temporal and blocking feasibility.
Computational experiments demonstrate that the proposed LBBD–DG achieves near-optimal solutions with up to 80 % stronger initial bounds and four times faster convergence than baseline models.
The method establishes a scalable near-exact framework for layout-aware scheduling that is capable of producing high quality schedules in minutes for very large problem instances.
Brigade vehicle deployment problem
A quantitative brigade vehicle deployment optimisation on a multimodal transport network
Current geopolitical tensions mean that NATO countries must now make greater efforts to deter threats to their territories, having neglected this defence task for years. In case of a major conflict, brigades and their vehicles are ordered to immediately travel to the area of conflict. This vehicle deployment is researched and modelled in a Multi-Integer Linear Programme (MILP), with the objective to minimise the makespan of the deployment, e.g. the arrival time of the last convoy. In this deployment the strategic and operational movements are modelled, meaning the starting point is the Point of Embarkation (POE) and the end point is the Staging Area (SA) or Concentration Area (CA). The model is suited for deployments that use a double modal network, being road and rail transport. Two scenarios are established, distinguishable by their transport networks and vehicle types, and they are subjected to configurations in which parameters are varied. The research is conducted in cooperation with the Royal Netherlands Army (RNLA), and serves as a handle to obtain understanding on deployments when varying certain parameters. It is observed that the usage of the rail mode mainly should be motivated by vehicle suitability, rather than makespan oriented reasoning, as it hardly improves the makespan of the deployment. Furthermore, platooning is a promising technology to implement in the deployment, with the ability to decrease the makespan by at least 11%. The influence of traffic congestion halfway the deployment is highlighted, encompassing an increase in makespan of at least 7-15%. It is found that the convoy amount and the amount of vehicles does not influence the makespan by a lot (pm1.9%). Lastly, the difference between the transport networks of the two scenarios is exposed with the use of a robustness examination, where the makespan of the first scenario improves substantially more (11.7%) than the makespan of the second scenario (0.4%). This research can be expanded in several directions, including the incorporation of additional modalities and a more comprehensive investigation into the robustness of transport networks within these deployments.
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Current geopolitical tensions mean that NATO countries must now make greater efforts to deter threats to their territories, having neglected this defence task for years. In case of a major conflict, brigades and their vehicles are ordered to immediately travel to the area of conflict. This vehicle deployment is researched and modelled in a Multi-Integer Linear Programme (MILP), with the objective to minimise the makespan of the deployment, e.g. the arrival time of the last convoy. In this deployment the strategic and operational movements are modelled, meaning the starting point is the Point of Embarkation (POE) and the end point is the Staging Area (SA) or Concentration Area (CA). The model is suited for deployments that use a double modal network, being road and rail transport. Two scenarios are established, distinguishable by their transport networks and vehicle types, and they are subjected to configurations in which parameters are varied. The research is conducted in cooperation with the Royal Netherlands Army (RNLA), and serves as a handle to obtain understanding on deployments when varying certain parameters. It is observed that the usage of the rail mode mainly should be motivated by vehicle suitability, rather than makespan oriented reasoning, as it hardly improves the makespan of the deployment. Furthermore, platooning is a promising technology to implement in the deployment, with the ability to decrease the makespan by at least 11%. The influence of traffic congestion halfway the deployment is highlighted, encompassing an increase in makespan of at least 7-15%. It is found that the convoy amount and the amount of vehicles does not influence the makespan by a lot (pm1.9%). Lastly, the difference between the transport networks of the two scenarios is exposed with the use of a robustness examination, where the makespan of the first scenario improves substantially more (11.7%) than the makespan of the second scenario (0.4%). This research can be expanded in several directions, including the incorporation of additional modalities and a more comprehensive investigation into the robustness of transport networks within these deployments.
This research investigates the optimal design of a charging infrastructure network for heavy-duty battery electric vehicles (HD BEVs) in the Netherlands. It addresses the challenges posed by the limited range of HD BEVs and the need for a robust charging network to support logistics operations. The study identifies key factors influencing fleet owners' charging behaviour and develops a qualitative decision model to estimate the demand distribution between private and public charging. Additionally, an optimization model is proposed to determine the optimal locations for public charging facilities, balancing the objectives of minimising infrastructure costs and maximising the number of trucks that can recharge.
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This research investigates the optimal design of a charging infrastructure network for heavy-duty battery electric vehicles (HD BEVs) in the Netherlands. It addresses the challenges posed by the limited range of HD BEVs and the need for a robust charging network to support logistics operations. The study identifies key factors influencing fleet owners' charging behaviour and develops a qualitative decision model to estimate the demand distribution between private and public charging. Additionally, an optimization model is proposed to determine the optimal locations for public charging facilities, balancing the objectives of minimising infrastructure costs and maximising the number of trucks that can recharge.
Passenger-centric robust timetabling in railways
A case study for the Eindhoven-Den Bosch-Tilburg network
Punctuality in railway transport is a critical concern for passengers, especially during unavoidable disturbances. Therefore, a sufficiently robust timetable is necessary. This thesis proposes a new timetabling method that minimizes passenger delay as the optimization objective, aiming to create a passenger-centric robust timetable. The model is implemented in the Dutch railway network in the Eindhoven-Den Bosch-Tilburg area to verify and validate its correctness and functionality. Experimental results demonstrate that the proposed model significantly reduces passenger delays when dealing with specific disturbances, and the improvement in robustness becomes more pronounced as the severity of the disturbances increases.
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Punctuality in railway transport is a critical concern for passengers, especially during unavoidable disturbances. Therefore, a sufficiently robust timetable is necessary. This thesis proposes a new timetabling method that minimizes passenger delay as the optimization objective, aiming to create a passenger-centric robust timetable. The model is implemented in the Dutch railway network in the Eindhoven-Den Bosch-Tilburg area to verify and validate its correctness and functionality. Experimental results demonstrate that the proposed model significantly reduces passenger delays when dealing with specific disturbances, and the improvement in robustness becomes more pronounced as the severity of the disturbances increases.
The ongoing global push towards energy transformation and decarbonisation underscores the urgent need for reducing greenhouse gas emissions, particularly in the maritime sector, which accounts for a significant portion of global trade and emissions. This study focuses on inland waterway transportation (IWT) as a key area for implementing zero-emission solutions, especially through the use of battery swapping technology. Given the average age and expected lifespan of vessels in the IWT sector, the need for immediate action is critical to make significant strides towards climate neutrality by 2050. This research delves into the complexities of multi-objective optimisation, balancing investment costs with operational efficiencies. It provides an in-depth sensitivity analysis of the impact of battery capacity, battery costs, the availability of docking station spots, and loading state of vessels on the overall system configuration.
The study emphasises the importance of carefully attributing and balancing weights to each objective to optimise network design and total costs. Moreover, the findings support the continued development of higher battery capacities and strategic resource allocation to enhance the efficiency and sustainability of IWT. The significance of this research lies in its potential to inform the deployment of renewable energy-powered, zero-carbon ships, contributing to global efforts to mitigate climate change and promote sustainable development in the maritime sector. Moreover, the insights gained in this thesis inform recommendations, aimed at guiding future research. ...
The study emphasises the importance of carefully attributing and balancing weights to each objective to optimise network design and total costs. Moreover, the findings support the continued development of higher battery capacities and strategic resource allocation to enhance the efficiency and sustainability of IWT. The significance of this research lies in its potential to inform the deployment of renewable energy-powered, zero-carbon ships, contributing to global efforts to mitigate climate change and promote sustainable development in the maritime sector. Moreover, the insights gained in this thesis inform recommendations, aimed at guiding future research. ...
The ongoing global push towards energy transformation and decarbonisation underscores the urgent need for reducing greenhouse gas emissions, particularly in the maritime sector, which accounts for a significant portion of global trade and emissions. This study focuses on inland waterway transportation (IWT) as a key area for implementing zero-emission solutions, especially through the use of battery swapping technology. Given the average age and expected lifespan of vessels in the IWT sector, the need for immediate action is critical to make significant strides towards climate neutrality by 2050. This research delves into the complexities of multi-objective optimisation, balancing investment costs with operational efficiencies. It provides an in-depth sensitivity analysis of the impact of battery capacity, battery costs, the availability of docking station spots, and loading state of vessels on the overall system configuration.
The study emphasises the importance of carefully attributing and balancing weights to each objective to optimise network design and total costs. Moreover, the findings support the continued development of higher battery capacities and strategic resource allocation to enhance the efficiency and sustainability of IWT. The significance of this research lies in its potential to inform the deployment of renewable energy-powered, zero-carbon ships, contributing to global efforts to mitigate climate change and promote sustainable development in the maritime sector. Moreover, the insights gained in this thesis inform recommendations, aimed at guiding future research.
The study emphasises the importance of carefully attributing and balancing weights to each objective to optimise network design and total costs. Moreover, the findings support the continued development of higher battery capacities and strategic resource allocation to enhance the efficiency and sustainability of IWT. The significance of this research lies in its potential to inform the deployment of renewable energy-powered, zero-carbon ships, contributing to global efforts to mitigate climate change and promote sustainable development in the maritime sector. Moreover, the insights gained in this thesis inform recommendations, aimed at guiding future research.
Semiconductor product development becomes increasingly challenging due to diminishing product life cycles, miniaturization, introduction of new physical principles, and new manufacturing processes. These problems are compounded in the absence of standardized development processes for the most complex semiconductor products like MEMS technologies, because the manufacturing of these products is often outsourced. Suppliers play a pivoting role in the realization of the product, from product design until process design and ramp-up. The supplier selection problem in this industry denotes the challenges in finding the right supplier while meeting all the technical, process and business requirements. The contribution of this research is in presenting how to develop a generic methodology for data-driven co-development. Thereby, this work presents a novel product development framework that leverages co-development and supply chain integration through data-driven decision-making. Co-development is reached through standardized methods for generating the required engineering output for supplier selection. Supply chain integration is introduced in the early stages of product development. This synthesizes with the outsourced manufacturing processes. Clustering algorithms are used to effectively shortlist suppliers based on their competences, and provide insights into supplier profiles and gaps. The latter is used to draw strategies for developing unattainable technologies. Using the framework, the required engineering output for supplier selection was generated in 77% less time while reducing information asymmetries between actors in the product development process. Furthermore, the framework made it possible to quantify decisions, allowed for supplier profile recognition and gap identification through its hybrid automated approach, and supplier shortlisting in 95% less time. This efficiency does not only showcase the immediate benefits of the proposed methodology, but also lays the foundation for future research towards a fully automated approach in semiconductor product development. The developed framework includes information flows between actors and steps in the product development process, their interfaces and demonstrates its added value and potential for a fully automated yet efficient future approach in semiconductor product development.
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Semiconductor product development becomes increasingly challenging due to diminishing product life cycles, miniaturization, introduction of new physical principles, and new manufacturing processes. These problems are compounded in the absence of standardized development processes for the most complex semiconductor products like MEMS technologies, because the manufacturing of these products is often outsourced. Suppliers play a pivoting role in the realization of the product, from product design until process design and ramp-up. The supplier selection problem in this industry denotes the challenges in finding the right supplier while meeting all the technical, process and business requirements. The contribution of this research is in presenting how to develop a generic methodology for data-driven co-development. Thereby, this work presents a novel product development framework that leverages co-development and supply chain integration through data-driven decision-making. Co-development is reached through standardized methods for generating the required engineering output for supplier selection. Supply chain integration is introduced in the early stages of product development. This synthesizes with the outsourced manufacturing processes. Clustering algorithms are used to effectively shortlist suppliers based on their competences, and provide insights into supplier profiles and gaps. The latter is used to draw strategies for developing unattainable technologies. Using the framework, the required engineering output for supplier selection was generated in 77% less time while reducing information asymmetries between actors in the product development process. Furthermore, the framework made it possible to quantify decisions, allowed for supplier profile recognition and gap identification through its hybrid automated approach, and supplier shortlisting in 95% less time. This efficiency does not only showcase the immediate benefits of the proposed methodology, but also lays the foundation for future research towards a fully automated approach in semiconductor product development. The developed framework includes information flows between actors and steps in the product development process, their interfaces and demonstrates its added value and potential for a fully automated yet efficient future approach in semiconductor product development.
Integrated infection and crowd behavior model for infection risk assessment onboard large passenger vessels
Investigating the effect of ship layout design, operational and behavioral measures on contagious disease spread
The development of the global COVID-19 pandemic from 2020 onward has had significant impact on the world and specifically the maritime industry. Striking examples were COVID-19 outbreaks onboard the Diamond Princess cruise vessel and the U.S.S. Theodore Roosevelt aircraft carrier at the start of the pandemic. Contagious disease management onboard large passenger ships remains a complex issue, amplified by the international character of the industry, confined environment and shared facilities. This report therefore presents an investigation into the effect of ship layout design, operational and behavioral measures on COVID-19 airborne infection risk onboard large passenger vessels. The novelty of this research lies with the integrated infection and crowd behavior model used to calculate agent-specific infection risk, incorporating guest and crew circulation through a passenger ship layout.
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The development of the global COVID-19 pandemic from 2020 onward has had significant impact on the world and specifically the maritime industry. Striking examples were COVID-19 outbreaks onboard the Diamond Princess cruise vessel and the U.S.S. Theodore Roosevelt aircraft carrier at the start of the pandemic. Contagious disease management onboard large passenger ships remains a complex issue, amplified by the international character of the industry, confined environment and shared facilities. This report therefore presents an investigation into the effect of ship layout design, operational and behavioral measures on COVID-19 airborne infection risk onboard large passenger vessels. The novelty of this research lies with the integrated infection and crowd behavior model used to calculate agent-specific infection risk, incorporating guest and crew circulation through a passenger ship layout.
Supporting machine vision system design for quality control in manufacturing
An automotive case study
Master thesis
(2024)
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L.W. van Keulen, A. Napoleone, R. Leite Patrão, A. Andersson, Y. Jacquet, B. Atasoy, R. Sabzevari
Quality control is considered an important process in manufacturing to minimise waste related to the manufacturing process. A way of performing quality control is with help of machine vision. Understanding all decisions that are to be made when designing a machine vision system for quality control is essential, but challenging. This research presents a method that aids decision-makers in the process of designing these machine vision systems for quality control so the efficiency of the decision-making process can be increased. The method results in a knowledge base and a tool that can both be considered as useful to decision-makers. The method was designed by following a Design Science Research approach and the method was applied on a use case in the automotive industry to prove that it addresses its goal.
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Quality control is considered an important process in manufacturing to minimise waste related to the manufacturing process. A way of performing quality control is with help of machine vision. Understanding all decisions that are to be made when designing a machine vision system for quality control is essential, but challenging. This research presents a method that aids decision-makers in the process of designing these machine vision systems for quality control so the efficiency of the decision-making process can be increased. The method results in a knowledge base and a tool that can both be considered as useful to decision-makers. The method was designed by following a Design Science Research approach and the method was applied on a use case in the automotive industry to prove that it addresses its goal.
Assessing the impact of charging operations on electric lift automated guided vehicles
A simulation study at APM Terminals MVII
The Automated Guided Vehicle (AGV) is the most commonly used piece of horizontal movement that uses electric power from an internal battery pack. With much focus on the charging strategy, current charge operation models lack the ability to model large scale terminal operations and to incorporate new types of horizontal movement. The objective of this research is to show the impact of recharge operations on terminal performances. A discrete event simulation model is based on the characteristics of APMT MVII to model the current impact. The results of numerical experiments show charging operations have a limited impact on the productivity of Lift-AGV. An exception is the change in battery type. Using a Li-ion battery lowers the need for recharging and increases total productivity. The findings show the importance of integrating modelling to accurately project terminal operations.
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The Automated Guided Vehicle (AGV) is the most commonly used piece of horizontal movement that uses electric power from an internal battery pack. With much focus on the charging strategy, current charge operation models lack the ability to model large scale terminal operations and to incorporate new types of horizontal movement. The objective of this research is to show the impact of recharge operations on terminal performances. A discrete event simulation model is based on the characteristics of APMT MVII to model the current impact. The results of numerical experiments show charging operations have a limited impact on the productivity of Lift-AGV. An exception is the change in battery type. Using a Li-ion battery lowers the need for recharging and increases total productivity. The findings show the importance of integrating modelling to accurately project terminal operations.