MD
M.B. Duinkerken
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
70 records found
1
Dimensioning CO2 Shipping Networks for Cost-Efficient and Robust Offshore Transport
A case study at RWE for North Sea Offshore Storage
CO2 shipping is increasingly used within CCS systems to transport captured CO2 from emission sources to offshore storage. However, system design is commonly based on nominal operating conditions, while real-world operations are subject to uncertainty. This study investigates how CO2 shipping systems can be dimensioned to achieve cost-efficient and robust transport to offshore storage under operational uncertainty. A discrete-event simulation model was developed and applied to the RWE BECCUS case in the Eemshaven. Six operating scenarios and sixteen system configurations were evaluated, varying in vessel size, fleet size, pressure regime, and storage capacity. The results show that system design is the dominant factor determining both operational and economic performance, while operational uncertainty has a limited influence on overall outcomes. Offshore injection forms the main bottleneck, and a transport capacity threshold of approximately 80–140 kt was identified for the investigated case. Storage acts primarily as a buffer and cannot compensate for insufficient transport capacity. No clear trade-off was found between cost and performance, as well-dimensioned systems achieve both low non-capture rates and low transport costs. Low-pressure systems provide the most attractive balance between operational and economic performance. Overall, robustness is achieved primarily through correct system design rather than through mitigating operational uncertainty.
...
CO2 shipping is increasingly used within CCS systems to transport captured CO2 from emission sources to offshore storage. However, system design is commonly based on nominal operating conditions, while real-world operations are subject to uncertainty. This study investigates how CO2 shipping systems can be dimensioned to achieve cost-efficient and robust transport to offshore storage under operational uncertainty. A discrete-event simulation model was developed and applied to the RWE BECCUS case in the Eemshaven. Six operating scenarios and sixteen system configurations were evaluated, varying in vessel size, fleet size, pressure regime, and storage capacity. The results show that system design is the dominant factor determining both operational and economic performance, while operational uncertainty has a limited influence on overall outcomes. Offshore injection forms the main bottleneck, and a transport capacity threshold of approximately 80–140 kt was identified for the investigated case. Storage acts primarily as a buffer and cannot compensate for insufficient transport capacity. No clear trade-off was found between cost and performance, as well-dimensioned systems achieve both low non-capture rates and low transport costs. Low-pressure systems provide the most attractive balance between operational and economic performance. Overall, robustness is achieved primarily through correct system design rather than through mitigating operational uncertainty.
Dynamic Vessel Speed Optimisation
A discrete-event simulation study on deep-sea container vessels arriving at Rotterdam World Gateway
Master thesis
(2026)
-
J.P.J. ter Meulen, M.B. Duinkerken, R.R. Negenborn, M.L. Ochoa Barnuevo, Age Dijkstra, Arjen de Waal
Deep-sea container vessels play a crucial role in the world economy. Despite the enormous operating costs, these vessels often have to wait at the anchorage point due to limited berth availability at container terminals. This thesis investigates the influence of vessel speed optimisation on the performance of container vessels and on berth occupancy at the container terminal, using a discrete-event simulation model.
Most of the existing research focuses either on the berth allocation problem or on vessel speed optimisation. If the two topics are combined, research often focuses on homogeneous vessels or on vessels departing from a fixed point. This thesis focuses on a more realistic scenario: implementing vessel speed optimisation in the berth allocation problem for heterogeneous vessels departing from ports at varying distances from the focal port.
In this model, four different configurations are compared: (1) A baseline configuration, in which vessels sail at their economic speed and are served at the terminal on a first-come, first-served basis, (2) single speed optimisation at departure, where the speed of the vessel is optimised only once at the departure of the previous terminal, (3) re-optimisation at new departures, where the speed of the vessel is optimised, when a new vessel departs which has a smaller remaining distance and (4) global speed optimisation, where the terminal adjusts the velocities for all sailing vessels at time intervals, and new vessel departures.
These configurations are compared in a scenario that represents operations at Rotterdam World Gateway in 2024 and serves as a baseline. Besides the baseline scenario, the global speed optimisation is evaluated in two additional scenarios in which the number of vessels arriving at the port is increased.
The model is evaluated on three KPIs: the departure delay, vessel costs and berth occupancy. The vessel's costs are divided into operating, fuel and waiting costs. Together, the departure delay and costs indicate the vessel's performance. The vessel's performance is indicated both at the system level, as an average over all vessels arriving, and at the individual level, over all vessels that have departed from the same port. The terminal's berth occupancy remains the same within a scenario, but it increases across scenarios as the number of vessels arriving every 2 weeks increases.
The results show that the global speed optimisation strategy reduces the average vessel costs by approximately $30000 per trip when compared to the baseline at scenario 1. The results also indicate that with global speed optimisation, the berth occupancy can increase by up to 2.2%, corresponding to approximately 44,000 additional TEU handled annually, while maintaining lower average vessel costs and comparable departure delays compared to the baseline scenario. Further increases, however, lead to congestion and a decrease in overall vessel performance.
These findings demonstrate that improved coordination between vessels and terminals can improve vessel performance while enabling an increase in berth occupancy and throughput at container terminals. ...
Most of the existing research focuses either on the berth allocation problem or on vessel speed optimisation. If the two topics are combined, research often focuses on homogeneous vessels or on vessels departing from a fixed point. This thesis focuses on a more realistic scenario: implementing vessel speed optimisation in the berth allocation problem for heterogeneous vessels departing from ports at varying distances from the focal port.
In this model, four different configurations are compared: (1) A baseline configuration, in which vessels sail at their economic speed and are served at the terminal on a first-come, first-served basis, (2) single speed optimisation at departure, where the speed of the vessel is optimised only once at the departure of the previous terminal, (3) re-optimisation at new departures, where the speed of the vessel is optimised, when a new vessel departs which has a smaller remaining distance and (4) global speed optimisation, where the terminal adjusts the velocities for all sailing vessels at time intervals, and new vessel departures.
These configurations are compared in a scenario that represents operations at Rotterdam World Gateway in 2024 and serves as a baseline. Besides the baseline scenario, the global speed optimisation is evaluated in two additional scenarios in which the number of vessels arriving at the port is increased.
The model is evaluated on three KPIs: the departure delay, vessel costs and berth occupancy. The vessel's costs are divided into operating, fuel and waiting costs. Together, the departure delay and costs indicate the vessel's performance. The vessel's performance is indicated both at the system level, as an average over all vessels arriving, and at the individual level, over all vessels that have departed from the same port. The terminal's berth occupancy remains the same within a scenario, but it increases across scenarios as the number of vessels arriving every 2 weeks increases.
The results show that the global speed optimisation strategy reduces the average vessel costs by approximately $30000 per trip when compared to the baseline at scenario 1. The results also indicate that with global speed optimisation, the berth occupancy can increase by up to 2.2%, corresponding to approximately 44,000 additional TEU handled annually, while maintaining lower average vessel costs and comparable departure delays compared to the baseline scenario. Further increases, however, lead to congestion and a decrease in overall vessel performance.
These findings demonstrate that improved coordination between vessels and terminals can improve vessel performance while enabling an increase in berth occupancy and throughput at container terminals. ...
Deep-sea container vessels play a crucial role in the world economy. Despite the enormous operating costs, these vessels often have to wait at the anchorage point due to limited berth availability at container terminals. This thesis investigates the influence of vessel speed optimisation on the performance of container vessels and on berth occupancy at the container terminal, using a discrete-event simulation model.
Most of the existing research focuses either on the berth allocation problem or on vessel speed optimisation. If the two topics are combined, research often focuses on homogeneous vessels or on vessels departing from a fixed point. This thesis focuses on a more realistic scenario: implementing vessel speed optimisation in the berth allocation problem for heterogeneous vessels departing from ports at varying distances from the focal port.
In this model, four different configurations are compared: (1) A baseline configuration, in which vessels sail at their economic speed and are served at the terminal on a first-come, first-served basis, (2) single speed optimisation at departure, where the speed of the vessel is optimised only once at the departure of the previous terminal, (3) re-optimisation at new departures, where the speed of the vessel is optimised, when a new vessel departs which has a smaller remaining distance and (4) global speed optimisation, where the terminal adjusts the velocities for all sailing vessels at time intervals, and new vessel departures.
These configurations are compared in a scenario that represents operations at Rotterdam World Gateway in 2024 and serves as a baseline. Besides the baseline scenario, the global speed optimisation is evaluated in two additional scenarios in which the number of vessels arriving at the port is increased.
The model is evaluated on three KPIs: the departure delay, vessel costs and berth occupancy. The vessel's costs are divided into operating, fuel and waiting costs. Together, the departure delay and costs indicate the vessel's performance. The vessel's performance is indicated both at the system level, as an average over all vessels arriving, and at the individual level, over all vessels that have departed from the same port. The terminal's berth occupancy remains the same within a scenario, but it increases across scenarios as the number of vessels arriving every 2 weeks increases.
The results show that the global speed optimisation strategy reduces the average vessel costs by approximately $30000 per trip when compared to the baseline at scenario 1. The results also indicate that with global speed optimisation, the berth occupancy can increase by up to 2.2%, corresponding to approximately 44,000 additional TEU handled annually, while maintaining lower average vessel costs and comparable departure delays compared to the baseline scenario. Further increases, however, lead to congestion and a decrease in overall vessel performance.
These findings demonstrate that improved coordination between vessels and terminals can improve vessel performance while enabling an increase in berth occupancy and throughput at container terminals.
Most of the existing research focuses either on the berth allocation problem or on vessel speed optimisation. If the two topics are combined, research often focuses on homogeneous vessels or on vessels departing from a fixed point. This thesis focuses on a more realistic scenario: implementing vessel speed optimisation in the berth allocation problem for heterogeneous vessels departing from ports at varying distances from the focal port.
In this model, four different configurations are compared: (1) A baseline configuration, in which vessels sail at their economic speed and are served at the terminal on a first-come, first-served basis, (2) single speed optimisation at departure, where the speed of the vessel is optimised only once at the departure of the previous terminal, (3) re-optimisation at new departures, where the speed of the vessel is optimised, when a new vessel departs which has a smaller remaining distance and (4) global speed optimisation, where the terminal adjusts the velocities for all sailing vessels at time intervals, and new vessel departures.
These configurations are compared in a scenario that represents operations at Rotterdam World Gateway in 2024 and serves as a baseline. Besides the baseline scenario, the global speed optimisation is evaluated in two additional scenarios in which the number of vessels arriving at the port is increased.
The model is evaluated on three KPIs: the departure delay, vessel costs and berth occupancy. The vessel's costs are divided into operating, fuel and waiting costs. Together, the departure delay and costs indicate the vessel's performance. The vessel's performance is indicated both at the system level, as an average over all vessels arriving, and at the individual level, over all vessels that have departed from the same port. The terminal's berth occupancy remains the same within a scenario, but it increases across scenarios as the number of vessels arriving every 2 weeks increases.
The results show that the global speed optimisation strategy reduces the average vessel costs by approximately $30000 per trip when compared to the baseline at scenario 1. The results also indicate that with global speed optimisation, the berth occupancy can increase by up to 2.2%, corresponding to approximately 44,000 additional TEU handled annually, while maintaining lower average vessel costs and comparable departure delays compared to the baseline scenario. Further increases, however, lead to congestion and a decrease in overall vessel performance.
These findings demonstrate that improved coordination between vessels and terminals can improve vessel performance while enabling an increase in berth occupancy and throughput at container terminals.
Resource Allocation within Multi-Client Warehouses
A Multi-Objective Optimisation Approach for Space and Labour Sharing Decisions
Multi-client warehouses (MCWs) are becoming larger, more complex, and increasingly important in third-party logistics. In these warehouses, multiple clients are served within one shared facility. This creates opportunities for economies of scale, but also raises questions about how space and labour should be allocated. This thesis was motivated by the challenge faced by DSV at Logistics Park Moerdijk (LPM), where no structured, data-driven framework yet existed to support tactical allocation decisions for future clients. The central research question was:
How can resource allocation and resource sharing be optimised within the fixed infrastructure of a multi-client warehouse?
The research focuses on tactical decision-making: the warehouse infrastructure is assumed fixed, while client placement, zoning, clustering, and labour-sharing policies can be adjusted. The study combines a literature review, expert interviews, IDEF0 modelling, and a mathematical multi-objective optimisation model. The model was implemented as a mixed-integer linear programming (MILP) model and applied to a case study based on DSV’s LPM warehouse.
The system analysis showed that MCWs cannot be treated as scaled-up dedicated warehouses. The multi-client context adds complexity through client heterogeneity and sequential onboarding. Clients differ in volume, SKU characteristics, order structures, handling requirements, and service-level agreements. Reactive allocation decisions can therefore lead to inefficient use of space over time. Space and labour were identified as the most relevant tactical resources. Key performance indicators were order-picking time, service level, total cost per pallet, and picking productivity.
The literature review showed that existing research does not provide an integrated framework for tactical resource allocation in MCWs. This thesis combines resource-sharing and allocation perspectives into a tactical multi-client framework. The main decision levers were translated into three policy dimensions: clustering versus no clustering, adjacent versus fragmented zoning, and dedicated, zone-based, or shared labour.
The optimisation model represents the warehouse as storage cells connected to blocks, zones, and optional clusters. Client demand is represented through aggregated product groups, making the model suitable for tactical planning when detailed data are unavailable. The model allocates product-group volume, assigns clients to blocks, and determines labour requirements under different policies. Two objective functions are considered: minimisation of total cost and minimisation of total picking time.
The model was verified through tests and validated through single-client and multi-client cases, showing plausible allocation patterns. Results indicate that the model is most useful as a policy comparison tool. Pareto fronts were often narrow, meaning insights lie mainly in comparing policy configurations. Fragmented zoning performed best, with configurations achieving strong reductions in cost and picking time, showing that spatial flexibility is a key driver of performance.
However, analytically strong solutions are not always practical. High flexibility can be difficult to manage in daily operations. Therefore, manageability was added as a third evaluation dimension. While flexible configurations performed best on cost and time, more structured configurations performed better when manageability was included. This shows that practical implementability is crucial in decision-making.
The main conclusion is that tactical optimisation in MCWs should focus on controlled flexibility rather than maximum flexibility. Fragmented zoning should generally be preferred, but bounded by operational constraints. Clustering should only be applied when it improves clarity, and labour should be organised either as dedicated or shared under clear rules.
This thesis contributes by framing MCW allocation as a multi-objective problem and by providing a MILP model linking system characteristics, policy choices, and performance. It offers practical guidance for tactical decision-making in large multi-client warehouses. Limitations include the use of aggregated data, a static model, and a focus on picking. Future research should incorporate dynamic settings, additional processes, and improved data. ...
How can resource allocation and resource sharing be optimised within the fixed infrastructure of a multi-client warehouse?
The research focuses on tactical decision-making: the warehouse infrastructure is assumed fixed, while client placement, zoning, clustering, and labour-sharing policies can be adjusted. The study combines a literature review, expert interviews, IDEF0 modelling, and a mathematical multi-objective optimisation model. The model was implemented as a mixed-integer linear programming (MILP) model and applied to a case study based on DSV’s LPM warehouse.
The system analysis showed that MCWs cannot be treated as scaled-up dedicated warehouses. The multi-client context adds complexity through client heterogeneity and sequential onboarding. Clients differ in volume, SKU characteristics, order structures, handling requirements, and service-level agreements. Reactive allocation decisions can therefore lead to inefficient use of space over time. Space and labour were identified as the most relevant tactical resources. Key performance indicators were order-picking time, service level, total cost per pallet, and picking productivity.
The literature review showed that existing research does not provide an integrated framework for tactical resource allocation in MCWs. This thesis combines resource-sharing and allocation perspectives into a tactical multi-client framework. The main decision levers were translated into three policy dimensions: clustering versus no clustering, adjacent versus fragmented zoning, and dedicated, zone-based, or shared labour.
The optimisation model represents the warehouse as storage cells connected to blocks, zones, and optional clusters. Client demand is represented through aggregated product groups, making the model suitable for tactical planning when detailed data are unavailable. The model allocates product-group volume, assigns clients to blocks, and determines labour requirements under different policies. Two objective functions are considered: minimisation of total cost and minimisation of total picking time.
The model was verified through tests and validated through single-client and multi-client cases, showing plausible allocation patterns. Results indicate that the model is most useful as a policy comparison tool. Pareto fronts were often narrow, meaning insights lie mainly in comparing policy configurations. Fragmented zoning performed best, with configurations achieving strong reductions in cost and picking time, showing that spatial flexibility is a key driver of performance.
However, analytically strong solutions are not always practical. High flexibility can be difficult to manage in daily operations. Therefore, manageability was added as a third evaluation dimension. While flexible configurations performed best on cost and time, more structured configurations performed better when manageability was included. This shows that practical implementability is crucial in decision-making.
The main conclusion is that tactical optimisation in MCWs should focus on controlled flexibility rather than maximum flexibility. Fragmented zoning should generally be preferred, but bounded by operational constraints. Clustering should only be applied when it improves clarity, and labour should be organised either as dedicated or shared under clear rules.
This thesis contributes by framing MCW allocation as a multi-objective problem and by providing a MILP model linking system characteristics, policy choices, and performance. It offers practical guidance for tactical decision-making in large multi-client warehouses. Limitations include the use of aggregated data, a static model, and a focus on picking. Future research should incorporate dynamic settings, additional processes, and improved data. ...
Multi-client warehouses (MCWs) are becoming larger, more complex, and increasingly important in third-party logistics. In these warehouses, multiple clients are served within one shared facility. This creates opportunities for economies of scale, but also raises questions about how space and labour should be allocated. This thesis was motivated by the challenge faced by DSV at Logistics Park Moerdijk (LPM), where no structured, data-driven framework yet existed to support tactical allocation decisions for future clients. The central research question was:
How can resource allocation and resource sharing be optimised within the fixed infrastructure of a multi-client warehouse?
The research focuses on tactical decision-making: the warehouse infrastructure is assumed fixed, while client placement, zoning, clustering, and labour-sharing policies can be adjusted. The study combines a literature review, expert interviews, IDEF0 modelling, and a mathematical multi-objective optimisation model. The model was implemented as a mixed-integer linear programming (MILP) model and applied to a case study based on DSV’s LPM warehouse.
The system analysis showed that MCWs cannot be treated as scaled-up dedicated warehouses. The multi-client context adds complexity through client heterogeneity and sequential onboarding. Clients differ in volume, SKU characteristics, order structures, handling requirements, and service-level agreements. Reactive allocation decisions can therefore lead to inefficient use of space over time. Space and labour were identified as the most relevant tactical resources. Key performance indicators were order-picking time, service level, total cost per pallet, and picking productivity.
The literature review showed that existing research does not provide an integrated framework for tactical resource allocation in MCWs. This thesis combines resource-sharing and allocation perspectives into a tactical multi-client framework. The main decision levers were translated into three policy dimensions: clustering versus no clustering, adjacent versus fragmented zoning, and dedicated, zone-based, or shared labour.
The optimisation model represents the warehouse as storage cells connected to blocks, zones, and optional clusters. Client demand is represented through aggregated product groups, making the model suitable for tactical planning when detailed data are unavailable. The model allocates product-group volume, assigns clients to blocks, and determines labour requirements under different policies. Two objective functions are considered: minimisation of total cost and minimisation of total picking time.
The model was verified through tests and validated through single-client and multi-client cases, showing plausible allocation patterns. Results indicate that the model is most useful as a policy comparison tool. Pareto fronts were often narrow, meaning insights lie mainly in comparing policy configurations. Fragmented zoning performed best, with configurations achieving strong reductions in cost and picking time, showing that spatial flexibility is a key driver of performance.
However, analytically strong solutions are not always practical. High flexibility can be difficult to manage in daily operations. Therefore, manageability was added as a third evaluation dimension. While flexible configurations performed best on cost and time, more structured configurations performed better when manageability was included. This shows that practical implementability is crucial in decision-making.
The main conclusion is that tactical optimisation in MCWs should focus on controlled flexibility rather than maximum flexibility. Fragmented zoning should generally be preferred, but bounded by operational constraints. Clustering should only be applied when it improves clarity, and labour should be organised either as dedicated or shared under clear rules.
This thesis contributes by framing MCW allocation as a multi-objective problem and by providing a MILP model linking system characteristics, policy choices, and performance. It offers practical guidance for tactical decision-making in large multi-client warehouses. Limitations include the use of aggregated data, a static model, and a focus on picking. Future research should incorporate dynamic settings, additional processes, and improved data.
How can resource allocation and resource sharing be optimised within the fixed infrastructure of a multi-client warehouse?
The research focuses on tactical decision-making: the warehouse infrastructure is assumed fixed, while client placement, zoning, clustering, and labour-sharing policies can be adjusted. The study combines a literature review, expert interviews, IDEF0 modelling, and a mathematical multi-objective optimisation model. The model was implemented as a mixed-integer linear programming (MILP) model and applied to a case study based on DSV’s LPM warehouse.
The system analysis showed that MCWs cannot be treated as scaled-up dedicated warehouses. The multi-client context adds complexity through client heterogeneity and sequential onboarding. Clients differ in volume, SKU characteristics, order structures, handling requirements, and service-level agreements. Reactive allocation decisions can therefore lead to inefficient use of space over time. Space and labour were identified as the most relevant tactical resources. Key performance indicators were order-picking time, service level, total cost per pallet, and picking productivity.
The literature review showed that existing research does not provide an integrated framework for tactical resource allocation in MCWs. This thesis combines resource-sharing and allocation perspectives into a tactical multi-client framework. The main decision levers were translated into three policy dimensions: clustering versus no clustering, adjacent versus fragmented zoning, and dedicated, zone-based, or shared labour.
The optimisation model represents the warehouse as storage cells connected to blocks, zones, and optional clusters. Client demand is represented through aggregated product groups, making the model suitable for tactical planning when detailed data are unavailable. The model allocates product-group volume, assigns clients to blocks, and determines labour requirements under different policies. Two objective functions are considered: minimisation of total cost and minimisation of total picking time.
The model was verified through tests and validated through single-client and multi-client cases, showing plausible allocation patterns. Results indicate that the model is most useful as a policy comparison tool. Pareto fronts were often narrow, meaning insights lie mainly in comparing policy configurations. Fragmented zoning performed best, with configurations achieving strong reductions in cost and picking time, showing that spatial flexibility is a key driver of performance.
However, analytically strong solutions are not always practical. High flexibility can be difficult to manage in daily operations. Therefore, manageability was added as a third evaluation dimension. While flexible configurations performed best on cost and time, more structured configurations performed better when manageability was included. This shows that practical implementability is crucial in decision-making.
The main conclusion is that tactical optimisation in MCWs should focus on controlled flexibility rather than maximum flexibility. Fragmented zoning should generally be preferred, but bounded by operational constraints. Clustering should only be applied when it improves clarity, and labour should be organised either as dedicated or shared under clear rules.
This thesis contributes by framing MCW allocation as a multi-objective problem and by providing a MILP model linking system characteristics, policy choices, and performance. It offers practical guidance for tactical decision-making in large multi-client warehouses. Limitations include the use of aggregated data, a static model, and a focus on picking. Future research should incorporate dynamic settings, additional processes, and improved data.
Due to poor working conditions caused by emissions, heavy workloads, and significant staff shortages, airlines and airports are turning towards automation for a solution. While many automation developments focus on turnaround management and scheduling, research on the automated execution of turnaround operations is lacking, especially within arrival and departure operations and on the usage of multi-operation vehicles. This study aims to determine whether combining multiple operations into a single autonomous platform is operationally and financially beneficial. Task dependencies and interconnections were modelled, allowing the simulation of interactions and propagation of delays. A financial model was then developed to quantify the impact of changes in turnaround durations on delay-related costs. Based on net present value, reliability, flexibility, and technological readiness, different automated concepts developed for each operation were assessed and compared with multi-operation automated platforms. Additionally, the effects of automation on the scheduling, spatial management, and communications during aircraft turnarounds were analysed, accompanied by a risk analysis. Findings from this study indicate that the automation of arrival and departure operations can provide operational gains and positive financial returns, provided reliability performance meets the required thresholds. Multi-operation platforms enhance flexibility significantly, but may underperform in operational efficiency and financial viability. The results from this thesis provide an informed approach in automating aircraft turnarounds, supporting decision-making on automation concepts and accelerating the transition to an environment that ensures occupational health and safety for ground staff.
...
...
Due to poor working conditions caused by emissions, heavy workloads, and significant staff shortages, airlines and airports are turning towards automation for a solution. While many automation developments focus on turnaround management and scheduling, research on the automated execution of turnaround operations is lacking, especially within arrival and departure operations and on the usage of multi-operation vehicles. This study aims to determine whether combining multiple operations into a single autonomous platform is operationally and financially beneficial. Task dependencies and interconnections were modelled, allowing the simulation of interactions and propagation of delays. A financial model was then developed to quantify the impact of changes in turnaround durations on delay-related costs. Based on net present value, reliability, flexibility, and technological readiness, different automated concepts developed for each operation were assessed and compared with multi-operation automated platforms. Additionally, the effects of automation on the scheduling, spatial management, and communications during aircraft turnarounds were analysed, accompanied by a risk analysis. Findings from this study indicate that the automation of arrival and departure operations can provide operational gains and positive financial returns, provided reliability performance meets the required thresholds. Multi-operation platforms enhance flexibility significantly, but may underperform in operational efficiency and financial viability. The results from this thesis provide an informed approach in automating aircraft turnarounds, supporting decision-making on automation concepts and accelerating the transition to an environment that ensures occupational health and safety for ground staff.
The objective of this study is to develop a predictive model that can accurately predict the marshaller task demand at an airport. Amsterdam Airport Schiphol is used as a case study for this research. Marshallers form a critical link between ground operations and airside operations, ensuring safe aircraft movements and a continuous operation. Findings indicate a misalignment between static staffing practices and dynamic operational demand. The aim is to build a data-driven model using historical data and influencing factors. ADS-B vehicle data, geofence polygons, aircraft arrival times, and engine testing records were used to determine the task demand. A process of spatial matching, task labelling, and segmentation was used to combine these datasets, after which the labelled segments were then combined into hourly counts. Resulting in an aggregated dataset with hourly task count that can be used in a machine learning model. LightGBM was implemented as a multi-output model that forecasts all task types jointly. The models were trained on nine months of data and performance was evaluated using the Mean Absolute Error, Root Mean Squared Error, Mean Error and the Coefficient of Determination. The models were validated each on their own forecasting horizons: 24 hours, 168 hours, and 2160 hours, and compared to two baselines: Seasonal naïve, and weekly hourly average. The results show that the predictive capability strongly differs between the task type. Docking is the only task that can be forecasted reliably, it follows daily patterns and has a clear link with aircraft arrivals. Docking shows stable performances over all horizons with RMSE values between 1.66 and 1.75 and MAE values between 1.26 and 1.35. The model outperformed the baselines on all prediction horizons and on all evaluation metrics. The other tasks did not show any valuable forecasting, with R2 values close to zero or negative. This indicates that these tasks are irregular or occur in low volumes. Making them not suitable to provide reliable hourly forecasts with the current data. Future work could assess if additional operational factors improve the predictive structure of the other three tasks. A longer dataset may also reveal patterns that are not visible in the current nine month data. Other resolutions such as 15-minute or 30-minute time intervals, or shift intervals may be relevant.
...
The objective of this study is to develop a predictive model that can accurately predict the marshaller task demand at an airport. Amsterdam Airport Schiphol is used as a case study for this research. Marshallers form a critical link between ground operations and airside operations, ensuring safe aircraft movements and a continuous operation. Findings indicate a misalignment between static staffing practices and dynamic operational demand. The aim is to build a data-driven model using historical data and influencing factors. ADS-B vehicle data, geofence polygons, aircraft arrival times, and engine testing records were used to determine the task demand. A process of spatial matching, task labelling, and segmentation was used to combine these datasets, after which the labelled segments were then combined into hourly counts. Resulting in an aggregated dataset with hourly task count that can be used in a machine learning model. LightGBM was implemented as a multi-output model that forecasts all task types jointly. The models were trained on nine months of data and performance was evaluated using the Mean Absolute Error, Root Mean Squared Error, Mean Error and the Coefficient of Determination. The models were validated each on their own forecasting horizons: 24 hours, 168 hours, and 2160 hours, and compared to two baselines: Seasonal naïve, and weekly hourly average. The results show that the predictive capability strongly differs between the task type. Docking is the only task that can be forecasted reliably, it follows daily patterns and has a clear link with aircraft arrivals. Docking shows stable performances over all horizons with RMSE values between 1.66 and 1.75 and MAE values between 1.26 and 1.35. The model outperformed the baselines on all prediction horizons and on all evaluation metrics. The other tasks did not show any valuable forecasting, with R2 values close to zero or negative. This indicates that these tasks are irregular or occur in low volumes. Making them not suitable to provide reliable hourly forecasts with the current data. Future work could assess if additional operational factors improve the predictive structure of the other three tasks. A longer dataset may also reveal patterns that are not visible in the current nine month data. Other resolutions such as 15-minute or 30-minute time intervals, or shift intervals may be relevant.
The decarbonization of heavy-duty road freight transport requires overcoming the dual challenge of limited driving range and insufficient charging infrastructure for battery-electric trucks (BETs). This thesis develops a nationwide bi-level optimization framework that integrates the deployment of static charging stations (SCS), electrified road systems (ERS), and truck-level battery assignment, leveraging large-scale tour-based freight data from the Netherlands. By operating explicitly at the tour level rather than the trip level, the framework captures cumulative energy feasibility across multiple linked trips, thereby providing a more realistic representation of freight operations. The model is solved using a Genetic Algorithm (GA), capable of evaluating more than 1.5 million tours and 3.5 million trips, and validated against exact MILP solutions on small instances. The results show that the optimization raises feasibility from 58% to 89.9%, reducing infeasible tours to 10.1%, while overall fitness improves by 34.7%. ERS emerges as the backbone of electrification, with 12,792 km deployed (€25.7 billion, 65.6% of CAPEX), while 251 SCS facilities (€50.2 million, <1% of CAPEX) provide low-cost redundancy at regional hubs. Battery allocation is highly heterogeneous: 25% of trucks operate on 90 kWh, 40% on 600 kWh, and the remainder on intermediate sizes, yielding an average of 357 kWh—closely aligned with ElaadNL’s benchmark of 289.5 kWh/day. This heterogeneity reduces battery CAPEX by approximately 19% compared to a uniform-capacity baseline. Operating expenditures (OPEX) are dominated by ERS charging, while penalty costs for infeasible tours remain substantial, averaging €362 per unserved tour. These findings demonstrate that nationwide electrification is feasible under a layered strategy: ERS as the long-haul backbone, SCS as regional redundancy, and heterogeneous batteries as cost optimizers. The study advances the literature by moving from trip-level to tour-level modelling at unprecedented scale, explicitly quantifying infeasibility, and decomposing system costs into CAPEX, OPEX, and penalties. The results provide actionable insights for policymakers and industry stakeholders seeking cost-effective and operationally viable pathways for freight decarbonization in the Netherlands and beyond.
...
The decarbonization of heavy-duty road freight transport requires overcoming the dual challenge of limited driving range and insufficient charging infrastructure for battery-electric trucks (BETs). This thesis develops a nationwide bi-level optimization framework that integrates the deployment of static charging stations (SCS), electrified road systems (ERS), and truck-level battery assignment, leveraging large-scale tour-based freight data from the Netherlands. By operating explicitly at the tour level rather than the trip level, the framework captures cumulative energy feasibility across multiple linked trips, thereby providing a more realistic representation of freight operations. The model is solved using a Genetic Algorithm (GA), capable of evaluating more than 1.5 million tours and 3.5 million trips, and validated against exact MILP solutions on small instances. The results show that the optimization raises feasibility from 58% to 89.9%, reducing infeasible tours to 10.1%, while overall fitness improves by 34.7%. ERS emerges as the backbone of electrification, with 12,792 km deployed (€25.7 billion, 65.6% of CAPEX), while 251 SCS facilities (€50.2 million, <1% of CAPEX) provide low-cost redundancy at regional hubs. Battery allocation is highly heterogeneous: 25% of trucks operate on 90 kWh, 40% on 600 kWh, and the remainder on intermediate sizes, yielding an average of 357 kWh—closely aligned with ElaadNL’s benchmark of 289.5 kWh/day. This heterogeneity reduces battery CAPEX by approximately 19% compared to a uniform-capacity baseline. Operating expenditures (OPEX) are dominated by ERS charging, while penalty costs for infeasible tours remain substantial, averaging €362 per unserved tour. These findings demonstrate that nationwide electrification is feasible under a layered strategy: ERS as the long-haul backbone, SCS as regional redundancy, and heterogeneous batteries as cost optimizers. The study advances the literature by moving from trip-level to tour-level modelling at unprecedented scale, explicitly quantifying infeasibility, and decomposing system costs into CAPEX, OPEX, and penalties. The results provide actionable insights for policymakers and industry stakeholders seeking cost-effective and operationally viable pathways for freight decarbonization in the Netherlands and beyond.
Dry Bulk Terminal Handling Performance
Measuring Unloading Performance Using Open Data
This study demonstrates that unloading performance at dry bulk terminals can be effectively assessed using open data sources such as AIS and aerial imagery. By structuring terminal operations into measurable performance indicators and applying the Overall Equipment Effectiveness (OEE) framework, we show that productivity, utilization, and occupancy can be quantified and benchmarked across terminals. The method is validated using data from two dry bulk terminals, revealing consistent patterns and highlighting operational differences. This approach enables scalable, low-cost performance analysis without requiring direct access to proprietary terminal data.
...
This study demonstrates that unloading performance at dry bulk terminals can be effectively assessed using open data sources such as AIS and aerial imagery. By structuring terminal operations into measurable performance indicators and applying the Overall Equipment Effectiveness (OEE) framework, we show that productivity, utilization, and occupancy can be quantified and benchmarked across terminals. The method is validated using data from two dry bulk terminals, revealing consistent patterns and highlighting operational differences. This approach enables scalable, low-cost performance analysis without requiring direct access to proprietary terminal data.
Over the past decade, consumer shopping habits have increasingly shifted towards online grocery purchases, creating a growing demand for efficient and scalable warehouse operations. As order volumes and complexity rise, the optimization of warehouse processes has become essential to meet requirements while controlling operational costs. This study addresses the Joint Order Batching Picker Routing Problem (JOBPRP) by developing an exact optimization approach and examining the interdependencies among key warehouse processes, such as batching, routing, and product allocation. Using a case study of Crisp B.V., an online grocery retailer, the proposed algorithm was implemented to optimize configurations across multiple temperature-controlled zones with varying operational characteristics, such as differing pick densities and operational constraints. The research shows that all warehouse processes are interconnected and the best performing configuration is depending on the operational characteristics of the warehouse. The optimization approach achieved a 39.14% reduction in weekly travel distance compared to Crisp’s current benchmark, highlighting its potential to significantly enhance travel distances. These findings highlight the significant impact of integrating order batching and picker routing on warehouse efficiency. The study not only demonstrates the critical correlation between warehouse processes but also provides actionable insights for optimizing order picking in high-density, large-scale warehouses
...
Over the past decade, consumer shopping habits have increasingly shifted towards online grocery purchases, creating a growing demand for efficient and scalable warehouse operations. As order volumes and complexity rise, the optimization of warehouse processes has become essential to meet requirements while controlling operational costs. This study addresses the Joint Order Batching Picker Routing Problem (JOBPRP) by developing an exact optimization approach and examining the interdependencies among key warehouse processes, such as batching, routing, and product allocation. Using a case study of Crisp B.V., an online grocery retailer, the proposed algorithm was implemented to optimize configurations across multiple temperature-controlled zones with varying operational characteristics, such as differing pick densities and operational constraints. The research shows that all warehouse processes are interconnected and the best performing configuration is depending on the operational characteristics of the warehouse. The optimization approach achieved a 39.14% reduction in weekly travel distance compared to Crisp’s current benchmark, highlighting its potential to significantly enhance travel distances. These findings highlight the significant impact of integrating order batching and picker routing on warehouse efficiency. The study not only demonstrates the critical correlation between warehouse processes but also provides actionable insights for optimizing order picking in high-density, large-scale warehouses
Conceptual design of a pipe routing system on a pipelaying vessel
A discrete event simulation study at Allseas
Master thesis
(2025)
-
L.N.M. Sijm, M.B. Duinkerken, X. Jiang, R. Leite Patrão, D. Bujakiewicz-Baars, J. Ramlakhan
The adoption of new technologies on pipelaying vessels is closely linked to the demand for full automation of pipe handling operations. However, pipe handling systems are rarely addressed in research, and, to the best of our knowledge, no studies exist that describe comparable intralogistics systems with similar control structures and movement restrictions. As a result, there is no clear reference for how such automation can be realized. This study addresses this gap by developing a conceptual pipe routing system for the pipelaying vessel Solitaire, operated by Allseas, and implements it within a Discrete Event Simulation model of the pipe handling system. The model is used to evaluate multiple routing strategies under different pipe supply scenarios and to determine the most effective routing strategy for the case study. The results show that a heuristic approach to sequential decision-making, framed within a model-free Markov Decision Process, can automate pipelaying operations and lay the groundwork for future optimization through reinforcement learning.
...
The adoption of new technologies on pipelaying vessels is closely linked to the demand for full automation of pipe handling operations. However, pipe handling systems are rarely addressed in research, and, to the best of our knowledge, no studies exist that describe comparable intralogistics systems with similar control structures and movement restrictions. As a result, there is no clear reference for how such automation can be realized. This study addresses this gap by developing a conceptual pipe routing system for the pipelaying vessel Solitaire, operated by Allseas, and implements it within a Discrete Event Simulation model of the pipe handling system. The model is used to evaluate multiple routing strategies under different pipe supply scenarios and to determine the most effective routing strategy for the case study. The results show that a heuristic approach to sequential decision-making, framed within a model-free Markov Decision Process, can automate pipelaying operations and lay the groundwork for future optimization through reinforcement learning.
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.
...
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.
Master thesis
(2024)
-
T.A. Nooyens, M.B. Duinkerken, A.J. van Binsbergen, R.R. Negenborn, Gijsbert Bast
Autonomous terminal tractors (ATTs) are a current development as a driver-less alternative to terminal tractors (TTs). This paper focuses on the integration of these ATTs in a rubbertired gantry (RTG)-based container terminal, and more specifically looking at how strategies for ATT-only intersections could influence the productivity of the terminal. For this study, a discrete-event simulation model of an ATT, ATT-controller and intersection management system (IMS) were designed. Within the IMS, four intersection strategies were developed: One-Vehicle-at-aTime First-Eligible-First-Serve (One-FEFS), Multiple-Vehicles-at-a-Time First-Eligible-First-Serve (Multi-FEFS), Quay-Crane Destination Priority (QC-Prio) and Priority for Delayed ATTs with QC Destination (Delay-Prio). These intersection strategies were tested in a peak-load and off-peak-load scenario, on a model of an RTG-based terminal, with configurations of varying fleet sizes. Here was found that the intersection strategies had a small influence on the QC productivity in the peak-load scenario, and a negligible influence in the off-peak-load scenario. Utilising either the Multi-FEFS or the QC-Prio strategy leaded to the highest terminal performance. In the simulation model, the ATT performed significantly worse than the existing non-autonomous TT, in every configuration.
...
Autonomous terminal tractors (ATTs) are a current development as a driver-less alternative to terminal tractors (TTs). This paper focuses on the integration of these ATTs in a rubbertired gantry (RTG)-based container terminal, and more specifically looking at how strategies for ATT-only intersections could influence the productivity of the terminal. For this study, a discrete-event simulation model of an ATT, ATT-controller and intersection management system (IMS) were designed. Within the IMS, four intersection strategies were developed: One-Vehicle-at-aTime First-Eligible-First-Serve (One-FEFS), Multiple-Vehicles-at-a-Time First-Eligible-First-Serve (Multi-FEFS), Quay-Crane Destination Priority (QC-Prio) and Priority for Delayed ATTs with QC Destination (Delay-Prio). These intersection strategies were tested in a peak-load and off-peak-load scenario, on a model of an RTG-based terminal, with configurations of varying fleet sizes. Here was found that the intersection strategies had a small influence on the QC productivity in the peak-load scenario, and a negligible influence in the off-peak-load scenario. Utilising either the Multi-FEFS or the QC-Prio strategy leaded to the highest terminal performance. In the simulation model, the ATT performed significantly worse than the existing non-autonomous TT, in every configuration.
Reducing risk exposure and financing cost by increasing delivery lead time
A Damen Shipyards case study
With fluctuating market demands, the tugboat industry confronts the challenge of adapting its supply chain to meet customer customisation needs while managing low order predictability. This study examines the shift from a Make-to-Stock (MTS) to three alternative production configurations based on the Assemble-to-Order (ATO) and Make-to-Order (MTO) configuration, focusing on the tugboat industry’s need for flexibility in response to market changes and customer-specific requirements.
The core issue addressed is the trade-off between investment risk and customer satisfaction, as the proposed configurations increase delivery lead times. To quantify the costs associated with adapting delivery lead times, a Bill of Materials and Operations (BOMO) is utilised, combining the Bill of Materials (BOM) with the production sequence (Bill of Operations, BOO).
A mathematical algorithm is developed to calculate the financial effects of the configurations based on BOMO data. The model involves a three-step process: importing part data, merging BOM, BOO, supplier, and transport data into a BOMO dataset, and performing value analysis on the BOMO data to quantify risk exposure and financing costs over time.
The study’s findings indicate that while increasing delivery lead times, the MTO configuration significantly reduces the risk exposure and financing costs. The BOMO is a strategic tool for analysing material costs and delivery lead times, providing insights into the financial implications of different production strategies. The research concludes that the MTO configuration is viable for Damen’s tugboat production, balancing risk exposure, financing costs, and delivery lead times. ...
The core issue addressed is the trade-off between investment risk and customer satisfaction, as the proposed configurations increase delivery lead times. To quantify the costs associated with adapting delivery lead times, a Bill of Materials and Operations (BOMO) is utilised, combining the Bill of Materials (BOM) with the production sequence (Bill of Operations, BOO).
A mathematical algorithm is developed to calculate the financial effects of the configurations based on BOMO data. The model involves a three-step process: importing part data, merging BOM, BOO, supplier, and transport data into a BOMO dataset, and performing value analysis on the BOMO data to quantify risk exposure and financing costs over time.
The study’s findings indicate that while increasing delivery lead times, the MTO configuration significantly reduces the risk exposure and financing costs. The BOMO is a strategic tool for analysing material costs and delivery lead times, providing insights into the financial implications of different production strategies. The research concludes that the MTO configuration is viable for Damen’s tugboat production, balancing risk exposure, financing costs, and delivery lead times. ...
With fluctuating market demands, the tugboat industry confronts the challenge of adapting its supply chain to meet customer customisation needs while managing low order predictability. This study examines the shift from a Make-to-Stock (MTS) to three alternative production configurations based on the Assemble-to-Order (ATO) and Make-to-Order (MTO) configuration, focusing on the tugboat industry’s need for flexibility in response to market changes and customer-specific requirements.
The core issue addressed is the trade-off between investment risk and customer satisfaction, as the proposed configurations increase delivery lead times. To quantify the costs associated with adapting delivery lead times, a Bill of Materials and Operations (BOMO) is utilised, combining the Bill of Materials (BOM) with the production sequence (Bill of Operations, BOO).
A mathematical algorithm is developed to calculate the financial effects of the configurations based on BOMO data. The model involves a three-step process: importing part data, merging BOM, BOO, supplier, and transport data into a BOMO dataset, and performing value analysis on the BOMO data to quantify risk exposure and financing costs over time.
The study’s findings indicate that while increasing delivery lead times, the MTO configuration significantly reduces the risk exposure and financing costs. The BOMO is a strategic tool for analysing material costs and delivery lead times, providing insights into the financial implications of different production strategies. The research concludes that the MTO configuration is viable for Damen’s tugboat production, balancing risk exposure, financing costs, and delivery lead times.
The core issue addressed is the trade-off between investment risk and customer satisfaction, as the proposed configurations increase delivery lead times. To quantify the costs associated with adapting delivery lead times, a Bill of Materials and Operations (BOMO) is utilised, combining the Bill of Materials (BOM) with the production sequence (Bill of Operations, BOO).
A mathematical algorithm is developed to calculate the financial effects of the configurations based on BOMO data. The model involves a three-step process: importing part data, merging BOM, BOO, supplier, and transport data into a BOMO dataset, and performing value analysis on the BOMO data to quantify risk exposure and financing costs over time.
The study’s findings indicate that while increasing delivery lead times, the MTO configuration significantly reduces the risk exposure and financing costs. The BOMO is a strategic tool for analysing material costs and delivery lead times, providing insights into the financial implications of different production strategies. The research concludes that the MTO configuration is viable for Damen’s tugboat production, balancing risk exposure, financing costs, and delivery lead times.
The design and optimisation of powder handling
A case study at Nestlé
This research investigates several concepts for better powder handling at the infant formula factory of Nestlé in Nunspeet. By exploring the current state of the powder handling, the problem has been clearly defined. Several concepts have been developed using the analytic hierarchy process. A literature review has been done in order to determine how the concepts can be modelled in order to compare them. The literature review highlights that one of the concepts can be modelled as a new version of the Bin Packing Problem (BPP). The concepts are then modelled and compared by using KPIs. The research shows that storing one of the powders in silos and the other powders in a rented warehouse is the best solution for Nestlé.
...
This research investigates several concepts for better powder handling at the infant formula factory of Nestlé in Nunspeet. By exploring the current state of the powder handling, the problem has been clearly defined. Several concepts have been developed using the analytic hierarchy process. A literature review has been done in order to determine how the concepts can be modelled in order to compare them. The literature review highlights that one of the concepts can be modelled as a new version of the Bin Packing Problem (BPP). The concepts are then modelled and compared by using KPIs. The research shows that storing one of the powders in silos and the other powders in a rented warehouse is the best solution for Nestlé.
In their Distribution Centre (DC) in Maasdam, FrieslandCampina (FC) uses a four-crew shift schedule to prepare all necessary orders for their clients, 24 hours of each Monday to Saturday. Their large automated warehouse is home to 10 000 pallet places, containing fresh dairy products. From here, orders are either prepared as full pallets, machine-picked layers or hand-picked "colli". In the last department especially, personnel cost is high relative to the throughput. Definitive picking deadlines are often ambiguous, posing challenges in job and personnel scheduling. The study goal is twofold. Firstly, to find out whether full knowledge of picking deadlines can contribute to a more efficient job, and so, shift schedule. Secondly, to offer insight for a trade-off between shift types to absorb workload. To reach this study goal, a Shift Minimisation Personnel Task Scheduling Problem (Krishnamoorthy et. al., 2012) and a Bin Packing Problem (Paquay et. al., 2014) were combined and tailored to fit the scheduling problem at FC's DC. In three weekly scenarios, the MILP model scheduled picking jobs in the least expensive shifts through a cost minimisation function. Two model configurations were used, one to prefer the shift between 09:00 and 17:00 (flex), and one to prefer either one of the 06:00-14:00 (morning) or the 14:00-22:00 (afternoon) shifts. Both model configurations inherently avoided the most expensive 22:00-06:00 (night) shift. Main findings include the possibility to absorb workload using the morning and afternoon shift and to avoid the night shift. Additionally, it was confirmed that insight in picking deadlines can contribute to an efficient personnel schedule a great deal.
...
In their Distribution Centre (DC) in Maasdam, FrieslandCampina (FC) uses a four-crew shift schedule to prepare all necessary orders for their clients, 24 hours of each Monday to Saturday. Their large automated warehouse is home to 10 000 pallet places, containing fresh dairy products. From here, orders are either prepared as full pallets, machine-picked layers or hand-picked "colli". In the last department especially, personnel cost is high relative to the throughput. Definitive picking deadlines are often ambiguous, posing challenges in job and personnel scheduling. The study goal is twofold. Firstly, to find out whether full knowledge of picking deadlines can contribute to a more efficient job, and so, shift schedule. Secondly, to offer insight for a trade-off between shift types to absorb workload. To reach this study goal, a Shift Minimisation Personnel Task Scheduling Problem (Krishnamoorthy et. al., 2012) and a Bin Packing Problem (Paquay et. al., 2014) were combined and tailored to fit the scheduling problem at FC's DC. In three weekly scenarios, the MILP model scheduled picking jobs in the least expensive shifts through a cost minimisation function. Two model configurations were used, one to prefer the shift between 09:00 and 17:00 (flex), and one to prefer either one of the 06:00-14:00 (morning) or the 14:00-22:00 (afternoon) shifts. Both model configurations inherently avoided the most expensive 22:00-06:00 (night) shift. Main findings include the possibility to absorb workload using the morning and afternoon shift and to avoid the night shift. Additionally, it was confirmed that insight in picking deadlines can contribute to an efficient personnel schedule a great deal.
Optimizing offshore wind farm transportation and installation strategies including manufacturing ports
Using a rolling horizon simulation model
The installation rate of offshore wind energy has to be quadrupled by 2030 to meet the green climate ambitions of European countries, but this growth is hindered by logistical challenges. Therefore, this study explores how two transportation and installation strategies, shuttling and feedering, affect the installation rate with the inclusion of manufacturing ports. Shuttling is where the installation vessel collects components itself at a port, and feedering is where the installation vessel remains offshore and gets supplied directly via feeder vessels. This is a novel approach as feedering and manufacturing ports are often not considered and production rates at manufacturing ports have not been considered at all. A rolling horizon simulation model is developed, which uses a Markov simulation model for weather forecasting, with a 72.92% forecast accuracy over two weeks, and a greedy algorithm for transportation and installation optimization. Results indicate that accurate initial buffer calculations, depending on the production rate at the manufacturing ports and project-dependent characteristics, can increase the installation rate significantly for either strategy. Shuttling becomes more efficient than feedering if the distance between the manufacturing ports and the offshore wind farm is too large or the size of the feeder vessels is too small. Feedering is more efficient in all other circumstances and, on average, results in a 9.2% higher installation rate and reduces project duration by 29 days compared to shuttling.
...
The installation rate of offshore wind energy has to be quadrupled by 2030 to meet the green climate ambitions of European countries, but this growth is hindered by logistical challenges. Therefore, this study explores how two transportation and installation strategies, shuttling and feedering, affect the installation rate with the inclusion of manufacturing ports. Shuttling is where the installation vessel collects components itself at a port, and feedering is where the installation vessel remains offshore and gets supplied directly via feeder vessels. This is a novel approach as feedering and manufacturing ports are often not considered and production rates at manufacturing ports have not been considered at all. A rolling horizon simulation model is developed, which uses a Markov simulation model for weather forecasting, with a 72.92% forecast accuracy over two weeks, and a greedy algorithm for transportation and installation optimization. Results indicate that accurate initial buffer calculations, depending on the production rate at the manufacturing ports and project-dependent characteristics, can increase the installation rate significantly for either strategy. Shuttling becomes more efficient than feedering if the distance between the manufacturing ports and the offshore wind farm is too large or the size of the feeder vessels is too small. Feedering is more efficient in all other circumstances and, on average, results in a 9.2% higher installation rate and reduces project duration by 29 days compared to shuttling.
Creating a continuous outbound flow at the flower auction
A case study at Royal FloraHolland Naaldwijk
Master thesis
(2024)
-
A. Gerritsen, J.M. Vleugel, M.B. Duinkerken, R.R. Negenborn, Ingrid Abels, Oscar Binneveld
This paper investigates how two sequential sub-processes at a flower auction can be well aligned to efficiently execute the overall auction process. Existing literature mainly focuses on warehouses without perishable goods and warehouses where all orders are known before the outbound process is started. However, at a flower auction, the gathering of goods and distribution takes place while the auction is still ongoing. In addition, flowers are vulnerable goods that must be handled with care.
During a case study at Royal FloraHolland Naaldwijk, the current process of order picking and in-house delivery is investigated to find the main strengths and bottlenecks. This is done physically and with data. From this analysis,
it has been found that the main issues are the spread and share of waiting times in the in-house delivery process and the output of the order-picking process that is too low. To improve the overall process based on the found issues, a calculation model has been built in Python to test possible improvements. It has been found that implementing limited waiting times and other alterations to increase efficiency results in a more reliable and better predictable
process that can be executed with approximately the same number of work hours or slightly more than in the current situation. ...
During a case study at Royal FloraHolland Naaldwijk, the current process of order picking and in-house delivery is investigated to find the main strengths and bottlenecks. This is done physically and with data. From this analysis,
it has been found that the main issues are the spread and share of waiting times in the in-house delivery process and the output of the order-picking process that is too low. To improve the overall process based on the found issues, a calculation model has been built in Python to test possible improvements. It has been found that implementing limited waiting times and other alterations to increase efficiency results in a more reliable and better predictable
process that can be executed with approximately the same number of work hours or slightly more than in the current situation. ...
This paper investigates how two sequential sub-processes at a flower auction can be well aligned to efficiently execute the overall auction process. Existing literature mainly focuses on warehouses without perishable goods and warehouses where all orders are known before the outbound process is started. However, at a flower auction, the gathering of goods and distribution takes place while the auction is still ongoing. In addition, flowers are vulnerable goods that must be handled with care.
During a case study at Royal FloraHolland Naaldwijk, the current process of order picking and in-house delivery is investigated to find the main strengths and bottlenecks. This is done physically and with data. From this analysis,
it has been found that the main issues are the spread and share of waiting times in the in-house delivery process and the output of the order-picking process that is too low. To improve the overall process based on the found issues, a calculation model has been built in Python to test possible improvements. It has been found that implementing limited waiting times and other alterations to increase efficiency results in a more reliable and better predictable
process that can be executed with approximately the same number of work hours or slightly more than in the current situation.
During a case study at Royal FloraHolland Naaldwijk, the current process of order picking and in-house delivery is investigated to find the main strengths and bottlenecks. This is done physically and with data. From this analysis,
it has been found that the main issues are the spread and share of waiting times in the in-house delivery process and the output of the order-picking process that is too low. To improve the overall process based on the found issues, a calculation model has been built in Python to test possible improvements. It has been found that implementing limited waiting times and other alterations to increase efficiency results in a more reliable and better predictable
process that can be executed with approximately the same number of work hours or slightly more than in the current situation.
Building beyond the booth
Improving logistics for the construction of trade fairs
Trade fairs bring together suppliers from specific industries or fields, offering them a valuable platform to showcase their products, gather information on competitors, and find potential partners. During construction, materials that are not yet in use are often stored in pathways. When multiple stands are under construction simultaneously, pathways can get increasingly congested, resulting in stands becoming inaccessible, safety risks and delays. Despite the importance of managing these issues, no scheduling method has been developed to address these challenges to date. To fill this gap, this research introduces a Mixed Integer Linear Programming (MILP) model designed to improve the construction scheduling process at trade fairs by incorporating the predicted impact of scheduled workspace availability on delays as a factor in scheduling decisions. A case study at RAI Amsterdam was performed to collect data and validate the model. The improved schedules proposed by the MILP model are estimated to reduce average aisle material storage by 3.1%, variance in material storage density by 38.9%, and workspace interferences by 18.0%. When accessibility constraints were relaxed, even greater gains were achieved, with reductions up to 10.7% in material storage and 64.6% in interferences. Simulation runs with varying input variables showed that the degree improvement varied by hall layout, stand density, and available construction time, with island layouts and additional setup time yielding the best results. The most recurring and clear scheduling strategy applied by the model was letting stands furthest away
from the accessible safety paths start first, followed sequentially by closer stands. Overall, the model provides a practical scheduling approach to reduce congestion and enhance safety, offering a useful tool for trade fair organizers. ...
from the accessible safety paths start first, followed sequentially by closer stands. Overall, the model provides a practical scheduling approach to reduce congestion and enhance safety, offering a useful tool for trade fair organizers. ...
Trade fairs bring together suppliers from specific industries or fields, offering them a valuable platform to showcase their products, gather information on competitors, and find potential partners. During construction, materials that are not yet in use are often stored in pathways. When multiple stands are under construction simultaneously, pathways can get increasingly congested, resulting in stands becoming inaccessible, safety risks and delays. Despite the importance of managing these issues, no scheduling method has been developed to address these challenges to date. To fill this gap, this research introduces a Mixed Integer Linear Programming (MILP) model designed to improve the construction scheduling process at trade fairs by incorporating the predicted impact of scheduled workspace availability on delays as a factor in scheduling decisions. A case study at RAI Amsterdam was performed to collect data and validate the model. The improved schedules proposed by the MILP model are estimated to reduce average aisle material storage by 3.1%, variance in material storage density by 38.9%, and workspace interferences by 18.0%. When accessibility constraints were relaxed, even greater gains were achieved, with reductions up to 10.7% in material storage and 64.6% in interferences. Simulation runs with varying input variables showed that the degree improvement varied by hall layout, stand density, and available construction time, with island layouts and additional setup time yielding the best results. The most recurring and clear scheduling strategy applied by the model was letting stands furthest away
from the accessible safety paths start first, followed sequentially by closer stands. Overall, the model provides a practical scheduling approach to reduce congestion and enhance safety, offering a useful tool for trade fair organizers.
from the accessible safety paths start first, followed sequentially by closer stands. Overall, the model provides a practical scheduling approach to reduce congestion and enhance safety, offering a useful tool for trade fair organizers.
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.
...
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.
Design of an In-Plant Transport System: A Case Study at Tata Steel
Discrete-Event Simulation of the System Design with Automated Vehicles for a Steel Coil Manufacturing Plant
Master thesis
(2024)
-
J.J. Linders, M.B. Duinkerken, A.J. van Binsbergen, E. Veenboer, A. Napoleone
Transport and storage operations in a steel coil manufacturing industry make up a large part of a factory’s operating costs. With the advent of automated forklifts, unmanned transport and storage operations are becoming increasingly viable. Automated forklifts have the potential to lower operating costs, reduce the required number of employees, minimize transport damages and eliminate over-processing. Nonetheless, transitioning from manual forklifts to a fully operational automated transport and storage system requires addressing a range of complex decisions. These include the flow path layout, fleet sizing, vehicle dispatching, storage location assignment, and other relevant subjects.
This thesis presents an integrated approach to how the flow path layout design influences the material flow effectiveness, incorporating vehicle scheduling and storage location assignment policies. Based on a real-world case study, analyzed through Discrete-Event Simulation (DES) software, this study addresses the following research question: What in-plant system design facilitates effective material flow by implementing automated transport in a steel coil manufacturing plant?
First, applicable literature is investigated, thereafter, the system is analysed, and design alternatives are generated. The design alternatives vary in the use of manual and automated forklifts, and the flow path layout considers both conventional and zone-based flow approaches. The experiments test the influence of dispatching policies and fleet sizing on all alternatives. Furthermore, battery management, idle-vehicle positioning, unit-load selection and case-specific system constraints are integrated. The storage location assignment is based on the order identification number and the fill level of the storage parks.
By capturing the dynamic and variable nature of a stochastic production system, the DES evaluates the impact of different configurations on performance, costs, and other performance indicators. The cost-performance relations are plotted, resulting in a Pareto front consisting of a set of non-dominated system design configurations. A preferred automation alternative is selected from this set. Conclusions regarding the most effective transport system design and the integrated system design process are drawn.
...
This thesis presents an integrated approach to how the flow path layout design influences the material flow effectiveness, incorporating vehicle scheduling and storage location assignment policies. Based on a real-world case study, analyzed through Discrete-Event Simulation (DES) software, this study addresses the following research question: What in-plant system design facilitates effective material flow by implementing automated transport in a steel coil manufacturing plant?
First, applicable literature is investigated, thereafter, the system is analysed, and design alternatives are generated. The design alternatives vary in the use of manual and automated forklifts, and the flow path layout considers both conventional and zone-based flow approaches. The experiments test the influence of dispatching policies and fleet sizing on all alternatives. Furthermore, battery management, idle-vehicle positioning, unit-load selection and case-specific system constraints are integrated. The storage location assignment is based on the order identification number and the fill level of the storage parks.
By capturing the dynamic and variable nature of a stochastic production system, the DES evaluates the impact of different configurations on performance, costs, and other performance indicators. The cost-performance relations are plotted, resulting in a Pareto front consisting of a set of non-dominated system design configurations. A preferred automation alternative is selected from this set. Conclusions regarding the most effective transport system design and the integrated system design process are drawn.
...
Transport and storage operations in a steel coil manufacturing industry make up a large part of a factory’s operating costs. With the advent of automated forklifts, unmanned transport and storage operations are becoming increasingly viable. Automated forklifts have the potential to lower operating costs, reduce the required number of employees, minimize transport damages and eliminate over-processing. Nonetheless, transitioning from manual forklifts to a fully operational automated transport and storage system requires addressing a range of complex decisions. These include the flow path layout, fleet sizing, vehicle dispatching, storage location assignment, and other relevant subjects.
This thesis presents an integrated approach to how the flow path layout design influences the material flow effectiveness, incorporating vehicle scheduling and storage location assignment policies. Based on a real-world case study, analyzed through Discrete-Event Simulation (DES) software, this study addresses the following research question: What in-plant system design facilitates effective material flow by implementing automated transport in a steel coil manufacturing plant?
First, applicable literature is investigated, thereafter, the system is analysed, and design alternatives are generated. The design alternatives vary in the use of manual and automated forklifts, and the flow path layout considers both conventional and zone-based flow approaches. The experiments test the influence of dispatching policies and fleet sizing on all alternatives. Furthermore, battery management, idle-vehicle positioning, unit-load selection and case-specific system constraints are integrated. The storage location assignment is based on the order identification number and the fill level of the storage parks.
By capturing the dynamic and variable nature of a stochastic production system, the DES evaluates the impact of different configurations on performance, costs, and other performance indicators. The cost-performance relations are plotted, resulting in a Pareto front consisting of a set of non-dominated system design configurations. A preferred automation alternative is selected from this set. Conclusions regarding the most effective transport system design and the integrated system design process are drawn.
This thesis presents an integrated approach to how the flow path layout design influences the material flow effectiveness, incorporating vehicle scheduling and storage location assignment policies. Based on a real-world case study, analyzed through Discrete-Event Simulation (DES) software, this study addresses the following research question: What in-plant system design facilitates effective material flow by implementing automated transport in a steel coil manufacturing plant?
First, applicable literature is investigated, thereafter, the system is analysed, and design alternatives are generated. The design alternatives vary in the use of manual and automated forklifts, and the flow path layout considers both conventional and zone-based flow approaches. The experiments test the influence of dispatching policies and fleet sizing on all alternatives. Furthermore, battery management, idle-vehicle positioning, unit-load selection and case-specific system constraints are integrated. The storage location assignment is based on the order identification number and the fill level of the storage parks.
By capturing the dynamic and variable nature of a stochastic production system, the DES evaluates the impact of different configurations on performance, costs, and other performance indicators. The cost-performance relations are plotted, resulting in a Pareto front consisting of a set of non-dominated system design configurations. A preferred automation alternative is selected from this set. Conclusions regarding the most effective transport system design and the integrated system design process are drawn.
Time-window based Truck Appointment System with Adaptive Slot management and Real-Time Truck Information
A case study for the loading operations in a Chemical Plant
Long truck queues and congestion around terminals is a common sight, however they come with many negative externalities for all the stakeholders involved. Truck Appointment System (TAS) is the most commonly used system to face these problems, but it still has some drawbacks and limitations. Consequently, in this research an extension of the typical TAS is proposed in order to improve its performance. The main components of this system are the use of truck dependent time-windows, the utilization of real-time information and the adaptive trucks rescheduling model. The duration of the arrival time-windows is longer than the actual service times, allowing overlap between time-windows. Thus, the actual service sequence might be different from the reserved one. To determine the actual loading sequence an Optimization model is developed, which is run periodically while utilizing real-time truck information. A chemical plant is used as a case study in this research. The performance of the proposed TAS is assessed with the use of a Simulation model. The outcomes of this research suggest that the a less strict TAS can significantly improve the system’s performance, especially trucks’ waiting times. Also, the system’s resilience against disruptions and the plant’s environmental footprint are improved, while queues are reduced.
...
Long truck queues and congestion around terminals is a common sight, however they come with many negative externalities for all the stakeholders involved. Truck Appointment System (TAS) is the most commonly used system to face these problems, but it still has some drawbacks and limitations. Consequently, in this research an extension of the typical TAS is proposed in order to improve its performance. The main components of this system are the use of truck dependent time-windows, the utilization of real-time information and the adaptive trucks rescheduling model. The duration of the arrival time-windows is longer than the actual service times, allowing overlap between time-windows. Thus, the actual service sequence might be different from the reserved one. To determine the actual loading sequence an Optimization model is developed, which is run periodically while utilizing real-time truck information. A chemical plant is used as a case study in this research. The performance of the proposed TAS is assessed with the use of a Simulation model. The outcomes of this research suggest that the a less strict TAS can significantly improve the system’s performance, especially trucks’ waiting times. Also, the system’s resilience against disruptions and the plant’s environmental footprint are improved, while queues are reduced.