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E. Çoksayar
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In most systems, delays may severely degrade performance. That is why they are monitored, controlled, and minimised as much as possible. However, doing so in complex industrial systems is particularly challenging, due to their intrinsically non-linear dynamics. This paper proposes an approach to model and analyse delay in such complex systems, seen as an instance of system of systems (SoS), by building on developments in Bayesian Networks (BN). The proposed BN considers the availability of different subsystems as an important cause of system and SoS delays. By conditioning the availability of systems to operational choices, such as maintenance strategies, it becomes possible to analyse their impact on the total SoS delay. A container terminal is used as an example case to implement and test the proposed BN approach. Multiple simulations were generated based on a combination of different external factors (e.g. weather conditions), as well as internal choices (e.g. maintenance strategies). Compared to other simulation methods, like discrete event simulation, this approach is less data-intensive and computationally demanding, allowing for the rapid evaluation of multiple operational choices and their impact on the system and SoS delays. For instance, we observed that an expected reduction of up to 13% in the container terminal delay could be achieved by implementing a weekly preventive maintenance policy, due to an improvement in the availability of all systems.
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In most systems, delays may severely degrade performance. That is why they are monitored, controlled, and minimised as much as possible. However, doing so in complex industrial systems is particularly challenging, due to their intrinsically non-linear dynamics. This paper proposes an approach to model and analyse delay in such complex systems, seen as an instance of system of systems (SoS), by building on developments in Bayesian Networks (BN). The proposed BN considers the availability of different subsystems as an important cause of system and SoS delays. By conditioning the availability of systems to operational choices, such as maintenance strategies, it becomes possible to analyse their impact on the total SoS delay. A container terminal is used as an example case to implement and test the proposed BN approach. Multiple simulations were generated based on a combination of different external factors (e.g. weather conditions), as well as internal choices (e.g. maintenance strategies). Compared to other simulation methods, like discrete event simulation, this approach is less data-intensive and computationally demanding, allowing for the rapid evaluation of multiple operational choices and their impact on the system and SoS delays. For instance, we observed that an expected reduction of up to 13% in the container terminal delay could be achieved by implementing a weekly preventive maintenance policy, due to an improvement in the availability of all systems.
This thesis presents a system-level reliability modelling framework for analyzing how failures, maintenance activities, and operational disruptions spread through interconnected subsystems in container terminal operations. The research combines reliability analysis with probabilistic reasoning to provide a clear understanding of how technical, human, and environmental factors affect delays and system performance. This connection is established by translating reliability states—such as equipment health, availability, and maintenance effectiveness—into probabilistic delay outcomes, allowing the model to quantify how reliability losses lead to operational delays.
The framework integrates Fault Tree Analysis (FTA) and Bayesian Networks (BNs) to capture both causal and probabilistic relationships within the terminal system. FTA decomposes the top event— total operational delay—into its contributing factors, providing the structural foundation for the BN. The initial BN structure captures subsystem interactions through nodes representing equipment failure, availability, efficiency, and environmental conditions. Each node describes a key aspect of terminal performance. Equipment failure indicates the operational state of critical assets and is modelled with a Weibull distribution. Availability represents the percentage of time each subsystem could work. Efficiency reflects performance under different external or internal constraints. Environmental factors, yard storage fullness, and terminal busyness acted as external drivers that influenced subsystem efficiency and delay propagation.
The BN model was further expanded to include maintenance and operator availability as new influencing factors. Both preventive and corrective maintenance were considered within the model. The maintenance effectiveness was determined using Weibull-based reliability parameters. The scale and shape parameters were adjusted through maintenance effect multipliers to account for imperfect repair conditions. This method enabled the BN to show how preventive maintenance improves equipment availability. Operator availability was also considered to account for human-related variability, showing how workforce presence impacts subsystem operability and overall delays.
The analysis of the BN used forward inference and sensitivity testing. The results indicate that disruptions in one subsystem can spread through the terminal, causing cumulative delays and underscoring the strong interconnections between quay cranes, yard cranes, and horizontal transport. The maintenance analysis revealed that by implementing preventive maintenance strategies, equipment availability could be increased. Operator availability affected total delay mainly during severe disruptions like strikes, while daily variations had a limited impact.
This developed framework combines reliability modelling and operational performance analysis in one structure. It offers a clear way to study how maintenance, operator availability, and environmental conditions influence reliability and delay in terminal operations. While the framework was created for container terminals, the same approach could be adapted to other connected systems where equipment, people, and external conditions interact to affect overall performance. ...
The framework integrates Fault Tree Analysis (FTA) and Bayesian Networks (BNs) to capture both causal and probabilistic relationships within the terminal system. FTA decomposes the top event— total operational delay—into its contributing factors, providing the structural foundation for the BN. The initial BN structure captures subsystem interactions through nodes representing equipment failure, availability, efficiency, and environmental conditions. Each node describes a key aspect of terminal performance. Equipment failure indicates the operational state of critical assets and is modelled with a Weibull distribution. Availability represents the percentage of time each subsystem could work. Efficiency reflects performance under different external or internal constraints. Environmental factors, yard storage fullness, and terminal busyness acted as external drivers that influenced subsystem efficiency and delay propagation.
The BN model was further expanded to include maintenance and operator availability as new influencing factors. Both preventive and corrective maintenance were considered within the model. The maintenance effectiveness was determined using Weibull-based reliability parameters. The scale and shape parameters were adjusted through maintenance effect multipliers to account for imperfect repair conditions. This method enabled the BN to show how preventive maintenance improves equipment availability. Operator availability was also considered to account for human-related variability, showing how workforce presence impacts subsystem operability and overall delays.
The analysis of the BN used forward inference and sensitivity testing. The results indicate that disruptions in one subsystem can spread through the terminal, causing cumulative delays and underscoring the strong interconnections between quay cranes, yard cranes, and horizontal transport. The maintenance analysis revealed that by implementing preventive maintenance strategies, equipment availability could be increased. Operator availability affected total delay mainly during severe disruptions like strikes, while daily variations had a limited impact.
This developed framework combines reliability modelling and operational performance analysis in one structure. It offers a clear way to study how maintenance, operator availability, and environmental conditions influence reliability and delay in terminal operations. While the framework was created for container terminals, the same approach could be adapted to other connected systems where equipment, people, and external conditions interact to affect overall performance. ...
This thesis presents a system-level reliability modelling framework for analyzing how failures, maintenance activities, and operational disruptions spread through interconnected subsystems in container terminal operations. The research combines reliability analysis with probabilistic reasoning to provide a clear understanding of how technical, human, and environmental factors affect delays and system performance. This connection is established by translating reliability states—such as equipment health, availability, and maintenance effectiveness—into probabilistic delay outcomes, allowing the model to quantify how reliability losses lead to operational delays.
The framework integrates Fault Tree Analysis (FTA) and Bayesian Networks (BNs) to capture both causal and probabilistic relationships within the terminal system. FTA decomposes the top event— total operational delay—into its contributing factors, providing the structural foundation for the BN. The initial BN structure captures subsystem interactions through nodes representing equipment failure, availability, efficiency, and environmental conditions. Each node describes a key aspect of terminal performance. Equipment failure indicates the operational state of critical assets and is modelled with a Weibull distribution. Availability represents the percentage of time each subsystem could work. Efficiency reflects performance under different external or internal constraints. Environmental factors, yard storage fullness, and terminal busyness acted as external drivers that influenced subsystem efficiency and delay propagation.
The BN model was further expanded to include maintenance and operator availability as new influencing factors. Both preventive and corrective maintenance were considered within the model. The maintenance effectiveness was determined using Weibull-based reliability parameters. The scale and shape parameters were adjusted through maintenance effect multipliers to account for imperfect repair conditions. This method enabled the BN to show how preventive maintenance improves equipment availability. Operator availability was also considered to account for human-related variability, showing how workforce presence impacts subsystem operability and overall delays.
The analysis of the BN used forward inference and sensitivity testing. The results indicate that disruptions in one subsystem can spread through the terminal, causing cumulative delays and underscoring the strong interconnections between quay cranes, yard cranes, and horizontal transport. The maintenance analysis revealed that by implementing preventive maintenance strategies, equipment availability could be increased. Operator availability affected total delay mainly during severe disruptions like strikes, while daily variations had a limited impact.
This developed framework combines reliability modelling and operational performance analysis in one structure. It offers a clear way to study how maintenance, operator availability, and environmental conditions influence reliability and delay in terminal operations. While the framework was created for container terminals, the same approach could be adapted to other connected systems where equipment, people, and external conditions interact to affect overall performance.
The framework integrates Fault Tree Analysis (FTA) and Bayesian Networks (BNs) to capture both causal and probabilistic relationships within the terminal system. FTA decomposes the top event— total operational delay—into its contributing factors, providing the structural foundation for the BN. The initial BN structure captures subsystem interactions through nodes representing equipment failure, availability, efficiency, and environmental conditions. Each node describes a key aspect of terminal performance. Equipment failure indicates the operational state of critical assets and is modelled with a Weibull distribution. Availability represents the percentage of time each subsystem could work. Efficiency reflects performance under different external or internal constraints. Environmental factors, yard storage fullness, and terminal busyness acted as external drivers that influenced subsystem efficiency and delay propagation.
The BN model was further expanded to include maintenance and operator availability as new influencing factors. Both preventive and corrective maintenance were considered within the model. The maintenance effectiveness was determined using Weibull-based reliability parameters. The scale and shape parameters were adjusted through maintenance effect multipliers to account for imperfect repair conditions. This method enabled the BN to show how preventive maintenance improves equipment availability. Operator availability was also considered to account for human-related variability, showing how workforce presence impacts subsystem operability and overall delays.
The analysis of the BN used forward inference and sensitivity testing. The results indicate that disruptions in one subsystem can spread through the terminal, causing cumulative delays and underscoring the strong interconnections between quay cranes, yard cranes, and horizontal transport. The maintenance analysis revealed that by implementing preventive maintenance strategies, equipment availability could be increased. Operator availability affected total delay mainly during severe disruptions like strikes, while daily variations had a limited impact.
This developed framework combines reliability modelling and operational performance analysis in one structure. It offers a clear way to study how maintenance, operator availability, and environmental conditions influence reliability and delay in terminal operations. While the framework was created for container terminals, the same approach could be adapted to other connected systems where equipment, people, and external conditions interact to affect overall performance.