JK
J.G.P. Klein Kranenbarg
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Proactive–Reactive Rescheduling for RCMPSP/max using Exact Methods
Optimisation of disruption-prone schedules of an aircraft engine MRO
Master thesis
(2025)
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J.G.P. Klein Kranenbarg, N. Yorke-Smith, B.F. Santos, M.J. Ribeiro, Serge Wieleman
Over the last few decades, the number of people flying has grown significantly. Now, a few years since the pandemic ended, air traffic is back to the level it was before the pandemic, and is expected to grow even faster than before the pandemic.
Most aircraft engine repairs are organised as Maintenance, Repair and Overhaul (MRO). Scheduling projects for an aircraft engine MRO presents several key challenges. Like the uncertainty of processes (especially when depending on external parties). At the time of writing, the supply chain of these MROs remains heavily disrupted, resulting in numerous parts being (near) unobtainable. And challenges involving personnel. Even if all other resources are ready and available, this means nothing if there are not enough personnel with the required skills available.
The goal of this research is to develop a model that uses exact methods to perform proactive rescheduling of the aeroplane engine MRO process. This model focuses on finding a solution that balances between: (1) Quality, (2) Deviation impact minimisation and (3) Robustness. Quality refers to metrics such as project turnaround time (TAT), workload performance, and minimisation of unused workforces. Robustness metrics focus on aspects such as minimising the number of excessively scheduled resources in a solution or indicating how well a solution responds to disruptions in the input data. Finally, deviation impact minimisation focuses on minimising the impact reschedules have on the actual workflow of the MRO. ...
Most aircraft engine repairs are organised as Maintenance, Repair and Overhaul (MRO). Scheduling projects for an aircraft engine MRO presents several key challenges. Like the uncertainty of processes (especially when depending on external parties). At the time of writing, the supply chain of these MROs remains heavily disrupted, resulting in numerous parts being (near) unobtainable. And challenges involving personnel. Even if all other resources are ready and available, this means nothing if there are not enough personnel with the required skills available.
The goal of this research is to develop a model that uses exact methods to perform proactive rescheduling of the aeroplane engine MRO process. This model focuses on finding a solution that balances between: (1) Quality, (2) Deviation impact minimisation and (3) Robustness. Quality refers to metrics such as project turnaround time (TAT), workload performance, and minimisation of unused workforces. Robustness metrics focus on aspects such as minimising the number of excessively scheduled resources in a solution or indicating how well a solution responds to disruptions in the input data. Finally, deviation impact minimisation focuses on minimising the impact reschedules have on the actual workflow of the MRO. ...
Over the last few decades, the number of people flying has grown significantly. Now, a few years since the pandemic ended, air traffic is back to the level it was before the pandemic, and is expected to grow even faster than before the pandemic.
Most aircraft engine repairs are organised as Maintenance, Repair and Overhaul (MRO). Scheduling projects for an aircraft engine MRO presents several key challenges. Like the uncertainty of processes (especially when depending on external parties). At the time of writing, the supply chain of these MROs remains heavily disrupted, resulting in numerous parts being (near) unobtainable. And challenges involving personnel. Even if all other resources are ready and available, this means nothing if there are not enough personnel with the required skills available.
The goal of this research is to develop a model that uses exact methods to perform proactive rescheduling of the aeroplane engine MRO process. This model focuses on finding a solution that balances between: (1) Quality, (2) Deviation impact minimisation and (3) Robustness. Quality refers to metrics such as project turnaround time (TAT), workload performance, and minimisation of unused workforces. Robustness metrics focus on aspects such as minimising the number of excessively scheduled resources in a solution or indicating how well a solution responds to disruptions in the input data. Finally, deviation impact minimisation focuses on minimising the impact reschedules have on the actual workflow of the MRO.
Most aircraft engine repairs are organised as Maintenance, Repair and Overhaul (MRO). Scheduling projects for an aircraft engine MRO presents several key challenges. Like the uncertainty of processes (especially when depending on external parties). At the time of writing, the supply chain of these MROs remains heavily disrupted, resulting in numerous parts being (near) unobtainable. And challenges involving personnel. Even if all other resources are ready and available, this means nothing if there are not enough personnel with the required skills available.
The goal of this research is to develop a model that uses exact methods to perform proactive rescheduling of the aeroplane engine MRO process. This model focuses on finding a solution that balances between: (1) Quality, (2) Deviation impact minimisation and (3) Robustness. Quality refers to metrics such as project turnaround time (TAT), workload performance, and minimisation of unused workforces. Robustness metrics focus on aspects such as minimising the number of excessively scheduled resources in a solution or indicating how well a solution responds to disruptions in the input data. Finally, deviation impact minimisation focuses on minimising the impact reschedules have on the actual workflow of the MRO.
Bachelor thesis
(2020)
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J.L. Buijnsters, D. Hofman, J.G.P. Klein Kranenbarg, C. El Moussaoui, K. Zheng, B.H.M. Gerritsen, K.F. Chan, H. Wang, O.W. Visser
ScenWise is an innovative company that specializes in data science revolving around traffic management. ScenWise strives to use the newest and best technologies and practices when it comes to web applications, data science and traffic management. The reason for this is that they provide tools to analyse and visualise a variety of situations that occur in traffic management. One such tool is SmartRoads 1.0, which allows users to analyse traffic data and situations via a web application. Unfortunately SmartRoads 1.0 does not perform as desired. Additionally, ScenWise itself has the problem of not being able to integrate previously made products by student groups into their own existing products. During the research aimed to resolve these problems another issue arose; the software development life cycle of ScenWise is very lacking. Research on the SmartRoads 1.0 performance problem showed that the bottleneck of its performance is due to the front-end. The outdated SmartRoads 1.0 front-end was thus replaced with a new and better SmartRoads 2.0 front-end. The integration problem and development life cycle problem are both addressed in the Longterm evolution (LTE) design found in appendix I. This LTE design contains the architecture migration plan. This plan will transform the current software architecture to a Service-oriented architecture (SOA) providing a solution for the current integration problems. A result of the first steps of this architecture migration plan is the Application Programming Interface (API) Gateway, which has been implemented in the aforementioned SmartRoads 2.0. Next to the migration plan, guidelines for ScenWise to improve their software development life cycle are elaborated in the LTE design. In this report the identified problems, their solutions and executions are explained, discussed and evaluated.
...
ScenWise is an innovative company that specializes in data science revolving around traffic management. ScenWise strives to use the newest and best technologies and practices when it comes to web applications, data science and traffic management. The reason for this is that they provide tools to analyse and visualise a variety of situations that occur in traffic management. One such tool is SmartRoads 1.0, which allows users to analyse traffic data and situations via a web application. Unfortunately SmartRoads 1.0 does not perform as desired. Additionally, ScenWise itself has the problem of not being able to integrate previously made products by student groups into their own existing products. During the research aimed to resolve these problems another issue arose; the software development life cycle of ScenWise is very lacking. Research on the SmartRoads 1.0 performance problem showed that the bottleneck of its performance is due to the front-end. The outdated SmartRoads 1.0 front-end was thus replaced with a new and better SmartRoads 2.0 front-end. The integration problem and development life cycle problem are both addressed in the Longterm evolution (LTE) design found in appendix I. This LTE design contains the architecture migration plan. This plan will transform the current software architecture to a Service-oriented architecture (SOA) providing a solution for the current integration problems. A result of the first steps of this architecture migration plan is the Application Programming Interface (API) Gateway, which has been implemented in the aforementioned SmartRoads 2.0. Next to the migration plan, guidelines for ScenWise to improve their software development life cycle are elaborated in the LTE design. In this report the identified problems, their solutions and executions are explained, discussed and evaluated.