MaRS - Maintenance Resource Scheduler: High fidelity maintenance resource scheduling for aviation

Kurzfassung

Maintenance shops for aircraft face many challenges, because they are highly regulated, must follow precise documentation requirements, and operate on tight schedules. For planning, they must combine traditional production planning with high uncertainties because of unknown faults. The decrease of available skilled labor puts additional strain on shops, which make more accurate and tighter scheduling necessary. This thesis uses a mixed-methods approach consisting of qualitative interviews, existing scheduling algorithms and newly developed data models and optimization algorithms to schedule durable maintenance resources for aircraft. It extracts fundamental constraints and current industry practices from expert interviews, which are then used to model a complete set of resources, requirements and maintenance steps. The steps are analyzed using the critical path method and then optimized with different algorithms, like heuristics and mixed integer linear programming. They are run and evaluated based on example data sets. The proposed algorithms can optimize for short term scheduling and produce shift schedules based on the available workers, but are also capable planning tools to estimate resource requirements in the future based on maintenance load predictions.