This thesis presents a novel framework for solving the Multi-Skill Resource-Constrained Multi-Modal Project Scheduling Problem with maximum time lags, addressing the challenges of scalability, deadline adherence, and uncertainty in job durations. The research is conducted through a case study with the maintenance department of a large European airline, using real-life maintenance scheduling data. To improve scalability, the framework integrates batching techniques that segment the scheduling horizon and a priority-rule-based heuristic to hot start the solver, significantly reducing computational runtimes for large problem instances. A range of objective functions, including single and multi-objective formulations, are explored to evaluate their impact on scheduling performance. The results demonstrate
that multi-objective formulations provide the best balance between throughput and deadline adherence and consistently outperform a priority-based heuristic. A clear trade-off is observed between optimizing for maximum tardiness and average tardiness, where minimizing maximum tardiness improves deadline adherence at the cost of lower throughput, while minimizing average tardiness has a more consistent throughput but allows slightly more deadline misses. To address job duration uncertainty, adaptive buffering strategies based on historical job performance are introduced and shown to outperform static buffers by tailoring slack times to individual job characteristics. In the examined case study, the combination of an adaptive buffering strategy with a multi-objective function combining makespan and
average weighted tardiness offers the most effective trade-off between robustness and efficiency. Overall, the framework proves to be scalable, adaptable, and well-suited to real-world scheduling environments with high variability and complex constraints.