Abstract
Uncertainty is pervasive in modern manufacturing settings. In order to cope with unexpected events, scheduling decisions are commonly taken resorting to dispatching rules, which are reactive in nature. However, rule performance varies according to shop utilisation and due date allowance, which often change in dynamic real-world job shops. Therefore, this paper explores systems that select dispatching rules as conditions change over time, namely periodic and real-time dispatching rule selection systems, which are based on supervised learning and reinforcement learning algorithms, respectively. These types of systems have been proposed in the past but have been further improved in this work by carefully selecting the most relevant state features and dispatching rules. Moreover, by testing both approaches on the same instances, it was possible to compare them and determine the most advantageous one. After the tests, which included a wide array of job shop instances, both periodic and real-time systems outperformed state-of-the-art dispatching rules by over 10% tardiness-wise. Nonetheless, the periodic rule selection approach was more robust across all tests than the real-time approach. These results demonstrate that there is a real incentive for managers to adopt dispatching rule selection systems.

Abstract
The Maintenance, Repair and Overhaul (MRO) activities in the aviation industry face constant challenges due to the uncertainty and variability of their operations. Aircraft engine maintenance, which is fundamental to the safety of aircraft operations, is particularly challenging due to its job-shop nature. Each engine requires a specific intervention process, based on its condition and the needs identified. The inherent uncertainty in task duration, resource availability, and the scope of required repairs adds complexity to capacity planning. Traditional capacity planning methods often fall short in accounting for these uncertainties, leading to potential inefficiencies and bottlenecks. Discrete Event Simulation (DES) emerges as a powerful tool to address these challenges. By modelling the entire MRO process, DES can consider various scenarios, incorporating the stochastic nature of task times, machine downtimes, and labour availability. This study explores the application of DES to evaluate capacity planning and quantify the impact of uncertainty on operational efficiency. The proposed methodology enables the anticipation of delays and enhances resource management. The primary contribution of this work is the ability to predict delays and quantify their impact. The future application of this tool in real-world MRO operations has the potential to enhance operational efficiency and reliability. © 2025 Elsevier B.V., All rights reserved.