This paper proposes using reinforcement learning (RL) to schedule maintenance tasks, which can significantly reduce direct operating costs for airlines. The approach consists of a static algorithm for long-term scheduling and an adaptive algorithm for rescheduling based on new maintenance information. To assess the performance of both approaches, three key performance indicators (KPIs) are defined: Ground Time, representing the hours an aircraft spends on the ground; Time Slack, measuring the proximity of tasks to their due dates; and Change Score, quantifying the similarity level between initial and adapted maintenance plans when new information surfaces. The results demonstrate the efficacy of RL in producing efficient maintenance plans, with the algorithms complementing each other to form a solid foundation for routine tasks and real-time responsiveness to new information. While the static algorithm performs slightly better in terms of Ground Time and Time Slack, the adaptive algorithm excels overwhelmingly in terms of Change Score, offering greater flexibility in handling new maintenance information. The proposed RL-based approach can improve the efficiency of aircraft maintenance and has the potential for further research in this area.

This paper presents a Reinforcement Learning (RL) approach to optimize the long-term scheduling of maintenance for an aircraft fleet. The problem considers fleet status, maintenance capacity, and other maintenance constraints to schedule hangar checks for a specified time horizon. The checks are scheduled within an interval, and the goal is to, schedule them as close as possible to their due date. In doing so, the number of checks is reduced, and the fleet availability increases. A Deep Q-learning algorithm is used to optimize the scheduling policy. The model is validated in a real scenario using maintenance data from 45 aircraft. The maintenance plan that is generated with our approach is compared with a previous study, which presented a Dynamic Programming (DP) based approach and airline estimations for the same period. The results show a reduction in the number of checks scheduled, which indicates the potential of RL in solving this problem. The adaptability of RL is also tested by introducing small disturbances in the initial conditions. After training the model with these simulated scenarios, the results show the robustness of the RL approach and its ability to generate efficient maintenance plans in only a few seconds.

Sweden has a strong demand on the construction of student accommodations and consequently significant efforts have been taken to increase and streamline construction methods. In addition, the fluctuation on the number of students admitted at each year, in each university, lead to periods of house shortage or, in opposition to that, to eventual surplus on the housing market. For these reasons urges finding a fast execution process in construction to fulfil the market needs, together with a housing mechanism of control which balances the students' needs with the housing availability. FRAMEUP system arises thus to solve both problems by combining a modular construction with an innovative execution process. The FRAMEUP buildings uses a steel frame in combination with prefabricated 3D modules (fully equipped and suitable for student accommodations) which are assembled by starting from the roof to the 1st floor. The existence of a lifting system permits the erection of the building, promoting each time the building is lifted, a clearance of one floor height, at ground level, for the assembly of a new floor. The procedure is repeated several times, according to the number of floors, until the 1st floor of the building, the last floor of the execution sequence, is assembled. Alongside with its advantage on the fast execution, the FRAMEUP system allows to efficiently increase or decrease the number of the floors and consequently its permutability with other buildings of the same nature. Thus, assuming a net of FRAMEUP buildings at each university, its permutability system would create the necessary conditions so that the number of floors at each campus would follow the fluctuations of the students' population among the different universities on different periods of time, so to suppress the needs for housing or to avoid the surplus on construction.