This study highlights the impact on costs generated by flight delays and cancellations, which pose a challenge for airlines in the country's economic context. The Aircraft Recovery Problem (ARP) consists of redefining an optimized sequencing of aircrafts, crews, and passengers after interruptions of operations. In this context, the objective of this study was to set up a flight planning model for minimizing the costs arising from aircraft delays and cancellations, i.e., a new plan that decides whether to keep the delayed flights, cancel the delayed flights or, if an aircraft is available, replaced it. A systematic literature review was conducted on the ARP and was identified relevant solution methods. In this paper, we formulate the ARP as a MIP model adopting a time-band network representation in which the time horizon is discretized, and developed an innovative algorithm that uses the Python-MIP library to solve it. The proposed algorithm allows solving the ARP to several scenarios. Computational results are illustrated for three different scenarios, considering the cancellation cost and passenger load on each flight, and results were satisfactory comparing with the study by Arguello et al. (1998). Discussion emphasizes the relevance of considering the computational complexity and limitations of approximation models when dealing with real problems, realizing that the results are only approximations of reality.