The present study aims to optimize the problem of airplane re-arrangement scheduling through flight disruptions, which significantly contributes to the efficient management of airlines’ operational resources. The study is developed by using the Dantzig-Wolfe decomposition (Dantzig and Wolfe 1960; Barnhart et al. 1998; Desrosiers and Lübbecke 2005), where a mixed-integer multi-product flow model with secondary constraints is built and imbedded in an industrial optimization solver program. The model ensures that the disrupted schedules return to normal by re-arrange flights and optimize the amount of aircrafts usage to minimize delays and swapping relevant ones to fix the perturbations. Firstly, disrupted flight days are randomly chosen from the system. Secondly, data formatting and cleaning is necessary to adapt to the chosen algorithm model. And then, the proposed solution from the model is tested by running through the optimization program. Lastly, a new graphic schedule is drawn out with full information about flight numbers corresponding to aircraft registration, routings, flight departure and arrival time. Applications to a specific airline operating in Vietnam show some prominent results for such an airline in optimizing its fleet, reducing total delay time, and efficiently attaining a new schedule arrangement. The outcome demonstrates a significant benefit in reducing time and associated costs, as well as improving the accuracy of the airlineis crew planning. Validations over the whole procedure of such an airline's operations planning, including flight data within longer periods of up to one year, will be implemented in future work.