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Flight planning, as one of the challenging issue in the industrial world, is faced with many uncertain conditions. One such condition is delay occurrence, which stems from various factors and imposes considerable costs on airlines, operators, and travelers. With these considerations in mind, we implemented flight delay prediction through proposed approaches that are based on machine learning algorithms. Parameters that enable the effective estimation of delay are identified, after which Bayesian modeling, decision tree, cluster classification, random forest, and hybrid method are applied to estimate the occurrences and magnitude of delay in a network. These methods were tested on a U.S. flight dataset and then refined for a large Iranian airline network. Results showed that the parameters affecting delay in US networks are visibility, wind, and departure time, whereas those affecting delay in Iranian airline flights are fleet age and aircraft type. The proposed approaches exhibited an accuracy of more than 70% in calculating delay occurance and magnitude in both the whole-network US and Iranian. It is hoped that the techniques put forward in this work will enable airline companies to accurately predict delays, improve flight planning, and prevent delay propagation.

In scheduled air transport, airline profitability is strongly impacted by an airline's ability to construct flight schedules. Airlines face operational challenges such as schedule disruptions and limited resources affecting the efficiency of the operations. These delays may have cascading impacts on the subsequent flights, as they affect the crew's schedule and possibly aircraft schedules as well. Consequently, negative effects of the initial delay propagate through the network until the slack between flights can absorb the delay. This paper makes functional and economic comparisons between robust planning and disruption management in the case of delay. Approaches based on the Dantzig–Wolfe decomposition are used to solve the problem. The proposed technique was implemented on an actual data set of one of the major Iranian airlines with more than 35 aeroplanes that fly between 50 airports. The computational results show the proposed methodology can solve the problem optimally and significantly reduce the delay of flights.

In the aviation and airline industry, crew costs are the second largest direct operating cost next to the fuel costs. But unlike the fuel costs, a considerable portion of the crew costs can be saved through optimized utilization of the internal resources of an airline company. Therefore, solving the flight perturbation scheduling problem, in order to provide an optimized schedule in a comprehensive manner that covered all problem dimensions simultaneously, is very important. In this paper, we defined an integrated recovery model as that which is able to recover aircraft and crew dimensions simultaneously in order to produce more economical solutions and create fewer incompatibilities between the decisions. Design/methodology: Current research is performed based on the development of one of the flight rescheduling models with disruption management approach wherein two solution strategies for flight perturbation problem are presented: Dantzig-Wolfe decomposition and Lagrangian heuristic. Findings: According to the results of this research, Lagrangian heuristic approach for the DW-MPsolved the problem optimally in all known cases. Also, this strategy based on the Dantig-Wolfe decomposition manage to produce a solution within an acceptable time (Under 1 Sec).

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