Developing a Model for Solving the Flight Perturbation Problem

Abstract

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).

Uma proposta de solução para o Aircraft Recovery Problem de companhias aéreas regulares de pequeno porte

“…Já o estudo de Nickkar et al (2015) baseou-se na resolução do ARP e PRP a partir do método de decomposição Dantzig-Wolfe e heurística langragiana.…”

Machine Learning Approach for Flight Departure Delay Prediction and Analysis

“…Artificial intelligence (AI) techniques have been frequently applied in aviation research, such as gate assignment ( 7 , 8 ), aircraft landing problem (ALP) ( 9 – 11 ), flight perturbation problem (FPP) ( 12 ), and so forth. As an AI technique, this research employs support vector machine (SVM) techniques to identify the main factors contributing to delay and investigations of how each factor affects the delay in the system, with a focus on major New York airports.…”