A heuristic approach based on shortest path problems for integrated flight, aircraft, and passenger rescheduling under disruptions

Abstract

In this paper, we present a heuristic method to solve an airline disruption management problem arising from the ROADEF 2009 challenge. Disruptions perturb an initial flight plan such that some passengers cannot start or conclude their planned trip. The developed algorithm considers passengers and aircraft with the same priority by reassigning passengers and by creating a limited number of flights. The aim is to minimize the cost induced for the airline by the recovery from the disruptions. The algorithm is tested on real-life based data as well as on large scale instances and ranks among the best methods proposed to the challenge in terms of quality, while being efficient in terms of computation time.

A dynamic model for road protection against flooding

“…A sensitivity analysis is carried out to understand how solutions perform under various disruption scenarios. Jozefowiez et al (2012) propose a shortest-path based heuristic for rescheduling airlines over a multi-period horizon when disruptions occur. Ishfaq (2013) formulate a shortest-path model which considers inter-modal shipments to react to transportation link disruptions.…”

A column generation post-optimization heuristic for the integrated aircraft and passenger recovery problem

“…The cost of infeasibility is increased in order to force the algorithm to find a feasible solution. Jozefowiez et al [18], who finished in seventh position, developed a three-phase heuristic. In the first phase, the disruptions are integrated in the schedule.…”

“…After the challenge, Acuna-Agost [3] presented a post-processing procedure combined with the three-phase heuristic of Jozefowiez et al [18]. The problem was formulated as an integer programming model based on a minimum cost multi-commodity flow problem.…”

“…Two algorithms were developed to reduce the number of variables and constraints by identifying incompatible or suboptimal network nodes for each commodity. The solution method was able to greatly improve the solutions obtained by Jozefowiez et al [18]. Sinclair et al [28] later presented a large neighbourhood search heuristic (LNS) based on that of Bisaillon et al [7].…”

Route generation for optimization in the Air Transport System : aircraft recovery problem and 4D trajectory planning

“…This heuristic is composed of three phases: construction, repair, and improvement, which iteratively destroys and repairs parts of the solution. Another heuristic-based framework for the joint aircraft and passenger recovery problem is by Jozefowiez et al (2013). This heuristic also contains several stages; in the first stage, aircraft recovery is performed.…”