Airline disruption management: yesterday, today and tomorrow

Airline Disruption Management: A Naturalistic Decision-Making Perspective in an Operational Control Centre

“…There are basically three main models of Airline OCCs (Castro & Oliveira, 2008; Jimenez Serrano & Kazda, 2017). The traditional one (used by most airlines and modelled in this study), is composed of functional specialised groups responsible for each of the main elements which are important in the execution of each day of operations, such as flight dispatch, maintenance control and response, crew schedule/tracking, customer service and communication with air traffic control.…”

“…In addition to the OCC, these companies have a Hub Control Centre, which manages more local disruptions to hub airports. The third model, mostly used by American Airlines and Qatar Airways, is the Integrated Operations Centre that encompasses the functions which are normally part of the OCC in addition to catering, ground handlers, maintenance, social media, data analysis, revenue management and others (Jimenez Serrano & Kazda, 2017).…”

“…Managing and adjusting for unplanned and unexpected events is the main objective of an Operational Control Centre, as well as exchanging information and coordinating with organisations responsible for managing the air traffic flow and reporting metrics (Clarke, 1998; Bratu & Barnhart, 2006; Machado, 2010; Jimenez Serrano & Kazda, 2017; For a deep understanding of how flight dispatchers interact with air traffic control systems, see Smith et al, 2007). These processes are continuous and even though much effort is put into flight network planning, disruptions are part of the normal operation: there is a constant need to reconcile elements that cannot be completely controlled, such as weather, airport constraints (congestion, runway closure), flight delays, unscheduled aircraft maintenance, crew and industrial action, which add complexity to the operation of passenger and cargo flights (Filar et al, 2001; Ball et al, 2007; Mota et al, 2009).…”

Modelo e heurística matemática híbrida para o problema da recuperação de malha aérea

“…Since the 2000s, alternative methods have been proposed for the recovery of schedules with more than 400 lights. However, desirable computational times of a maximum of 30 min (Serrano & Kazdab, 2017) have not been achieved for large instances. Another point is the treatment of maintenance situations.…”

“…The mathematical heuristic presented for the aircraft recovery problem proved to be effective, as it produced results close to the optimal for the instances considered (GAP_Atrib did not exceed 1% in most instances). It was also ef icient, once it solved all instances in much less than the 30 minutes stipulated in the literature (Serrano et al, 2017) or even less than the 20 minutes considered by Petersen et al, 2012. It was found that what makes an instance dif icult is more the impact of disruptions than the size of the initial schedule. Also, it is possible to classify the disruptions regarding to the ability to clutter the schedule, making the recovery solution more dif icult.…”