The airline perturbation problem: considering disrupted passengers

Jafari, Niloofar; Zegordi, Seyed Hessameddin

doi:10.1080/03081061003643788

Cited by 29 publications

(13 citation statements)

References 14 publications

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“…The aircraft arcs found in the LNS solution form a set X n ¼ fði; j; f Þ: ði; jÞ A A; f A F and x ijf ¼ 1g. Therefore, the assignment constraints (15) can be replaced with…”

Section: Multi-commodity Flow Problemmentioning

confidence: 99%

“…Constraints (13) and (14) impose the airport capacity limit. Constraints (15) assign each flight arc to at most one aircraft. Constraints (16) and (17) are the passenger flow conservation constraints.…”

Section: Mixed Integer Linear Programming Modelmentioning

confidence: 99%

“…Stojković et al [30] developed a linear program model for this joint problem, whereas Abdelghany et al [2] developed a multi-phase heuristic which integrates a simulation model and a resource assignment optimization model. As for the joint aircraft and passenger recovery problem, Zergodi et al [34] presented an ant colony optimization algorithm that takes into consideration passenger delay and cancellation costs in the objective function, while Jafari et al [15,16] presented a mixed integer programming model in which the variables represented aircraft rotations and passenger itineraries instead of flight legs. A detailed survey of the recovery problems can be found in Clausen et al [11].…”

Section: Introductionmentioning

confidence: 99%

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

Sinclair

Cordeau

Laporte

2016

Computers & Operations Research

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“…The aircraft arcs found in the LNS solution form a set X n ¼ fði; j; f Þ: ði; jÞ A A; f A F and x ijf ¼ 1g. Therefore, the assignment constraints (15) can be replaced with…”

Section: Multi-commodity Flow Problemmentioning

confidence: 99%

Section: Mixed Integer Linear Programming Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Sinclair

Cordeau

Laporte

2016

Computers & Operations Research

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“…Constraint contains that the number of reassigned passengers do not exceed the aircraft capacity, and each passenger would be assigned to a recovery scheme. [9]for solving aircrafts recovery problem considering passengers. The aircraft rotation is the object of their study and it reduces the size of the problem.…”

Section: Introductionmentioning

confidence: 99%

Solving the airline recovery problem based on vehicle routing problem with time window modeling and genetic algorithm

Gao

Zhan

2013

2013 Ninth International Conference on Natural Computation (ICNC)

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Disruptions caused by severe weather conditions, congestion at airports, air traffic control and mechanical failures can result in breakdown of planned schedule. To cope with these disruptions, the airline should recover from its disrupted schedule. The recovery includes aircrafts, crew and passengers recovery. We propose a method of modeling to this problem. The method transforms the airline recovery problem into a vehicle routing problem with time window, and the formulation considering aircrafts' rerouting and passengers' delivery is presented. Based on the model we present and the data from an airline in China, we carried out experimental computations. The result shows our model and solving method could be used in practical airline recovery.

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“…Having in mind that connecting passengers are almost three times more likely to be disrupted than originating ones, by missing their connecting flights they will be often re-accommodated on their best possible, but rather on the best available itineraries, assuming that each recovery itinerary must operationally be feasible while all its flight-legs operated [7]. This is made by applying the Passenger Recovery Plan which is based on the necessity to reassign disrupted passengers to alternative itineraries, commencing at the location after their available times and terminating at their destination or a location nearby [9]. Enabling also the minimum connecting time (MCT) achievable for getting the followup-flight(s), airlines may apply various policies for the passenger recovering such as: (i) unranked or first-disrupted-first-recovered policy, or (ii) ranked where the passengers are recovered in order of decreasing fare-class-value (the most valuable first), or in order of decreasing FFPs-status (FFP-members first) [7].…”

Section: Introductionmentioning

confidence: 99%

A decision support tool for evaluating decision options for out-bound flight delays considering high-valuable passengers

Radivojevic

Milbredt

2015

Eur. Transp. Res. Rev.

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Introduction Though studies show that arriving on-time is the service characteristic most valued by the air passengers and passenger service coordinators are consulted in the operation disruptions, passengers rarely drive operational decisionmaking in the disruption management of an airline. Taking into consideration airlines' seeking the prevention of losing passengers' goodwill in situations where the level of service quality (SQ) performed is not as high as the promoted one, this research is focused particularly on an influence of delayed connecting (or in-bound) high-fare passengers on making decisions on onward delays. Operationally seen, connecting passengers are of a particular importance for airlines, since they are already involved in their operations-flow, while becoming in this way the third most important airlines' aspect of operations, succeeding aircraft and crew. On the other hand, exclusive travellers such as first class and elite-status, contracted corporate members, and business passengers are considered as the high-valuable passengers, who are enough (financially) worth to the airline, particularly for to be waited for if they are delayed in arriving with an in-bound flight. Based upon an examination made from the airlines' operational point of view and for determined prioritization strategy, a Decision Support System (DSS) tool, named DEVOTED DSS Tool, for use in the disruption management of the Airline Operation Control Centre (AOCC) is presented. For assisting the airline operation controllers in decisions on whether to delay the departure of out-bound flights in order to wait for arriving-delayed highfare passengers from an in-bound flight, an influence of these passengers' satisfaction on making decisions on onward delays, the passenger segmentation per flight, associated consequences in terms of the Level of Service (LOS) performed by the carrier and the one perceived by the passengers, as well as the accompanying costs are taken into account. Methods The designed tool incorporates the key elements of Human-Centred Design (HCD) relying on a multi-criteria algorithm making-up its decision making process for evaluation of decision options and making suggestion. Being scenariodriven and knowledge-based the tool implements the airline prioritisation policies. Hereby, the level of service quality delivered by the air carrier and the level of service quality expected and perceived by the high-fare passengers are determined quantitatively by employing a created LOS-model which relies on the basic categorization rules of the Kano's quality model. Results For testing of the tool, two scheduled airlines with two different prioritization strategies and operating on the same flight route between the same origin-destination airports (i.e. city-pairs) are taken. Though the testing is real-world data based, in order to expose a tangible aim and capabilities of the designed tool, the testing scenarios are constructed in such a way to reflect specific borderline operational situations, while emph...

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The airline perturbation problem: considering disrupted passengers

Cited by 29 publications

References 14 publications

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

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

Solving the airline recovery problem based on vehicle routing problem with time window modeling and genetic algorithm

A decision support tool for evaluating decision options for out-bound flight delays considering high-valuable passengers

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