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

Sinclair, Karine; Cordeau, Jean‐François; Laporte, Gilbert

doi:10.1016/j.cor.2015.06.014

Cited by 34 publications

(10 citation statements)

References 30 publications

(48 reference statements)

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“…Most commercial airlines use hub-and-spoke networks to make aircraft schedules [36]. Therefore, the generated LOFs were classified into the following four categories after being scored: hub to hub, hub to spoke, spoke to hub, and spoke to spoke.…”

Section: Solution Methods: Two-stage Heuristicmentioning

confidence: 99%

“…The algorithm consisted of three stages: first, the aircraft schedule is recovered by delaying, cancelling, or reassigning; second, flights that violate constraints are repaired, and cancelled passenger itineraries are reassigned to the existing flight schedule; and third, a local search is performed to improve the solution. Sinclair et al [36] proposed a mixed integer model for the integrated aircraft and passenger recovery problem and solved it by combining LNS heuristic and column generation.…”

Section: Integrated Recoverymentioning

confidence: 99%

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Two-Stage Heuristic Algorithm for Aircraft Recovery Problem

Zhang

2017

Discrete Dynamics in Nature and Society

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This study focuses on the aircraft recovery problem (ARP). In real-life operations, disruptions always cause schedule failures and make airlines suffer from great loss. Therefore, the main objective of the aircraft recovery problem is to minimize the total recovery cost and solve the problem within reasonable runtimes. An aircraft recovery model (ARM) is proposed herein to formulate the ARP and use feasible line of flights as the basic variables in the model. We define the feasible line of flights (LOFs) as a sequence of flights flown by an aircraft within one day. The number of LOFs exponentially grows with the number of flights. Hence, a two-stage heuristic is proposed to reduce the problem scale. The algorithm integrates a heuristic scoring procedure with an aggregated aircraft recovery model (AARM) to preselect LOFs. The approach is tested on five real-life test scenarios. The computational results show that the proposed model provides a good formulation of the problem and can be solved within reasonable runtimes with the proposed methodology. The two-stage heuristic significantly reduces the number of LOFs after each stage and finally reduces the number of variables and constraints in the aircraft recovery model.

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Section: Solution Methods: Two-stage Heuristicmentioning

confidence: 99%

Section: Integrated Recoverymentioning

confidence: 99%

Two-Stage Heuristic Algorithm for Aircraft Recovery Problem

Zhang

2017

Discrete Dynamics in Nature and Society

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“…Sinclair et al [25] improved the heuristic by adding some additional steps in each phase for the same problem. Then, Sinclair et al [26] solved the similar problem with a column generation postoptimization heuristic, which slightly increased the allotted computing time. The heuristic is based on a mixed-integer programming mode and forms various hierarchies of passengers, flights, and other elements to recursively solve the problem.…”

Section: Postdisruption Recoveries Of Aircraft and Passengersmentioning

confidence: 99%

Considering Passenger Preferences in Integrated Postdisruption Recoveries of Aircraft and Passengers

Yang

2019

Mathematical Problems in Engineering

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After a disruption to its operations, an airline needs to dispatch its aircraft and accommodate affected passengers in order to resume normal operations. When facing a flight delay or cancelation, passengers usually have two options—switching to a different itinerary or receiving ticket refunds. This paper focuses on the integrated recovery of both aircraft and passengers by taking into consideration passengers’ preferences regarding the two options. Objectives include minimizing the financial loss of airlines and minimizing the utility loss of passengers. To solve the optimization problem, we present a loop-based multiobjective genetic algorithm (GA) by leveraging a special characteristic of flight networks. Experiments based on real-world data demonstrate the effectiveness and stability of the algorithm in various situations. The outcome has theoretical and practical implications for disruption management and airline operations.

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“…However, most ARP studies (Abdelghany et al, 2008;Andersson and Värbrand, 2004;Argüello, 1997;Argüello et al, 1997;Bierlaire et al, 2007;Bratu and Barnhart, 2006;Cao and Kanafani, 1997a,b;Jarrah et al, 1993;Maher, 2016;Teodorović and Guberinić, 1984;Thengvall et al, 2003Thengvall et al, , 2001Yan and Lin, 1997) do not take airport capacity constraints explicitly into account. For the few models incorporating airport capacity constraints (Bisaillon et al, 2011;Jozefowiez et al, 2013;Rosenberger et al, 2003;Sinclair et al, 2016;Zhang et al, 2016), because of the complexity of the problems, they are usually solved by heuristics which do not guarantee optimality or provide any optimality gap for near-optimal solutions. Although Petersen et al (2012) provide an optimization approach to airline integrated recovery, it uses a heuristic to preselect flights to be input into its model to reduce model size.…”

Section: Aircraft Trajectory Planningmentioning

confidence: 99%

“…In contrast, if the blue aircraft is allowed to swap its planned maintenance with the green aircraft, it can be maintained at Airport B and its flights need not be cancelled. The (Bisaillon et al, 2011;Bratu and Barnhart, 2006;Maher, 2016;Sinclair et al, 2014Sinclair et al, , 2016Thengvall et al, 2000Thengvall et al, , 2003Thengvall et al, , 2001Yan and Lin, 1997;Zhang et al, 2016). Flights are copied and separated by predetermined intervals to represent all possible delay options as illustrated in Figure 2.2.…”

Section: Aircraft Trajectory Planningmentioning

confidence: 99%

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

Qian¹

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First and foremost, I thank my parents, Qian Rongchang and Yuan Zhaohua, who gave me life and raised me up in spite of all the difficulties. I am also deeply indebted to my fiancée, Cao Yin, her parents, Cao Di and Qian Ming, and her grandmother, Shen Guanbai. I could never have reached so far in my study without their firm support and confidence in me, even during the time I was in doubt of myself. I also thank my uncles and aunties, Yuan Yuehua and He Liqun, Yuan Jianjun and Ma Yuan, and, particularly, Yuan Jianmin and Hong Qin, who are always willing to give me their kind help. This dissertation is dedicated to my beloved grandparents, Qian Hao and Cui Yonglan and Yuan Gensheng and Jiang Shizhen, as well. I am so fortunate that I have been helped by so many significant professors during my Ph.D. study. I am extremely grateful to my supervisor, Prof Chen Chun-Hsien, for his profound knowledge, patient guidance, and heartwarming encouragement. In addition, my sincere thanks go to my co-supervisor, Prof Chen Songlin, for his insightful suggestion on research and career. I would also express my great gratitude to Prof Duan Fei, for his wisdom and care, which solved my confusion and anxiety in my dark time. What's more, I am privileged to have Prof Wen Changyun and Prof Jitamitra Desai as my thesis advisory committee members, and the discussions with Prof Nie Xiaofeng are always helpful. I will never forget Prof Mao Jianfeng, who brought me to Nanyang Technological University, got me start this marvelous Ph.D. journey, and continued to offer his generous help to me ever after. Moreover, I would like to extend my special thanks to Prof Liang Zhe, who kindly invited me to do research at Tongji University in Shanghai for one year. It is one year full of my best memories. I am eternally grateful for the teaching and training that I received during that period of time. Finally, my biggest thanks to my dear friends I have ever made in Singapore and Shanghai during my Ph.D. life. They are Yang Changpeng,

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

Cited by 34 publications

References 30 publications

Two-Stage Heuristic Algorithm for Aircraft Recovery Problem

Two-Stage Heuristic Algorithm for Aircraft Recovery Problem

Considering Passenger Preferences in Integrated Postdisruption Recoveries of Aircraft and Passengers

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

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