Math-heuristic to solve the airline recovery problem considering aircraft and passenger networks

Authors

  • José Carlos Fontoura Guimarães Universidade de São Paulo, São Paulo – Brasil https://orcid.org/0000-0002-1922-1175
  • Nicolau Dionísio Fares Gualda Universidade de São Paulo, São Paulo – Brasil (In memorium - O coautor Gualda faleceu em Junho de 2021)

DOI:

https://doi.org/10.14295/transportes.v29i2.2166

Keywords:

Recovery, Airline networks disruption, Math-heuristic

Abstract

The airline recovery problem involves aircraft, crew and passenger networks impacted by disruptions. Models to solve the problem consider one or more of these networks, on an integrated way or not. It belongs to the NP-hard class, even considering only one network. This work presents a math-heuristic to solve the problem integrating the aircraft and the passenger networks. A model to restore the aircraft network with minimum cost was also developed. These two models compose a framework which permits the airline to obtain the cost impact of including the passenger network in the recovery problem. Both models were tested with real world ROADEF instances using an Intel i7 microcomputer (16Gb of RAM) and a high-performance cluster node (HPC) with 512 GB of RAM. The microcomputer solved instances with up to 85 aircraft and 276 impacted flights in less than 30 minutes (imposed limit). The faster high-performance server reached solutions with minimum gap of 0 to 0.7% for the instances with higher number of flights. Total costs considering aircraft and passenger networks were very close to the aircraft network recovery results, showing a cost compensation which highlights the importance of solving the recovery problem integrating aircraft and passenger networks.

Downloads

Download data is not yet available.

Author Biographies

José Carlos Fontoura Guimarães, Universidade de São Paulo, São Paulo – Brasil

Engenheiro Naval pela Escola Politécnica da Universidade de São Paulo, Mestre em Matemática Aplicada pelo Instituto de Matemática e Estatítica da Universidade de São Paulo e Mestre em Engenharia de Transportes pela Escola Politécnica da Universidade de São Paulo

Nicolau Dionísio Fares Gualda, Universidade de São Paulo, São Paulo – Brasil (In memorium - O coautor Gualda faleceu em Junho de 2021)

Professor Titular Sênior
Coordenador do LPT/EPUSP - Laboratório de Planejamento e Operação de Transportes

References

Abdelghany, K.; S. Shah; S. Raina and A. Abdelghany (2004) A model for projecting flight delays during irregular operation conditions. Journal of Air Transport Management, v.10, p.385-394. DOI: 10.1016/j.jairtraman.2004.06.008

Abdelghany, K.; A. Abdelghany and G. Ekollu (2008) An integrated decision support tool for Airlines schedule recovery during irregular operations. European Journal of Operational Research, v.185, n.2, p.825–848. DOI: 10.1016/j.ejor.2006.12.045

Arikan, U.; S. Gurel and M. Akturk (2016) Integrated aircraft and passenger recovery with cruise time controllability. Annals of Operations Research, v.236, n.2, p.295-317. DOI: 10.1007/s10479-013-1424-2

Artigues, C.; E. Bourreau E; M. Afsar; O. Briant and M. Boudia (2012) Disruption management for commercial airlines: methods and results for the ROADEF 2009 Challenge, European Journal of Industrial Engineering, v.6, n.6, p. 669 – 689. DOI: 10.1504/EJIE.2012.051072

Bisaillon, S.; J-F. Cordeau J-F; G. Laporte and F. Pasin (2011) A large neighborhood search heuristic for the aircraft and passenger recovery problem. 4OR: A Quarterly Journal of Operations Research, v.9, p.139-157. DOI: 10.1007/s10288-010-0145-5

Bratu, S. and C. Barnhart (2006) Flight operations recovery: new approaches considering passenger recovery. Journal of Scheduling, v.9, n.3, p.279–298. DOI: 10.1007/s10951-006-6781-0

Caetano, D.J. and N. D .F. Gualda N.D.F. (2011) MAGS - An Aco-based Model to Solve the Schedule Generation and Fleet Assignment Integrated Problem. In Proceedings of the International Conference on Evolutionary Computation Theory and Applications, v.1: ECTA, p.227-232, 2011, Paris, France. DOI: 10.5220/0003673502270232

Caetano, D. J. and N. D. F. Gualda (2012). An Integrated Model for Flight Scheduling and Fleet Assignment Based on the Ant Colony Meta-Heuristic. In Proceedings of the XVII Pan-American Conference of Traffic and Transportation Engineering and Logistics, v.1, p.1-19, 2012, Santiago, Chile.

Caserta, M.; A. Ramirez and S. Voß (2010) A Math-Heuristic for the Multi-Level Capacitated Lot Sizing Problem with Carryover. European Conference on the Applications of Evolutionary Computation, p.462-471, Springer-Verlag, Berlin. DOI: 10.1007/978-3-642-12242-2_47

Castro, A. (2014) How automated negotiation and learning help to manage disruptions in airline operations control, Intelligent transportation systems applied to logistics and transportation conference, session IX, MASDIMA, Lda., 2014, Cascais, Portugal.

Castro, A. and E. Oliveira (2009) Quantifying quality operational costs in a multi-agent system for airline operations recovery. International Review on Computers and Software (IRECOS), v.4, n.4. https://hdl.handle.net/10216/15925

Castro, A. and E. Oliveira (2011) Airline operations control: a new concept for operations recovery. In: Airline Industry: Strategies, Operations and Safety, p.61-97, Connor R. Walsh, ISBN: 978-1-61122-079-7 2011.

Castro, A.; A. Rocha and E. Oliveira (2014) A new approach for disruption management in airline operations control. Studies in Computational Intelligence, v.562, Springer-Verlag, ISBN: 978-3-662-43373-7.

Eggenberg N.; M. Salani and M. Bierlaire (2010) Constraint-specific recovery network for solving recovery problems. Computers and Operations Research, v.37, n.6, p.1014–1026. DOI: 10.1016/j.cor.2009.08.006

Gao, C. (2007) Airline Integrated Planning and Operations. Ph.D. thesis, Georgia Institute of Technology, Atlanta, USA. http://hdl.handle.net/1853/16292

Gao Q.; X. Tang and J. Zhu (2009) Research on Greedy Simulated Annealing Algorithm for Irregular Flight Schedule Recovery Model. Proceedings of 2009 IEEE International Conference on Grey Systems and Intelligent Services, Nanjing, China. DOI:10.1007/978-3-642-13938-3_44

Gomes, W. P. (2014) Modelagem integrada do problema de programação de tripulantes de aeronaves. Tese (Doutorado), Departamento de Engenharia de Transportes, Escola Politécnica da Universidade de São Paulo, São Paulo, SP. DOI: 10.11606/t.3.2014.tde-25112014-143703

Gomes, W. P. and N. D. F. Gualda (2011). A Hybrid Genetic Algorithm for the Airline Crew Assignment Problem. In: Interna-tional Conference on Evolutionary Computation Theory and Applications (ECTA), 2011, Paris. Proceedings of the ECTA. Paris: SciTePress, 2011. p.190-195. ISBN: 978-989-8425-83-6

Gomes, W. P. and N. D. F. Gualda (2015) Heuristics to solve the integrated airline crew assignment problem. The Journal of Transport Literature, v.9, p.25-29. DOI: 10.1590/2238-1031.jtl.v9n1a5

Hu, Y.; Y. Song; K. Zhao and B. Xu (2016) Integrated recovery of aircraft and passengers after airline operation disruption based on a GRASP algorithm, Transportation Research Part E: Logistics abd Transportation Review, v.87, p.97–112. DOI: 10.1016/j.tre.2016.01.002

Jafari, N. and S. Zegordi (2010) The airline perturbation problem: considering disrupted passengers. Transportation Planning and Technology v.33, n.2, p.203–220. DOI: 10.1080/03081061003643788

Jafari, N. and S. Zegordi (2011) Simultaneous recovery model for aircraft and passengers, Journal of the Franklin Institute, v.348, n.7, p.1638–1655. DOI: 10.1016/j.jfranklin.2010.03.012

Jozefowiez, N.; C. Mancel and F. Mora-Camino (2012) A heuristic approach based on shortest path problems for integrated flight, aircraft, and passenger rescheduling under disruptions. Journal of the Operational Research Society, v.64, n.3, p.384. DOI: 10.1057/jors.2012.20

Kohl, N.; A. Larsen; J. Larsen; A. Ross and A. Tiourine (2007) Airline disruption management – Perspectives, experiences and outlook. Journal of Transportation Management, v.13, p.149-162. DOI: 10.1016/j.jairtraman.2007.01.001

Maher, S. (2015) A novel passenger recovery approach for the integrated airline recovery problem. Computer and Operations Research, v.57, p.123-137. DOI: 10.1016/j.cor.2014.11.005

Maher, S. (2016) Solving the integrated airline recovery problem using column-and-row generation. Transportation Science, v. 50, n.1, p.216-239. DOI: 10.1287/trsc.2014.0552

Marla, L.; B. Vaaben and C. Barnhart (2017) Integrated Disruption Management and Flight Planning to Trade Off Delays and Fuel Burn. Transportation Science, v.51, n.1, p.88-111. DOI: 10.1287/trsc.2015.0609

Medau, J. C. and N. D. F. Gualda. Alocação de aeronaves a voos considerando restrições operacionais, de manutenção e de desempenho das aeronaves, Transportes, v.26, p.101-117, 2018. DOI: 10.14295/transportes.v26i2.1316

Morais, F.; D. Caetano and N. D. F. Gualda (2018) Math-heuristic para o problema de recuperação de malha aérea (Recovery Problem). https://www.researchgate.net/publication/328433545_MATH-HEURISTIC PARA O_PROBLEMA DE _RECUPERACAO_ DE_ MALHA_AEREA_ RECOVERY _PROBLEM, XVII AIR TRANSPORTATION SYMPOSIUM - SITRAER, 2018, Volume: Conference Program, p. 34, São Paulo. (Retrieved in 5 set 2019) .

Muligan J. (2019) Europe's flight delays cost €100 a minute, Business World, Independent.ie https://www.independent.ie/business/world/europes-flight-delays-cost-100-a-minute-37934906.html, March, 21, 2019, Ireland. (Retrieved in 5 set 2019).

Palpant, M.; M. Boudia; C-A. Robelin; S. Gabteni and F. Laburthe (2009) ROADEF 2009 Challenge: Disruption Management for Commercial Aviation, Amadeus S.A.S., Operations Research Division, France.

Petersen, J.; G. Solveling; E. J. Johnson; J-P. Clarke and S. Shebalov (2012) An optimization approach to airline integrated recovery. Transportation Science v.46, n.4, p.482–500. DOI: 10.1287/trsc.1120.0414

Sinclair, K.; J-F. Cordeau and G. Laporte (2014) Improvements to a large neighborhood search heuristic for an integrated aircraft and passenger recovery problem. European Journal of Operational Research, v.233, p. 234–245. DOI: 10.1016/j.ejor.2013.08.034

Yu, G. and X. Qi (2004) Disruption management: frameworks, models and applications, World Scientific Publishing Co., London. ISBN: 978-9812560179

Zegordi, S. and N. Jafari (2010) Solving the airline recovery problem by using ant colony optimization. International Journal of Industiral Engineering & Production Research, v.21, n.3, p.121–128. DOI: http://IJIEPR.iust.ac.ir/

Zhang, Y. (2008) Real-time intermodal substitution: strategy for airline recovery from schedule perturbation and for mitigation of airport congestion. PhD Dissertation, University of California, Berkeley, USA.

Zhang, D.; H. Lau and C. Yu (2015) A two stage heuristic algorithm for the integrated aircraft and crew schedule recovery problems, Computers & Industrial Engineering, v.87, p.436–453. DOI: 10.1016/j.cie.2015.05.033

Zhang, D.; C. Yu C; J. Desai and H. Lau (2016) A math-heuristic algorithm for the integrated air service recovery, Transportation Research Part B, v.84, p.211–236. DOI: 10.1016/j.trb.2015.11.016

Downloads

Published

2021-07-30

How to Cite

Guimarães, J. C. F. ., & Gualda, N. D. F. . (2021). Math-heuristic to solve the airline recovery problem considering aircraft and passenger networks. TRANSPORTES, 29(2), 2166. https://doi.org/10.14295/transportes.v29i2.2166

Issue

Section

Artigos