The Airline Operations Control Centre (AOCC) of an airline company is the organization responsible for monitoring and solving operational problems. It includes teams of human experts specialized in solving problems related with aircrafts, crewmembers, and passengers, in a process called disruption management or operations recovery. In this article, the authors propose a new concept for disruption management in this domain. The organization of the AOCC is represented by a multi-agent system (MAS), where roles that correspond to the most frequent tasks that could benefit from a cooperative approach, are performed by intelligent agents. The human experts, represented by agents that are able to interact with them, are part of this AOCC-MAS supervising the system and taking the final decision from the solutions proposed by the AOCC-MAS. The authors show the architecture of this AOCC-MAS, including the main costs involved and details about how the system takes decisions. They tested the concept, using several real airline crew-related problems and using four methods: human experts (traditional way), the AOCC-MAS with and without using quality-costs, and the integrated approach presented in this article. The results are presented and discussed.
ClausenJ.LarsenA.LarsenJ.Disruption management in the airline industry - concepts, models and methods. Technical report 2005-01, Informatics and Mathematical Modeling, Technical University of Denmark, DTU, 2005.
2.
KohlN.LarsenJ.RossA.LarsenJ.RossA.TiourlineS.Airline disruption management - perspectives, experiences and outlook. Technical report CRTR-0407, Carmen Research, 2004.
3.
BarnhartC.BelobabaP.OdoniA.‘Applications of operations research in the air transport industry.’Transp. Sci., 2003, 37, 368–391.
4.
ClausenJ.LarsenA.LarsenJ.RezanovaN. J.‘Disruption management in the airline industry - concepts, models and methods.’Comput. Oper. Res., 2010, 37, 809–821.
5.
CastroA. J. M.OliveiraE.‘Quantifying quality operational costs in a multi-agent system for airline operations recovery.’Int. Rev. Comput. Softw. (IRECOS), 2009, 4(4), 504–516.
6.
AbdelghanyK. F.AbdelghanyA. F.EkolluG.‘An integrated decision support tool for airlines schedule recovery during irregular operations’Eur. J. Oper. Res., 2008, 185, 825–848.
YangM.Using advanced tabu search techniques to solve airline disruption management problems. PhD DissertationThe University of Texas at Austin, USA, December, 2007.
9.
ZhaoX.ZhuJ.Grey programming for irregular flight scheduling. In Proceedings of the IEEE International Conference on Grey systems and intelligent services, New York, 2007, pp. 1607–1611 (IEEE).
10.
EggenbergN.BierlaireM.SalaniM.A column generation algorithm for disrupted airline schedules Technical report, Ecole Polytechnique Federale de Lausanne, 2007.
11.
ZhaoX.ZhuJ.Guo, Application of grey programming in irregular flight scheduling In Proceedings of the IEEE International Conference on Industrial engineering and engineering management, New York, 2007, pp. 164–168 (IEEE).
12.
CastroA. J. M.OliveiraE.Using specialized agents in a distributed mas to solve airline operations problems: a case study In Proceedings of the IAT 2007 (Intelligent Agent Technology Conference), SiliconValley, California, USA, 2–5 November 2007, pp. 473–476 (IEEE Computer Society).
13.
MedardC.SawhneyN.‘Airline Crew Scheduling: From planning to operations’Eur. J. Oper. Res., 2007, 183, 1013–1027.
14.
LiuT. K.JengC. R.LiuY. T.TzengJ. Y.Applications of multi-objective evolutionary algorithm to airline disruption management. In Proceedings of the 2006 IEEE InternationalConferenceonSystems, manand cybernetics, New York, 2006, pp. 4130–4135 (IEEE).
15.
LiuT.JengC.ChangY.‘Disruption management of an inequality-based multi-fleet airline schedule by a multi-objective genetic algorithm’Transp. Plan. Technol., 2008, 31(6), 613–639.
AbdelgahnyA.EkolluG.NarisimhanR.AbdelgahnyK.‘A proactive crew recovery decision support tool for commercial airlines during irregular operations’Ann. Oper. Res., 2004, 127, 309–331.
23.
GuoY.A decision support framework for the airline crew schedule disruption management with strategy mapping. In Operations Research Proceedings 2004, Berlin, Heidelberg, Springer, 2005.
24.
YuG.ArguelloM.SongG.McCowanS. M.WhiteA.‘A new era for crew recovery at Continental Airlines’Interfaces, 2003, 33, 5–22.
25.
RosenbergerJ.JohnsonE.NemhauserG.‘Rerouting aircraft for airline recovery’Transp. Sci., 2003, 37, 408–421.
BardJ.YuG.ArguelloM.‘Optimizing aircraft routings in response to groundings and delays’IIE Trans., 2001, 33, 931–947.
28.
ThengvallB.YuG.BardJ.‘Multiple fleet aircraft schedule recovery following hub closures.’Transp. Res. A, 2001, 35, 289–308.
29.
ThengvallB. G.BardJ. F.YuG.‘A bundle algorithm approach for the aircraft schedule recovery problem during hub closures’Transp. Sci., 2003, 37, 392–407.
30.
StojkovicM.SoumisF.‘An optimization model for the simultaneous operational flight and pilot scheduling problem’Manage. Sci., 2001, 47, 1290–1305.
ThengvallB.BardJ.YuG.‘Balancing user preferences for aircraft schedule recovery during irregular operations’IIE Trans., 2000, 32, 181–193.
33.
LuoS.YuG.‘Airline schedule perturbation problem: Landing and take-off with nonsplitable resource for the ground delay program’In Operations research in the airline industry (Ed. YuG.), 1998 (Kluwer Academic Publishers, Boston).
LettovskyL.Airline operations recovery: an optimization approach. PhD Dissertation, Georgia Institute of Technology, Atlanta, USA, 1997.
36.
WeiG.YuG.SongM.‘Optimization model and algorithm for crew management during airline irregular operations.’J. Comb. Optim., 1997, 1, 305–321.
37.
ArguelloM.BardJ.YuG.‘A GRASP for aircraft routing in response to groundings and delays’J. Comb. Optim., 1997, 5211–228.
38.
LuoS.YuG.‘On the airline schedule perturbation problem caused by the ground delay program’Transp. Sci., 1997, 31, 298–311.
39.
CaoJ. M.KanafaniA.‘Real-time decision support for integration of airline flight cancellations and delays, part I: Mathematical formulation’Transp. Plan. Technol., 1997, 20, 183–199.
40.
CaoJ. M.KanafaniA.‘Real-time decision support for integration of airline flight cancellations and delays, part II: Algorithm and computational experiments’Transp. Plan. Technol., 1997, 20, 201–217.
41.
YanS.TuY.P.‘Multifleet routing and multistop flight scheduling for schedule perturbation’Eur. J. Oper. Res., 1997, 103, 155–169.
42.
ClarkeM.Development of heuristic procedures for flight rescheduling in the aftermath of irregular airline operations. Working paper, October 1997.
43.
ClarkeM.The airline schedule recovery problem Working paper, October 1997.
44.
YanS.YangD. H.‘A decision support framework for handling schedule perturbations’Transp. Res. B, 1996, 30, 405–419.
45.
TalluriK.‘Swapping applications in a daily airline fleet assignment’Transp. Sci., 1996, 30, 237–248.
46.
MathaiselD.‘Decision support for airline system operations control and irregular operations’Comput. Oper. Res., 1996, 23, 1083–1098.
JohnsonV.LettovskyL.NemhauserG. L.PanditR.QueridoS.Final report to Northwest Airlines on the crew recovery problem. Technical report, The Logistic Institute, Georgia Institute of Technology, Atlanta, USA, 1994.
JarrahA.YuG.KrishnamurthyN.RakshitA.‘A decision support framework for airline flight cancellations and delays’Transp. Sci., 1993, 27, 266–280.
51.
RakshitA.KrishnamurthyN.YuG.‘System operations advisor: A real-time decision support system for managing airline operations at united airlines.’1996, Interfaces26, 50–58.
TeodorovicD.GuberinicS.‘Optimal dispatching strategy on an airline network after a schedule perturbation.’Eur. J. Oper. Res., 1984, 15, 178–182.
54.
CastroA.Centros de Controlo Operacional: Organização e Ferramentas. Monograph for Post-graduation in Air Transport Operations, ISEC - Instituto Superior de Educação e Ciências, Outubro2008 (in Portuguese).
55.
WooldridgeM.When is an agent-based solution appropriate? An introduction to multiagent systems2nd edition, 2009, p. 183 (John Wiley & Sons, West Sussex, UK).
56.
CastroA. J. M.Designing a multi-agent system for monitoring and operations recovery for an airline operations control centre. MScThesis, Faculty of Engineering, University of Porto, Porto, Portugal, 2007 pp. 1–133.
57.
JonkerG.MeyerJ.-J.DignumF.Towards a market mechanism for airport traffic control. In Proceedings of the 12th PortugueseConference on Artificial intelligence, (EPIA 2005), Covilha, Portugal, 2005.
58.
TumerK.AgoginoA.Distributed agent-based air traffic flow management. In Proceedings of the 6th International Joint Conference on Autonomous agents and multiagent systems (AAMAS 2007), Honolulu, Hawaii, 2007.
59.
WolfeS. R.JarvisP. A.EnomotoF. Y.SierhuisM.Comparing route selection strategies in collaborative traffic flow management. In Proceedings of the IAT 2007 (Intelligent Agent Technology Conference), Silicon Valley, California, USA, 2–5November 2007 (IEEE Computer Society).
60.
OuelhadjD. A.A multi-agent system for the integrated dynamic scheduling of steel production. PhD Dissertation, School of Computer Science and Information Technology, The University of Nottingham, UK, August 2003.
61.
CowlingP. I.OuelhadjD.PetrovicS.‘A multi-agent architecture for dynamic scheduling of steel hot rolling’J. Intel. Manuf. Special Issue Agent-Based Manuf. Process Plan. Sched., 2003, 15(5), 457–470.
KohlN.KarischS.‘Airline crew rostering. problem types, modeling, and optimization’Ann. Oper. Res., 2004, 127, 223–257.
64.
BellifemineF.CaireG.TruccoT.RimassaG.JADE 3.3, TILab S.p.A, 2004. 65 Elamy, A. Perspectives in agents-based technology, AgentLink News, 18 August 2005.
65.
ElamyA.‘Perspectives in agents-based technology’AgentLink News, 18 August 2005.
66.
CastroA.OliveiraE.‘The rationale behind the development of an airline operations control centre using Gaia-based methodology’Int. J. Agent-Oriented Softw. Eng., 2008, 2(3), 350–377.
RochaA. P.OliveiraE.Electronic institutions as a framework for agentsnegotiation and mutual commitment. In Proceedings of the 10th EPIA Conference on Progress in artificial intelligence (Eds BrazdilP.JorgeA.), LNAI 2258, 2001, pp. 232–245 (Springer, London, UK).
70.
RochaA. P.OliveiraE.An electronic market architecture for the formation of virtual enterprises, Infrastructures for Virtual Enterprises-Networking Industrial Enterprises, IFIP TC5 WG5.3/PRODNET (Eds Camarinha-MatosL.AfsarmaneshH.), 1999, pp. 421–432 (Kluwer Academic Publishers, Deventer, The Nether-lands).
71.
EhlersE. M.LangermanJ. J.A learning agent to assist in airline disruption management. In Proceedings of the 2005 International Conference on Computational intelligence for modelling, control and automation (CIMCA-IAWTIC'05), Vienna, Austria, 2005.
72.
SmithR.‘The contract net protocol: High-level communication and control in a distributed problem solver.’IEEE Trans. Comput., 1980, C-29(12), 1104–1113.
73.
MotaA.Multi-agent system for an airline operations control centre. MSc Thesis, Faculty of Engineering, University of Porto, Portugal2007.
74.
CastroA. J. M.OliveiraE.Using quality costs in a multi-agent system for airline operations control. In Proceedings of the 11th International Conference on Enterprise information systems, artificial intelligence and decision support systems, Milan, Italy, 6–10 May 2009, pp. 19–24.
