Operations Management Perspectives in the Air Transport Management

Gulsah Hancerliogullari Koksalmis (Istanbul Technical University)


The area of operations management has had a substantial effect on the today’s air transportation management. Having moved with huge demand from management to obtain a competitive advantage in the market, the airlines are utilizing advanced optimization techniques to develop decision support systems for operations management and control. In order to provide a service with high quality and low cost, airlines spend a tremendous amount of resources and effort to generate profitable and cost-effective fare classes, flight schedules, fleet plans, aircraft routes, crew scheduling, gate assignment, etc. In this paper, the techniques and operations management applications that are used in the air transportation industry are reviewed including demand forecasting, fleet assignment, aircraft routing, crew scheduling, runway scheduling problem and gate assignment.


Air transportation; Operations management; Runway scheduling; Fleet assignment; Crew scheduling; Aircraft routing; Demand forecasting; Gate assignment

Full Text:



[1] Coldren GM, Koppelman FS, Kasturirangan K, Mukherjee A. Modeling aggregate air-travel itinerary shares: logit model development at a major US airline. Journal of Air Transport Management. 2003;9:361-9.

[2] Abdelghany A, Abdelghany K. Modeling applications in the airline industry: Routledge; 2016.

[3] De Neufville R. Airports of the future: the development of airport systems. AIAA International Air and Space Symposium and Exposition: The Next 100 Years2003. p. 2536.

[4] Xiao Y, Fu X, Zhang A. Demand uncertainty and airport capacity choice. Transportation Research Part B: Methodological. 2013;57:91-104.

[5] Yu G. Operations Research in the Airline Industry. International Series in Operations Research & Management Science. Kluwer Academic Publishers; 1998.

[6] Burke EK, De Causmaecker P, De Maere G, Mulder J, Paelinck M, Berghe GV. A multi-objective approach for robust airline scheduling. Computers & Operations Research. 2010;37:822-32.

[7] Petersen JD. Large-scale mixed integer optimization approaches for scheduling airline operations under irregularity: Georgia Institute of Technology; 2012.

[8] Coldren GM, Koppelman FS. Modeling the competition among air-travel itinerary shares: GEV model development. Transportation Research Part A: Policy and Practice. 2005;39:345-65.

[9] Hsiao C-Y, Hansen M. A passenger demand model for air transportation in a hub-and-spoke network. Transportation Research Part E: Logistics and Transportation Review. 2011;47:1112-25.

[10] Hess S. Posterior analysis of random taste coefficients in air travel behaviour modelling. Journal of Air Transport Management. 2007;13:203-12.

[11] Nassiri H, Rezaei A. Air itinerary choice in a low-frequency market: A decision rule approach. Journal of Air Transport Management. 2012;18:34-7.

[12] Sherali HD, Bish EK, Zhu X. Airline fleet assignment concepts, models, and algorithms. European Journal of Operational Research. 2006;172:1-30.

[13] Abara J. Applying integer linear programming to the fleet assignment problem. Interfaces. 1989;19:20-8.

[14] Berge ME, Hopperstad CA. Demand driven dispatch: A method for dynamic aircraft capacity assignment, models and algorithms. Operations Research. 1993;41:153-68.

[15] Rushmeier RA, Kontogiorgis SA. Advances in the optimization of airline fleet assignment. Transportation Science. 1997;31:159-69.

[16] Desaulniers G, Desrosiers J, Dumas Y, Solomon MM, Soumis F. Daily aircraft routing and scheduling. Management Science. 1997;43:841-55.

[17] Kohl N, Madsen OB. An optimization algorithm for the vehicle routing problem with time windows based on Lagrangian relaxation. Operations Research. 1997;45:395-406.

[18] Belanger N, Desaulniers G, Soumis F, Desrosiers J. Periodic airline fleet assignment with time windows, spacing constraints, and time dependent revenues. European Journal of Operational Research. 2006;175:1754-66.

[19] Salazar-González J-J. Approaches to solve the fleet-assignment, aircraft-routing, crew-pairing and crew-rostering problems of a regional carrier. Omega. 2014;43:71-82.

[20] Jiang H, Barnhart C. Robust airline schedule design in a dynamic scheduling environment. Computers & Operations Research. 2013;40:831-40.

[21] Pilla VL, Rosenberger JM, Chen V, Engsuwan N, Siddappa S. A multivariate adaptive regression splines cutting plane approach for solving a two-stage stochastic programming fleet assignment model. European Journal of Operational Research. 2012;216:162-71.

[22] Bielli M, Bielli A, Rossi R. Trends in models and algorithms for fleet management. Procedia-Social and Behavioral Sciences. 2011;20:4-18.

[23] Barnhart C, Cohn AM, Johnson EL, Klabjan D, Nemhauser GL, Vance PH. Airline crew scheduling. Handbook of transportation science: Springer; 2003. p. 517-60.

[24] Anbil R, Gelman E, Patty B, Tanga R. Recent advances in crew-pairing optimization at American Airlines. Interfaces. 1991;21:62-74.

[25] Gopalakrishnan B, Johnson EL. Airline crew scheduling: state-of-the-art. Annals of Operations Research. 2005;140:305-37.

[26] Arabeyre J, Fearnley J, Steiger F, Teather W. The airline crew scheduling problem: A survey. Transportation Science. 1969;3:140-63.

[27] Andersson E, Housos E, Kohl N, Wedelin D. Crew pairing optimization. Operations research in the airline industry: Springer; 1998. p. 228-58.

[28] Ryan DM. The solution of massive generalized set partitioning problems in aircrew rostering. Journal of the operational research society. 1992;43:459-67.

[29] Dawid H, König J, Strauss C. An enhanced rostering model for airline crews. Computers & Operations Research. 2001;28:671-88.

[30] Weide O, Ryan D, Ehrgott M. An iterative approach to robust and integrated aircraft routing and crew scheduling. Computers & Operations Research. 2010;37:833-44.

[31] Sherali HD, Hobeika AG, Trani AA, Kim BJ. An integrated simulation and dynamic programming approach for determining optimal runway exit locations. Management Science. 1992;38:1049-62.

[32] Brinton CR. An implicit enumeration algorithm for arrival aircraft. Digital Avionics Systems Conference, 1992 Proceedings, IEEE/AIAA 11th: IEEE; 1992. p. 268-74.

[33] Bazargan M, Fleming K, Subramanian P. A simulation study to investigate runway capacity using TAAM. Simulation Conference, 2002 Proceedings of the Winter: IEEE; 2002. p. 1235-43.

[34] Al-Salem A, Farhadi F, Kharbeche M, Ghoniem A. Multiple-runway aircraft sequencing problems using mixed-integer programming. IIE Annual Conference Proceedings: Institute of Industrial and Systems Engineers (IISE); 2012. p. 1.

[35] Bennell JA, Mesgarpour M, Potts CN. Airport runway scheduling. 4OR. 2011;9:115.

[36] Cao J-M, Kanafani A. The value of runway time slots for airlines. European Journal of Operational Research. 2000;126:491-500.

[37] Sherali HD, Hill JM, McCrea MV, Trani AA. Integrating slot exchange, safety, capacity, and equity mechanisms within an airspace flow program. Transportation Science. 2011;45:271-84.

[38] Vossen T, Ball M. Optimization and mediated bartering models for ground delay programs. Naval Research Logistics (NRL). 2006;53:75-90.

[39] Dear RG, Sherif YS. The dynamic scheduling of aircraft in high density terminal areas. Microelectronics Reliability. 1989;29:743-9.

[40] Dear RG, Sherif YS. An algorithm for computer assisted sequencing and scheduling of terminal area operations. Transportation Research Part A: General. 1991;25:129-39.

[41] Artiouchine K, Baptiste P, Dürr C. Runway sequencing with holding patterns. European Journal of Operational Research. 2004.

[42] Atkin JA, Burke EK, Greenwood JS. TSAT allocation at London Heathrow: the relationship between slot compliance, throughput and equity. Public Transport. 2010;2:173-98.

[43] Atkin JA, Burke EK, Greenwood JS, Reeson D. Hybrid metaheuristics to aid runway scheduling at London Heathrow airport. Transportation Science. 2007;41:90-106.

[44] Atkin JA, Burke EK, Greenwood JS, Reeson D. A metaheuristic approach to aircraft departure scheduling at London Heathrow airport. Computer-aided Systems in Public Transport: Springer; 2008. p. 235-52.

[45] Beasley JE, Krishnamoorthy M, Sharaiha YM, Abramson D. Displacement problem and dynamically scheduling aircraft landings. Journal of the operational research society. 2004;55:54-64.

[46] Beasley JE, Sonander J, Havelock P. Scheduling aircraft landings at London Heathrow using a population heuristic. Journal of the operational research society. 2001;52:483-93.

[47] Venkatakrishnan C, Barnett A, Odoni AR. Landings at Logan Airport: Describing and increasing airport capacity. Transportation Science. 1993;27:211-27.

[48] Psaraftis HN. A dynamic programming approach for sequencing groups of identical jobs. Operations Research. 1980;28:1347-59.

[49] Bianco L, Dell’Olmo P, Giordani S. Scheduling models and algorithms for TMA traffic management. Modelling and simulation in air traffic management: Springer; 1997. p. 139-67.

[50] Bianco L, Rinaldi G, Sassano A. A combinatorial optimization approach to aircraft sequencing problem. Flow Control of Congested Networks: Springer; 1987. p. 323-39.

[51] Balakrishnan H, Chandran BG. Algorithms for scheduling runway operations under constrained position shifting. Operations Research. 2010;58:1650-65.

[52] Ghoniem A, Sherali HD, Baik H. Enhanced models for a mixed arrival-departure aircraft sequencing problem. INFORMS Journal on Computing. 2014;26:514-30.

[53] Sherali HD, Adams WP. A hierarchy of relaxations between the continuous and convex hull representations for zero-one programming problems. SIAM Journal on Discrete Mathematics. 1990;3:411-30.

[54] Sherali HD, Adams WP. A hierarchy of relaxations and convex hull characterizations for mixed-integer zero—one programming problems. Discrete Applied Mathematics. 1994;52:83-106.

[55] Sherali HD, Adams WP. Reformulation-Convexification Technique for Quadratic Programs and Some Convex Envelope Characterizations. A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems: Springer; 1999. p. 297-367.

[56] Sarin SC, Sherali HD, Bhootra A. New tighter polynomial length formulations for the asymmetric traveling salesman problem with and without precedence constraints. Operations research letters. 2005;33:62-70.

[57] Sherali HD, Sarin SC, Tsai P-F. A class of lifted path and flow-based formulations for the asymmetric traveling salesman problem with and without precedence constraints. Discrete Optimization. 2006;3:20-32.

[58] Sherali HD, Staats RW, Trani AA. An airspace planning and collaborative decision-making model: Part I—Probabilistic conflicts, workload, and equity considerations. Transportation Science. 2003;37:434-56.

[59] Nemhauser GL, Wolsey LA. Integer programming and combinatorial optimization. Wiley, Chichester GL Nemhauser, MWP Savelsbergh, GS Sigismondi (1992) Constraint Classification for Mixed Integer Programming Formulations COAL Bulletin. 1988;20:8-12.

[60] Bazaraa MS, Jarvis JJ, Sherali HD. Linear programming and network flows: John Wiley & Sons; 2011.

[61] Desrosiers J, Lübbecke ME. A primer in column generation. Column generation: Springer; 2005. p. 1-32.

[62] Lübbecke ME, Desrosiers J. Selected topics in column generation. Operations Research. 2005;53:1007-23.

[63] Ghoniem A, Sherali HD. Complementary column generation and bounding approaches for set partitioning formulations. Optimization Letters. 2009;3:123.

[64] Blum C, Roli A. Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM computing surveys (CSUR). 2003;35:268-308.

[65] Gendreau M, Potvin J-Y. Metaheuristics in combinatorial optimization. Annals of Operations Research. 2005;140:189-213.

[66] Bianchi L, Dorigo M, Gambardella LM, Gutjahr WJ. A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing. 2009;8:239-87.

[67] Pinol H, Beasley JE. Scatter search and bionomic algorithms for the aircraft landing problem. European Journal of Operational Research. 2006;171:439-62.

[68] Ciesielski V, Scerri P. Real time genetic scheduling of aircraft landing times. Evolutionary Computation Proceedings, 1998 IEEE World Congress on Computational Intelligence, The 1998 IEEE International Conference on: IEEE; 1998. p. 360-4.

[69] Ernst AT, Krishnamoorthy M, Storer RH. Heuristic and exact algorithms for scheduling aircraft landings. Networks: An International Journal. 1999;34:229-41.

[70] Soomer MJ, Franx GJ. Scheduling aircraft landings using airlines’ preferences. European Journal of Operational Research. 2008;190:277-91.

[71] Hancerliogullari G, Rabadi G, Al-Salem AH, Kharbeche M. Greedy algorithms and metaheuristics for a multiple runway combined arrival-departure aircraft sequencing problem. Journal of Air Transport Management. 2013;32:39-48.

[72] Kabbani NM, Patty BW. Aircraft routing at American airlines. proceedings of the agifors symposium1992.

[73] Clarke L, Johnson E, Nemhauser G, Zhu Z. The aircraft rotation problem. Annals of Operations Research. 1997;69:33-46.

[74] Mukherjee A, Hansen M. A dynamic rerouting model for air traffic flow management. Transportation Research Part B: Methodological. 2009;43:159-71.

[75] Mercier A, Cordeau J-F, Soumis F. A computational study of Benders decomposition for the integrated aircraft routing and crew scheduling problem. Computers & Operations Research. 2005;32:1451-76.

[76] Mercier A, Soumis F. An integrated aircraft routing, crew scheduling and flight retiming model. Computers & Operations Research. 2007;34:2251-65.

[77] Haouari M, Aissaoui N, Mansour FZ. Network flow-based approaches for integrated aircraft fleeting and routing. European Journal of Operational Research. 2009;193:591-9.

[78] Sherali HD, Bae K-H, Haouari M. A benders decomposition approach for an integrated airline schedule design and fleet assignment problem with flight retiming, schedule balance, and demand recapture. Annals of Operations Research. 2013;210:213-44.

[79] Dorndorf U, Jaehn F, Lin C, Ma H, Pesch E. Disruption management in flight gate scheduling. Statistica Neerlandica. 2007;61:92-114.

[80] Braaksma J. Reducing walking distances at existing airports. Airport Forum1977.

[81] Obata T. The quadratic assignment problem: Theory and algorithms (tech. rep.). Troy, NY: Rensselaer Polytechnic Institute. 1979.

[82] Babić O, Teodorović D, Tošić V. Aircraft stand assignment to minimize walking. Journal of Transportation Engineering. 1984;110:55-66.

[83] Mangoubi R, Mathaisel DF. Optimizing gate assignments at airport terminals. Transportation Science. 1985;19:173-88.

[84] Hassounah MI, Steuart GN. Demand for aircraft gates. Transportation Research Record. 1993.

[85] Bolat A. Procedures for providing robust gate assignments for arriving aircrafts. European Journal of Operational Research. 2000;120:63-80.

[86] Cheng Y. Network-based simulation of aircraft at gates in airport terminals. Journal of Transportation Engineering. 1998;124:188-96.

[87] Haghani A, Chen M-C. Optimizing gate assignments at airport terminals. Transportation Research Part A: Policy and Practice. 1998;32:437-54.

[88] Tang C-H, Wang W-C. Airport gate assignments for airline-specific gates. Journal of Air Transport Management. 2013;30:10-6.

DOI: https://doi.org/10.30564/jbar.v2i1.288


  • There are currently no refbacks.
Copyright © 2019 Gulsah Hancerliogullari Koksalmis

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.