The Impact of Cruise Controllability on the Decision Making of Schedule Construction




Operations research, Transport, Aviation, Decision making


Nowadays, challenged by diverse uncertainties and disruptions (e.g., bad weather), as well as the strict environmental regulations imposed by authorities (e.g., on carbon emissions), airlines are struggling. How to improve their operational efficiency in such a volatile and adverse market becomes a top agenda of airlines. Among various operations, crew scheduling is fundamentally important as staffing cost is a big part of the total operational expenses. It is known that in crew scheduling, “robust crew pairing” is crucial to make the produced pairings less vulnerable in real operations. Existing studies generally construct robustness assuming that the aircraft cruise speed is fixed. However, prior studies have found that flight times exhibit significant variations due to reasons like cruise speed adjustment, and aircraft can control cruise speed for purposes like reducing delays. For crew, cruise speed controllability is also useful in hedging disruptions. For example, one flight can speed up to meet its on-time arrival even if it departs late due to crew disruptions. However, the impacts of cruise speed controllability on crew pairing robustness and the related environmental costs are under-explored. We thus propose this preliminary study to explore the possibility to use cruise speed controllability to enhance schedule robustness for crews.


Barnhart, C., & Cohn, A. (2004). Airline schedule planning: Accomplishments and opportunities. Manufacturing & service operations management, 6(1), 3-22.

Cacchiani, V., & Salazar-González, J. J. (2017). Optimal solutions to a real-world integrated airline scheduling problem. Transportation Science, 51(1), 250-268.

Deveci, M., & Demirel, N. C. (2018). Evolutionary algorithms for solving the airline crew pairing problem. Computers & Industrial Engineering, 115, 389-406.

Haouari, M., Mansour, F. Z., & Sherali, H. D. (2019). A new compact formulation for the daily crew pairing problem. Transportation Science, 53(3), 811-828.

Wen, X., Chung, S. H., Ji, P., & Sheu, J. B. (2022). Individual scheduling approach for multi-class airline cabin crew with manpower requirement heterogeneity. Transportation Research Part E: Logistics and Transportation Review, 163, 102763.

Antunes, D., Vaze, V., & Antunes, A. P. (2019). A Robust Pairing Model for Airline Crew Scheduling. Transportation Science, 53(6), 1751-1771.

Deveci, M., & Demirel, N. Ç. (2018). A survey of the literature on airline crew scheduling. Engineering Applications of Artificial Intelligence, 74, 54-69.

Gao, C., Johnson, E., & Smith, B. (2009). Integrated airline fleet and crew robust planning. Transportation Science, 43(1), 2-16.

Tekiner, H., Birbil, Ş. İ., & Bülbül, K. (2009). Robust crew pairing for managing extra flights. Computers & Operations Research, 36(6), 2031-2048.

Wei, K., & Vaze, V. (2018). Modeling crew itineraries and delays in the national air transportation system. Transportation Science, 52(5), 1276-1296.

Weide, O., Ryan, D., & Ehrgott, M. (2010). An iterative approach to robust and integrated aircraft routing and crew scheduling. Computers & Operations Research, 37(5), 833-844.

Wen, X., Ma, H. L., Chung, S. H., & Khan, W. A. (2020). Robust airline crew scheduling with flight flying time variability. Transportation Research Part E: Logistics and Transportation Review, 144, 102132.

Wen, X., Sun, X., Sun, Y., & Yue, X. (2021). Airline crew scheduling: Models and algorithms. Transportation Research Part E: Logistics and Transportation Review, 149, 102304.

Yan, S., Tung, T. T., & Tu, Y. P. (2002). Optimal construction of airline individual crew pairings. Computers & Operations Research, 29(4), 341-363.

AhmadBeygi, S., Cohn, A., & Weir, M. (2009). An integer programming approach to generating airline crew pairings. Computers & Operations Research, 36(4), 1284-1298.

Erdoğan, G., Haouari, M., Matoglu, M. Ö., & Özener, O. Ö. (2015). Solving a large-scale crew pairing problem. Journal of The Operational Research Society, 66(10), 1742-1754.

Quesnel, F., Desaulniers, G., & Soumis, F. (2019a). A branch-and-price heuristic for the crew pairing problem with language constraints. European Journal of Operational Research.

Quesnel, F., Desaulniers, G., & Soumis, F. (2019b). Improving air crew rostering by considering crew preferences in the crew pairing problem. Transportation Science.

Saddoune, M., Desaulniers, G., & Soumis, F. (2013). Aircrew pairings with possible repetitions of the same flight number. Computers & Operations Research, 40(3), 805-814.

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

Shao, S., Sherali, H. D., & Haouari, M. (2017). A novel model and decomposition approach for the integrated airline fleet assignment, aircraft routing, and crew pairing problem. Transportation Science, 51(1), 233-249.

Yan, S., & Tu, Y. P. (2002). A network model for airline cabin crew scheduling. European Journal of Operational Research, 140(3), 531-540.

Chung, S. H., Ma, H. L., & Chan, H. K. (2017). Cascading delay risk of airline workforce deployments with crew pairing and schedule optimization. Risk Analysis, 37(8), 1443-1458.

Yen, J. W., & Birge, J. R. (2006). A stochastic programming approach to the airline crew scheduling problem. Transportation Science, 40(1), 3-14.

Muter, İ., Birbil, Ş. İ., Bülbül, K., Şahin, G., Yenigün, H., Taş, D., & Tüzün, D. (2013). Solving a robust airline crew pairing problem with column generation. Computers & Operations Research, 40(3), 815-830.

Shebalov, S., & Klabjan, D. (2006). Robust airline crew pairing: Move-up crews. Transportation Science, 40(3), 300-312.

Dunbar, M., Froyland, G., & Wu, C. L. (2012). Robust airline schedule planning: Minimizing propagated delay in an integrated routing and crewing framework. Transportation Science, 46(2), 204-216.

Sun, X., Chung, S. H., Choi, T. M., Sheu, J. B., & Ma, H. L. (2020). Combating lead-time uncertainty in global supply chain's shipment-assignment: Is it wise to be risk-averse? Transportation Research Part B: Methodological, 138, 406-434.

Aktürk, M. S., Atamtürk, A., & Gürel, S. (2014). Aircraft rescheduling with cruise speed control. Operations Research, 62(4), 829-845.

Arıkan, U., Gürel, S., & Aktürk, M. S. (2016). Integrated aircraft and passenger recovery with cruise time controllability. Annals of Operations Research, 236(2), 295-317.

Arıkan, U., Gürel, S., & Aktürk, M. S. (2017). Flight network-based approach for integrated airline recovery with cruise speed control. Transportation Science, 51(4), 1259-1287.

Duran, A. S., Gürel, S., & Aktürk, M. S. (2015). Robust airline scheduling with controllable cruise times and chance constraints. IIE Transactions, 47(1), 64-83.

Gürkan, H., Gürel, S., & Aktürk, M. S. (2016). An integrated approach for airline scheduling, aircraft fleeting and routing with cruise speed control. Transportation Research Part C: Emerging Technologies, 68, 38-57.

Şafak, Ö., Çavuş, Ö., & Selim Aktürk, M. (2018). Multi-stage airline scheduling problem with stochastic passenger demand and non-cruise times. Transportation Research Part B: Methodological, 114, 39-67.

Şafak, Ö., Gürel, S., & Aktürk, M. S. (2017). Integrated aircraft-path assignment and robust schedule design with cruise speed control. Computers & Operations Research, 84, 127-145.

EUROCONTROL. (2009). Base of aircraft data (BADA) aircraft performance modelling report.

Dunbar, M., Froyland, G., & Wu, C. L. (2014). An integrated scenario-based approach for robust aircraft routing, crew pairing and re-timing. Computers & Operations Research, 45, 68-86.



How to Cite

Wen, X. ., & Guo, Z. . (2024). The Impact of Cruise Controllability on the Decision Making of Schedule Construction. Journal of Soft Computing and Decision Analytics, 2(1), 159-168.