Abstract
Pilots and aircraft are among the most valuable assets of an airline. Buying aircraft and hiring pilots are crucial strategic decisions companies must oversee for sustainability. The cost of buying, selling, leasing, and long production times for aircraft challenge companies in making optimal long-term decisions. Union rules, pilot shortages, pilot surplus, and the cost of employing an excessive number of pilots are factors complicating the workforce planning for airline companies worldwide. Under these volatile and conflicting circumstances, many companies cannot strategically plan for the planning of pilots to aircraft to meet short-term tactical decisions against mid/long-term company strategies. In this study, our objective is to optimize long-term crew planning by minimizing the total crew cost considering captain promotions and new hires, without compromising the pilot experience. A mixed integer programming model is developed to solve the long-term airline crew planning problem. Realistic business scenarios are used to determine the optimal pilot hiring and promotion patterns for both high-and low-demand scenarios. The results show that the proposed optimization method significantly reduces crew costs without compromising the pilot experience in various demand and cost scenarios. The mathematical model, the realistic business scenarios, and the business insights for airlines are deemed novel contributions to the pertinent literature and industry practices.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Akyurt İZ, Kuvvetli Y, Deveci M et al (2022) A new mathematical model for determining optimal workforce planning of pilots in an airline company. Complex Intell Syst 8:429–441. https://doi.org/10.1007/s40747-021-00386-x
Altenstedt F, Thalén B, Sjögren P, Nilsson S (2017) Solving the airline manpower planning problem. In: 13th workshop on models and algorithms for planning and scheduling problems. Bremen, Germany, p 53
Bagheri M, Ebrahimnejad A, Razavyan S et al (2021) Solving fuzzy multi-objective shortest path problem based on data envelopment analysis approach. Complex Intell Syst. https://doi.org/10.1007/s40747-020-00234-4
Bai Y, Li Q, Fan Y, Liu S (2021) Motif-h: a novel functional backbone extraction for directed networks. Complex Intell Syst. https://doi.org/10.1007/s40747-021-00530-7
Biruk S, Ja´skowski PJ, Maciaszczyk M (2022) Conceptual framework of a simulation-based manpower planning method for construction enterprises. Sustainability 14:5341. https://doi.org/10.3390/su14095341
Cankaya B, Wari E, Eren Tokgoz B (2019) Practical approaches to chemical tanker scheduling in ports: a case study on the Port of Houston. Marit Econ Logist 21:559–575
Cankaya B, Topuz K, Delen D, Glassman A (2023) Evidence-based managerial decision-making with machine learning: the case of bayesian inference in aviation incidents. Omega 120:102906. https://doi.org/10.1016/j.omega.2023.102906
Chen S, Shen Y (2013) An improved column generation algorithm for crew scheduling problems. J Inf Comput Sci 10:175
Chu SCK, Lin CKY (1994) Cohort analysis technique for long-term manpower planning: the case of a Hong Kong tertiary institution. J Oper Res Soc 45:696–709. https://doi.org/10.1057/jors.1994.107
Chuanchao Z (2020) Two-level fusion big data compression and reconstruction framework combining second-generation wavelet and lossless compression. Complex Intell Syst. https://doi.org/10.1007/s40747-020-00158-z
Chung SH, Tse YK, Choi TM (2015) Managing disruption risk in express logistics via proactive planning. Ind Manag Data Syst. https://doi.org/10.1108/IMDS-04-2015-0155
Di Francesco M, Llorente ND-M, Zanda S, Zuddas P (2016) An optimization model for the short-term manpower planning problem in transhipment container terminals. Comput Ind Eng 97:183–190. https://doi.org/10.1016/j.cie.2016.04.012
Diaz R, Phan C, Golenbock D, Sanford B (2022) A prescriptive framework to support express delivery supply chain expansions in highly urbanized environments. Ind Manag Data Syst. https://doi.org/10.1108/IMDS-02-2022-0076
Dijkstra MC, Kroon LG, van Nunen JAEE, Salomon M (1991) A DSS for capacity planning of aircraft maintenance personnel. Int J Prod Econ 23:69–78. https://doi.org/10.1016/0925-5273(91)90049-Y
Ding R, Gao M (2012) Research on pilots development planning. J Softw Eng Appl 05:1016–1022. https://doi.org/10.4236/jsea.2012.512118
Doi T, Nishi T, Voß S (2018) Two-level decomposition-based metaheuristic for airline crew rostering problems with fair working time. Eur J Oper Res 367:428–438
Egilmez G, Erenay B, Süer GA (2014) Stochastic skill-based manpower allocation in a cellular manufacturing system. J Manuf Syst 33:578–588. https://doi.org/10.1016/j.jmsy.2014.05.005
Elattar HM, Elminir HK, Riad AM (2016) Prognostics: a literature review. Complex Intell Syst. https://doi.org/10.1007/s40747-016-0019-3
Eltoukhy AEE, Chan FTS, Chung SH et al (2017a) Heuristic approaches for operational aircraft maintenance routing problem with maximum flying hours and man-power availability considerations. Ind Manag Data Syst. https://doi.org/10.1108/IMDS-11-2016-0475
Eltoukhy AEE, Chan FTS, Chung SH (2017b) Airline schedule planning: a review and future directions. Ind Manag Data Syst 117:1201–1243. https://doi.org/10.1108/IMDS-09-2016-0358
Erenay B, Suer GA (2018) Heuristic and mathematical modelling approaches for optimal manpower allocation and cell size determination. In: Suer GA, Gen M (eds) Cellular manufacturing systems: recent developments, analysis and case studies. Nova Science Publishers, Hauppauge, New York, pp 347–380
FAA Code of Federal Regulations (2022) Aeronautical experience: Airplane category rating. In: https://www.ecfr.gov/current/title-14/chapter-I/subchapter-D/part-61/subpart-G/section-61.159
Gang Yu, Dugan S, Argüello M (1998) Moving toward an integrated decision support system for manpower planning at continental airlines: optimization of pilot training assignments. In: Gang Yu (ed) Industrial applications of combinatorial optimization. Springer, Boston, MA, pp 1–24
Harvey G, Turnbull P (2010) On the go: walking the high road at a low cost airline. Int J Hum Resour Manag 21:230–241
Holm Å (2008) Manpower planning in airlines: modeling and optimization. Linköping University
Jin Y, Gao Y, Wang P et al (2019) Improved manpower planning based on traffic flow forecast using a historical queuing model. IEEE Access 7:125101–125112. https://doi.org/10.1109/ACCESS.2019.2933319
Kangis P, O’Reilly MD (2003) Strategies in a dynamic marketplace. a case study in the airline industry. J Bus Res 56:105–111. https://doi.org/10.1016/S0148-2963(01)00282-X
Kıbış EY, Büyüktahtakın İE, Haight RG, Akhundov N, Knight K, Flower CE (2021) A multistage stochastic programming approach to the optimal surveillance and control of the emerald ash borer in cities. INFORMS Journal on Computing 33(2):808–834
Lee D, Singer E (2014) What’s your number? Interpreting the “fair and equitable” standard in seniority integration for airlines and other industries. Econ Transp. https://doi.org/10.1016/j.ecotra.2013.12.002
Li X, Tu F, Li Y, et al (2004) Optimal manpower recruitment and dismissal decision for single-type job. J Syst Sci Inf; 2
Liu SQ, D’ariano A, Kozan E, et al (2019) A classification and literature survey on aviation management. In: Proceedings of the 2019 international conference on industrial engineering and systems management, IESM, 1–5 2019
Maung YSY, Douglas I, Tan D (2022) Identifying the drivers of profitable airline growth. Transp Policy 115:275–285. https://doi.org/10.1016/j.tranpol.2021.11.007
Morén B (2012) Utilizing problem specic structures in branch and bound methods for manpower planning. Linköping University
Mould GI (1996) Case study of manpower planning for clerical operations. J Oper Res Soc 47:358. https://doi.org/10.2307/3010578
Neto M (1987) Strategic human resources information system: a simulation model for the airline pilots labor market. Northwestern University
North M, Mccuan S, Mohammed T, et al (2019) A perspective exploration of the airline industry
Onifade M, Oluwaseyi JA, Oroye OA (2020) Modelling the manpower planning of Nigerian Port operations. J Acad Res Econ; 12
Orlady LM (2010) Airline pilot training today and tomorrow. Crew resource management. Elsevier, Amsterdam, pp 469–491
Qin Y, Ma HL, Chan FTS, Khan WA (2020) A scenario-based stochastic programming approach for aircraft expendable and rotable spare parts planning in MRO provider. Ind Manag Data Syst. https://doi.org/10.1108/IMDS-03-2020-0131
Sa CAA, Santos BF, Clarke JPB (2020) Portfolio-based airline fleet planning under stochastic demand. Omega (united Kingdom). https://doi.org/10.1016/j.omega.2019.08.008
Sohoni MG, Johnson EL, Bailey TG (2004) Long-range reserve crew manpower planning. Manage Sci 50:724–739. https://doi.org/10.1287/mnsc.1030.0141
Sohoni MG, Johnson EL, Bailey TG (2006) Operational airline reserve crew planning. J Sched 9:203–221. https://doi.org/10.1007/s10951-006-6778-8
Verbeek PJ (1991) Decision support systems—an application in strategic manpower planning of airline pilots. Eur J Oper Res 55:368–381. https://doi.org/10.1016/0377-2217(91)90206-B
Wang C, Clegg J, Kafouros M (2009) Country-of-origin effects of foreign direct investment: an industry level analysis. Manag Int Rev. https://doi.org/10.1007/s11575-008-0135-4
Wu WY, Liao YK (2014) A balanced scorecard envelopment approach to assess airlines’ performance. Ind Manag Data Syst. https://doi.org/10.1108/IMDS-03-2013-0135
Wu Y, Wu S, Hu X (2021) Multi-constrained cooperative path planning of multiple drones for persistent surveillance in urban environments. Complex Intell Syst. https://doi.org/10.1007/s40747-021-00300-5
Yu G, Pachon J, Thengvall B et al (2004) Optimizing pilot planning and training for continental airlines. Interfaces (providence) 34:253–264. https://doi.org/10.1287/inte.1040.0082
Yu M, Ding Y, Lindsey R, Shi C (2016) A data-driven approach to manpower planning at U.S.–Canada border crossings. Transp Res Part A Policy Pract 91:34–47. https://doi.org/10.1016/j.tra.2016.06.006
Zhang Y, Li Z, Jiao P, Zhu S (2021) Two-stage stochastic programming approach for limited medical reserves planning under uncertainties. Complex Intell Syst 7:3003–3013
Zhang H, Wu Y, Sun Z (2022) EHEFT-R: multi-objective task scheduling scheme in cloud computing. Complex Intell Syst. https://doi.org/10.1007/s40747-021-00479-7
Funding
This research is partially funded through Embry-Riddle Aeronautical University’s FIRST Grant # (000868-00001A).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Cankaya, B., Erenay, B., Kibis, E. et al. Charting the future of pilots: maximizing airline workforce efficiency through advanced analytics. Oper Res Int J 24, 50 (2024). https://doi.org/10.1007/s12351-024-00861-6
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12351-024-00861-6