Share:


Control model for ground crew scheduling problem at small airports: case of Serbia

Abstract

Present-day airline industry is quite a competitive field and crew scheduling represents one of the crucial problems due to significant impact on the airline’s cost. The crew scheduling problem is based on the assignment of crew members to operate different tasks of route. The main goal of this paper is to provide an analysis and a solution to one of the biggest problems detected on a small airport in the Serbia - the problem of ground crew scheduling. The paper presents the main characteristics, goals and limitations of a real-life problem identified at this small airport. In order to solve the problem, we developed a dynamic discrete simulation model. The model is developed in a spreadsheet environment of Microsoft Excel. Some of the main limitations found in the development of the model are strong constraints and multiple goals. The model presented in the paper is designed as a useful management tool for smaller airports and is aimed at the improvement of operative processes.

Keyword : crew scheduling problem, modelling, air transport, small airport, management, spreadsheets

How to Cite
Đorđević Milutinović, L., Makajić-Nikolić, D., Antić, S., Živić, M., & Lisec, A. (2021). Control model for ground crew scheduling problem at small airports: case of Serbia. Transport, 36(3), 235-245. https://doi.org/10.3846/transport.2021.15369
Published in Issue
Sep 14, 2021
Abstract Views
635
PDF Downloads
522
Creative Commons License

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

References

Bach, L.; Dollevoet, T.; Huisman, D. 2016. Integrating timetabling and crew scheduling at a freight railway operator, Transportation Science 50(3): 878–891. https://doi.org/10.1287/trsc.2015.0648

Bazargan, M. 2010. Airline Operations and Scheduling. Routledge. 302 p. https://doi.org/10.4324/9781315566474

Boyer, V.; Ibarra-Rojas, O. J.; Ríos-Solís, Y. Á. 2018. Vehicle and crew scheduling for flexible bus transportation systems, Transportation Research Part B: Methodological 112: 216–229. https://doi.org/10.1016/j.trb.2018.04.008

Brusco, M. J.; Jacobs, L. W.; Bongiorno, R. J.; Lyons, D. V.; Tang, B. 1995. Improving personnel scheduling at airline stations, Operations Research 43(5): 741–751. https://doi.org/10.1287/opre.43.5.741

Chu, S. C. K. 2007. Generating, scheduling and rostering of shift crew-duties: applications at the Hong Kong International Airport, European Journal of Operational Research 177(3): 1764–1778. https://doi.org/10.1016/j.ejor.2005.10.008

Clausen, T. 2010. Airport Ground Staff Scheduling. PhD Thesis. Technical University of Denmark, Kongens Lyngby, Denmark. 231 p. Available from Internet: https://orbit.dtu.dk/en/publications/airport-ground-staff-scheduling

Duque, P. A. M.; Dolinskaya, I. S.; Sörensen, K. 2016. Network repair crew scheduling and routing for emergency relief distribution problem, European Journal of Operational Research 248(1): 272–285. https://doi.org/10.1016/j.ejor.2015.06.026

Giachetti, R. E.; Damodaran, P.; Mestry, S.; Prada, C. 2013. Optimization-based decision support system for crew scheduling in the cruise industry, Computers & Industrial Engineering 64(1): 500–510. https://doi.org/10.1016/j.cie.2012.08.011

Herbers, J. 2005. Models and Algorithms for Ground Staff Scheduling on Airports. Doctoral Dissertation. RWTH Aachen University, Aachen, Germany. 259 p. Available from Internet: http://publications.rwth-aachen.de/record/59558

IATA. 2019. International Air Transport Association. Available from Internet: https://www.iata.org

ICAO. 2019. International Civil Aviation Organization. Available from Internet: https://www.icao.int

Ivković, I.; Čokorilo, O.; Kaplanović, S. 2018. The estimation of GHG emission costs in road and air transport sector: case study of Serbia, Transport 33(1): 260–267. https://doi.org/10.3846/16484142.2016.1169557

Kasirzadeh, A.; Saddoune, M.; Soumis, F. 2017. Airline crew scheduling: models, algorithms, and data sets, EURO Journal on Transportation and Logistics 6(2): 111–137. https://doi.org/10.1007/s13676-015-0080-x

NCGA. 2019. Statistics. Niš Constantine the Great Airport (NCGA), Serbia. Available from Internet: http://nis-airport.com/en/traffic-figures

Oketch, C. A. 2013. Algorithm Optimization for Solving Crew Scheduling Problems. MSc Thesis. Open University of Catalonia, Barcelona, Catalonia, Spain. Available from Internet: http://hdl.handle.net/10609/19179

Pavlin, S.; Dimnik, I.; Starčević, M. 2007. Influence of low-cost carriers on airport infrastructure development, Promet – Traffic & Transportation 19(1): 49–54.

Pour, S. M.; Drake, J. H.; Ejlertsen, L. S.; Rasmussen, K. M.; Burke, E. K. 2018. A hybrid Constraint Programming/Mixed Integer Programming framework for the preventive signaling maintenance crew scheduling problem, European Journal of Operational Research 269(1): 341–352. https://doi.org/10.1016/j.ejor.2017.08.033

Rodič, B.; Baggia, A. 2017. Airport ground crew scheduling using heuristics and simulation, in M. Mujica Mota, I. Flores De La Mota (Eds.). Applied Simulation and Optimization: New Applications in Logistics, Industrial and Aeronautical Practice, 131–160. https://doi.org/10.1007/978-3-319-55810-3_5

Santosa, B.; Sunarto, A.; Rahman, A. 2010. Using differential evolution method to solve crew rostering problem, Applied Mathematics 1(4): 316–325. https://doi.org/10.4236/am.2010.14042

Soukour, A. A.; Devendeville, L.; Lucet, C.; Moukrim, A. 2013. A memetic algorithm for staff scheduling problem in airport security service, Expert Systems with Applications 40(18): 7504–7512. https://doi.org/10.1016/j.eswa.2013.06.073

Wang, Y.; Shang, Z.; Huisman, D.; D’Ariano, A.; Zhang, J. C. 2018. A Lagrangian Relaxation Approach Based on a Time-Space-State Network for Railway Crew Scheduling. Econometric Institute Report EI2018-45. Erasmus University Rotterdam, The Netherlands. 24 p. Available from Internet: https://repub.eur.nl/pub/114114

Yen, J. W.; Birge, J. R. 2006. A stochastic programming approach to the airline crew scheduling problem, Transportation Science 40(1): 3–14. https://doi.org/10.1287/trsc.1050.0138

Zhou, W.; Yang, X.; Deng, L.; Qin, J. 2016. Crew scheduling considering both crew duty time difference and cost on urban rail system, Promet – Traffic & Transportation 28(5): 449–460. https://doi.org/10.7307/ptt.v28i5.1842