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A hybrid scatter search heuristic for personalized crew rostering in the airline industry

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  • Maenhout, Broos
  • Vanhoucke, Mario

Abstract

The crew scheduling problem in the airline industry is extensively investigated in the operations research literature since efficient crew employment can drastically reduce operational costs of airline companies. Given the flight schedule of an airline company, crew scheduling is the process of assigning all necessary crew members in such a way that the airline is able to operate all its flights and constructing a roster line for each employee minimizing the corresponding overall cost for personnel. In this paper, we present a scatter search algorithm for the airline crew rostering problem. The objective is to assign a personalized roster to each crew member minimizing the overall operational costs while ensuring the social quality of the schedule. We combine different complementary meta-heuristic crew scheduling combination and improvement principles. Detailed computational experiments in a real-life problem environment are presented investigating all characteristics of the procedure. Moreover, we compare the proposed scatter search algorithm with optimal solutions obtained by an exact branch-and-price procedure and a steepest descent variable neighbourhood search.

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  • Maenhout, Broos & Vanhoucke, Mario, 2010. "A hybrid scatter search heuristic for personalized crew rostering in the airline industry," European Journal of Operational Research, Elsevier, vol. 206(1), pages 155-167, October.
    • Handle: RePEc:eee:ejores:v:206:y:2010:i:1:p:155-167
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    Cited by:

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    2. Mangesh Gharote & Rahul Patil & Sachin Lodha, 2017. "Scatter search for trainees to software project requirements stable allocation," Journal of Heuristics, Springer, vol. 23(4), pages 257-283, August.
    3. Van den Bergh, Jorne & Beliën, Jeroen & De Bruecker, Philippe & Demeulemeester, Erik & De Boeck, Liesje, 2013. "Personnel scheduling: A literature review," European Journal of Operational Research, Elsevier, vol. 226(3), pages 367-385.
    4. Nishi, Tatsushi & Sugiyama, Taichi & Inuiguchi, Masahiro, 2014. "Two-level decomposition algorithm for crew rostering problems with fair working condition," European Journal of Operational Research, Elsevier, vol. 237(2), pages 465-473.
    5. De Bruecker, Philippe & Van den Bergh, Jorne & Beliën, Jeroen & Demeulemeester, Erik, 2015. "Workforce planning incorporating skills: State of the art," European Journal of Operational Research, Elsevier, vol. 243(1), pages 1-16.
    6. Breugem, T. & Dollevoet, T.A.B. & Huisman, D., 2017. "Is Equality always desirable?," Econometric Institute Research Papers EI2017-30, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. Stefaan Haspeslagh & Patrick De Causmaecker & Andrea Schaerf & Martin Stølevik, 2014. "The first international nurse rostering competition 2010," Annals of Operations Research, Springer, vol. 218(1), pages 221-236, July.
    8. Doi, Tsubasa & Nishi, Tatsushi & Voß, Stefan, 2018. "Two-level decomposition-based matheuristic for airline crew rostering problems with fair working time," European Journal of Operational Research, Elsevier, vol. 267(2), pages 428-438.
    9. Vahid Zeighami & François Soumis, 2019. "Combining Benders’ Decomposition and Column Generation for Integrated Crew Pairing and Personalized Crew Assignment Problems," Transportation Science, INFORMS, vol. 53(5), pages 1479-1499, September.
    10. Lin, Shih-Wei & Ying, Kuo-Ching, 2014. "Minimizing shifts for personnel task scheduling problems: A three-phase algorithm," European Journal of Operational Research, Elsevier, vol. 237(1), pages 323-334.
    11. Jesica Armas & Luis Cadarso & Angel A. Juan & Javier Faulin, 2017. "A multi-start randomized heuristic for real-life crew rostering problems in airlines with work-balancing goals," Annals of Operations Research, Springer, vol. 258(2), pages 825-848, November.
    12. Fuentes, Manuel & Cadarso, Luis & Marín, Ángel, 2019. "A hybrid model for crew scheduling in rail rapid transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 125(C), pages 248-265.
    13. Mesquita, Marta & Moz, Margarida & Paias, Ana & Pato, Margarida, 2015. "A decompose-and-fix heuristic based on multi-commodity flow models for driver rostering with days-off pattern," European Journal of Operational Research, Elsevier, vol. 245(2), pages 423-437.
    14. Thomas Breugem & Twan Dollevoet & Dennis Huisman, 2022. "Is Equality Always Desirable? Analyzing the Trade-Off Between Fairness and Attractiveness in Crew Rostering," Management Science, INFORMS, vol. 68(4), pages 2619-2641, April.
    15. Julien Maheut & Jose P. Garcia-Sabater & Julio J. Garcia-Sabater & Sofia Garcia-Manglano, 2024. "Solving the multisite staff planning and scheduling problem in a sheltered employment centre that employs workers with intellectual disabilities by MILP: a Spanish case study," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 32(3), pages 569-591, September.
    16. Atoosa Kasirzadeh & Mohammed Saddoune & François Soumis, 2017. "Airline crew scheduling: models, algorithms, and data sets," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(2), pages 111-137, June.
    17. Hoong Chuin Lau & Zhi Yuan & Aldy Gunawan, 2016. "Patrol scheduling in urban rail network," Annals of Operations Research, Springer, vol. 239(1), pages 317-342, April.

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