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Simulated annealing for the multi-objective aircrew rostering problem

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  • Lucic, Panta
  • Teodorovic, Dusan

Abstract

The aircrew rostering problem entails the assignment of crew members to planned rotations. Airline companies have the monthly task of constructing personalized monthly schedules (rosters). The paper attempts to solve the aircrew rostering problem as a multi-objective optimization problem, thus constructing personalized monthly schedules on the basis of several criteria. The proposed algorithm to solve the aircrew rostering problem contains two steps. The first step uses the 'pilot-by-pilot' heuristic algorithm to generate an initial feasible solution. The second step uses the simulated annealing technique for multi-objective optimization problems to improve the solution obtained in the first step. The paper develops two models to improve the initial solution obtained in the first step. The developed models are tested on a numerical example whose dimensions are characteristic of small- and medium-sized airline carriers. The other examples on which the models are tested are randomly generated. ©

Suggested Citation

  • Lucic, Panta & Teodorovic, Dusan, 1999. "Simulated annealing for the multi-objective aircrew rostering problem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(1), pages 19-45, January.
    • Handle: RePEc:eee:transa:v:33:y:1999:i:1:p:19-45
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    References listed on IDEAS

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    1. Bernardo Nicoletti, 1975. "Automatic Crew Rostering," Transportation Science, INFORMS, vol. 9(1), pages 33-42, February.
    2. Eglese, R. W., 1990. "Simulated annealing: A tool for operational research," European Journal of Operational Research, Elsevier, vol. 46(3), pages 271-281, June.
    3. Michael Ball & Anito Roberts, 1985. "A Graph Partitioning Approach to Airline Crew Scheduling," Transportation Science, INFORMS, vol. 19(2), pages 107-126, May.
    4. J. P. Arabeyre & J. Fearnley & F. C. Steiger & W. Teather, 1969. "The Airline Crew Scheduling Problem: A Survey," Transportation Science, INFORMS, vol. 3(2), pages 140-163, May.
    5. Jerrold Rubin, 1973. "A Technique for the Solution of Massive Set Covering Problems, with Application to Airline Crew Scheduling," Transportation Science, INFORMS, vol. 7(1), pages 34-48, February.
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    Cited by:

    1. Huseyin Onur Mete & Zelda B. Zabinsky, 2014. "Multiobjective Interacting Particle Algorithm for Global Optimization," INFORMS Journal on Computing, INFORMS, vol. 26(3), pages 500-513, August.
    2. Salazar-González, Juan-José, 2014. "Approaches to solve the fleet-assignment, aircraft-routing, crew-pairing and crew-rostering problems of a regional carrier," Omega, Elsevier, vol. 43(C), pages 71-82.
    3. Yan, Shangyao & Yang, Ta-Hui & Chen, Hsuan-Hung, 2004. "Airline short-term maintenance manpower supply planning," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(9-10), pages 615-642.
    4. 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.
    5. Adam Farmer & Jeffrey S. Smith & Luke T. Miller, 2007. "Scheduling Umpire Crews for Professional Tennis Tournaments," Interfaces, INFORMS, vol. 37(2), pages 187-196, April.
    6. B Suman & P Kumar, 2006. "A survey of simulated annealing as a tool for single and multiobjective optimization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(10), pages 1143-1160, October.
    7. Ernst, A. T. & Jiang, H. & Krishnamoorthy, M. & Sier, D., 2004. "Staff scheduling and rostering: A review of applications, methods and models," European Journal of Operational Research, Elsevier, vol. 153(1), pages 3-27, February.
    8. 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.
    9. Jones, D. F. & Mirrazavi, S. K. & Tamiz, M., 2002. "Multi-objective meta-heuristics: An overview of the current state-of-the-art," European Journal of Operational Research, Elsevier, vol. 137(1), pages 1-9, February.
    10. Eltoukhy, Abdelrahman E.E. & Wang, Z.X. & Chan, Felix T.S. & Fu, X., 2019. "Data analytics in managing aircraft routing and maintenance staffing with price competition by a Stackelberg-Nash game model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 143-168.
    11. Margarida Moz & Ana Respício & Margarida Vaz Pato, 2009. "Bi-objective evolutionary heuristics for bus driver rostering," Public Transport, Springer, vol. 1(3), pages 189-210, August.

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