How to cite this paper
Chutima, P & Krisanaphan, N. (2022). Cockpit crew pairing Pareto optimisation in a budget airline.International Journal of Industrial Engineering Computations , 13(1), 67-80.
Refrences
Aggarwal, D., Saxena, D. K., Back, T., & Emmerich, M. (2020). Real-World Airline Crew Pairing Optimization: Customized Genetic Algorithm versus Column Generation Method. arXiv preprint arXiv:2003.03792.
AhmadBeygi, S., Cohn, A., & Weir, M. (2009). An integer programming approach to generating airline crew pairings. Computers & Operations Research, 36(4), 1284-1298.
Anbil, R., Forrest, J. J., & Pulleyblank, W. R. (1998). Column generation and the airline crew pairing problem. Documenta Mathematica, 3(1), 677-686.
Anbil, R., Gelman, E., Patty, B., & Tanga, R. (1991). Recent advances in crew-pairing optimization at American Airlines. Interfaces, 21(1), 62-74.
Asefi, H., Jolai, F., Rabiee, M., & Araghi, M. T. (2014). A hybrid NSGA-II and VNS for solving a bi-objective no-wait flexible flowshop scheduling problem. The International Journal of Advanced Manufacturing Technology, 75(5-8), 1017-1033.
Aydemir-Karadag, A., Dengiz, B., & Bolat, A. (2013). Crew pairing optimization based on hybrid approaches. Computers & Industrial Engineering, 65(1), 87-96.
Barnhart, C., Hatay, L., & Johnson, E. L. (1995). Deadhead selection for the long-haul crew pairing problem. Operations Research, 43(3), 491-499.
Barnhart, C., & Talluri, K. T. (1997). Airline operations research. Design and operation of civil and environmental engineering systems. Ed. Revelle, C. and McGarity, A. E., 435-469. New York, Wiley.
Chandrasekar, K., & Ramana, N. (2012). Performance comparison of GA, DE, PSO and SA approaches in enhancement of total transfer capability using FACTS devices. Journal of Electrical Engineering and Technology, 7(4), 493-500.
Chutima, P., & Kirdphoksap, T. (2019). Solving Many-Objective Car Sequencing Problems on Two-Sided Assembly Lines Using an Adaptive Differential Evolutionary Algorithm. Engineering Journal, 23(4), 121-156.
Chutima, P., & Olarnviwatchai, S. (2018). A multi-objective car sequencing problem on two-sided assembly lines. Journal of Intelligent Manufacturing, 29(7), 1617-1636.
Deb, K., & Jain, H. (2013). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577-601.
Demirel, N. Ç., & Deveci, M. (2017). Novel search space updating heuristics-based genetic algorithm for optimizing medium-scale airline crew pairing problems. International Journal of Computational Intelligence Systems, 10(1), 1082-1101.
Desaulniers, G., Desrosiers, J., Dumas, Y., Marc, S., Rioux, B., Solomon, M. M., & Soumis, F. (1997). Crew pairing at Airfrance. European Journal of Operational Research, 97(2), 245-259.
Deveci, M., & Demirel, N. C. (2018). Evolutionary algorithms for solving the airline crew pairing problem. Computers & Industrial Engineering, 115, 389-406.
Gopalakrishnan, B., & Johnson, E. L. (2005). Airline crew scheduling: state-of-the-art. Annals of Operations Research, 140(1), 305-337.
Graves, G. W., McBride, R. D., Gershkoff, I., Anderson, D., & Mahidhara, D. (1993). Flight crew scheduling. Management Science, 39(6), 736-745.
Ibrahim, A., Rahnamayan, S., Martin, M. V., & Deb, K. (2016, July). EliteNSGA-III: An improved evolutionary many-objective optimization algorithm. In 2016 IEEE Congress on Evolutionary Computation (CEC) (pp. 973-982). IEEE.
Jiang, S., Ong, Y. S., Zhang, J., & Feng, L. (2014). Consistencies and contradictions of performance metrics in multiobjective optimization. IEEE Transactions on Cybernetics, 44(12), 2391-2404.
Klabjan, D., Johnson, E. L., Nemhauser, G. L., Gelman, E., & Ramaswamy, S. (2001). Solving large airline crew scheduling problems: Random pairing generation and strong branching. Computational Optimization and Applications 20(1), 73-91.
Lavoie, S., Minoux, M., & Odier, E. (1988). A new approach for crew pairing problems by column generation with an application to air transportation. European Journal of Operational Research, 35(1), 45-58.
Li, H., & Zhang, Q. (2009). Multi-objective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation, 13(2), 284-302.
Liu, T. K., Chen, C. H., Chou, J. H., Chen, S. H., & Chou, T. Y. (2009, August). Application of multiobjective genetic algorithms for optimizing aircraft crew pairing problems. In 2009 ICCAS-SICE (pp. 3748-3753). IEEE.
Liu, Q., Li, X., Liu, H., & Guo, Z. (2020). Multi-objective metaheuristics for discrete optimization problems: A review of the state-of-the-art. Applied Soft Computing, 106382.
Lu, C., Gao, L., Pan, Q., Li, X., & Zheng, J. (2019). A multi-objective cellular grey wolf optimizer for hybrid flowshop scheduling problem considering noise pollution. Applied Soft Computing, 75, 728-749.
Montgomery, D. C. (2017). Design and analysis of experiments. John Wiley & Sons.
Price, K., Storn, R. M., & Lampinen, J. A. (2006). Differential evolution: a practical approach to global optimization. Springer Science & Business Media.
Reisi, N. M., & Moslehi, G. (2013). Cockpit crew pairing problem in airline scheduling: Shortest path with resources constraints approach. International Journal of Industrial Engineering and Production Research, 24(4), 259-268.
Souai, N., & Teghem, J. (2009). Genetic algorithm-based approach for the integrated airline crew-pairing and rostering problem. European Journal of Operational Research, 199(3), 674-683.
Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341-359.
Xue, F., Sanderson, A. C., & Graves, R. J. (2003, December). Pareto-based multi-objective differential evolution. In The 2003 Congress on Evolutionary Computation, 2003. CEC'03. (Vol. 2, pp. 862-869). IEEE.
Zade, A. E., Sadegheih, A., & Lotfi, M. M. (2014). A modified NSGA-II solution for a new multi-objective hub maximal covering problem under uncertain shipments. Journal of Industrial Engineering International, 10(4), 185-197.
Zeren, B., & Özkol, İ. (2012). An improved genetic algorithm for crew pairing optimization. Journal of Intelligent Learning Systems and Applications, 4(1), 70-80.
Zeren, B., & Özkol, I. (2016). A novel column generation strategy for large scale airline crew pairing problems. Expert Systems with Applications, 55, 133-144.
Zhang, Q., & Li, H. (2007). MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712-731.
AhmadBeygi, S., Cohn, A., & Weir, M. (2009). An integer programming approach to generating airline crew pairings. Computers & Operations Research, 36(4), 1284-1298.
Anbil, R., Forrest, J. J., & Pulleyblank, W. R. (1998). Column generation and the airline crew pairing problem. Documenta Mathematica, 3(1), 677-686.
Anbil, R., Gelman, E., Patty, B., & Tanga, R. (1991). Recent advances in crew-pairing optimization at American Airlines. Interfaces, 21(1), 62-74.
Asefi, H., Jolai, F., Rabiee, M., & Araghi, M. T. (2014). A hybrid NSGA-II and VNS for solving a bi-objective no-wait flexible flowshop scheduling problem. The International Journal of Advanced Manufacturing Technology, 75(5-8), 1017-1033.
Aydemir-Karadag, A., Dengiz, B., & Bolat, A. (2013). Crew pairing optimization based on hybrid approaches. Computers & Industrial Engineering, 65(1), 87-96.
Barnhart, C., Hatay, L., & Johnson, E. L. (1995). Deadhead selection for the long-haul crew pairing problem. Operations Research, 43(3), 491-499.
Barnhart, C., & Talluri, K. T. (1997). Airline operations research. Design and operation of civil and environmental engineering systems. Ed. Revelle, C. and McGarity, A. E., 435-469. New York, Wiley.
Chandrasekar, K., & Ramana, N. (2012). Performance comparison of GA, DE, PSO and SA approaches in enhancement of total transfer capability using FACTS devices. Journal of Electrical Engineering and Technology, 7(4), 493-500.
Chutima, P., & Kirdphoksap, T. (2019). Solving Many-Objective Car Sequencing Problems on Two-Sided Assembly Lines Using an Adaptive Differential Evolutionary Algorithm. Engineering Journal, 23(4), 121-156.
Chutima, P., & Olarnviwatchai, S. (2018). A multi-objective car sequencing problem on two-sided assembly lines. Journal of Intelligent Manufacturing, 29(7), 1617-1636.
Deb, K., & Jain, H. (2013). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577-601.
Demirel, N. Ç., & Deveci, M. (2017). Novel search space updating heuristics-based genetic algorithm for optimizing medium-scale airline crew pairing problems. International Journal of Computational Intelligence Systems, 10(1), 1082-1101.
Desaulniers, G., Desrosiers, J., Dumas, Y., Marc, S., Rioux, B., Solomon, M. M., & Soumis, F. (1997). Crew pairing at Airfrance. European Journal of Operational Research, 97(2), 245-259.
Deveci, M., & Demirel, N. C. (2018). Evolutionary algorithms for solving the airline crew pairing problem. Computers & Industrial Engineering, 115, 389-406.
Gopalakrishnan, B., & Johnson, E. L. (2005). Airline crew scheduling: state-of-the-art. Annals of Operations Research, 140(1), 305-337.
Graves, G. W., McBride, R. D., Gershkoff, I., Anderson, D., & Mahidhara, D. (1993). Flight crew scheduling. Management Science, 39(6), 736-745.
Ibrahim, A., Rahnamayan, S., Martin, M. V., & Deb, K. (2016, July). EliteNSGA-III: An improved evolutionary many-objective optimization algorithm. In 2016 IEEE Congress on Evolutionary Computation (CEC) (pp. 973-982). IEEE.
Jiang, S., Ong, Y. S., Zhang, J., & Feng, L. (2014). Consistencies and contradictions of performance metrics in multiobjective optimization. IEEE Transactions on Cybernetics, 44(12), 2391-2404.
Klabjan, D., Johnson, E. L., Nemhauser, G. L., Gelman, E., & Ramaswamy, S. (2001). Solving large airline crew scheduling problems: Random pairing generation and strong branching. Computational Optimization and Applications 20(1), 73-91.
Lavoie, S., Minoux, M., & Odier, E. (1988). A new approach for crew pairing problems by column generation with an application to air transportation. European Journal of Operational Research, 35(1), 45-58.
Li, H., & Zhang, Q. (2009). Multi-objective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation, 13(2), 284-302.
Liu, T. K., Chen, C. H., Chou, J. H., Chen, S. H., & Chou, T. Y. (2009, August). Application of multiobjective genetic algorithms for optimizing aircraft crew pairing problems. In 2009 ICCAS-SICE (pp. 3748-3753). IEEE.
Liu, Q., Li, X., Liu, H., & Guo, Z. (2020). Multi-objective metaheuristics for discrete optimization problems: A review of the state-of-the-art. Applied Soft Computing, 106382.
Lu, C., Gao, L., Pan, Q., Li, X., & Zheng, J. (2019). A multi-objective cellular grey wolf optimizer for hybrid flowshop scheduling problem considering noise pollution. Applied Soft Computing, 75, 728-749.
Montgomery, D. C. (2017). Design and analysis of experiments. John Wiley & Sons.
Price, K., Storn, R. M., & Lampinen, J. A. (2006). Differential evolution: a practical approach to global optimization. Springer Science & Business Media.
Reisi, N. M., & Moslehi, G. (2013). Cockpit crew pairing problem in airline scheduling: Shortest path with resources constraints approach. International Journal of Industrial Engineering and Production Research, 24(4), 259-268.
Souai, N., & Teghem, J. (2009). Genetic algorithm-based approach for the integrated airline crew-pairing and rostering problem. European Journal of Operational Research, 199(3), 674-683.
Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341-359.
Xue, F., Sanderson, A. C., & Graves, R. J. (2003, December). Pareto-based multi-objective differential evolution. In The 2003 Congress on Evolutionary Computation, 2003. CEC'03. (Vol. 2, pp. 862-869). IEEE.
Zade, A. E., Sadegheih, A., & Lotfi, M. M. (2014). A modified NSGA-II solution for a new multi-objective hub maximal covering problem under uncertain shipments. Journal of Industrial Engineering International, 10(4), 185-197.
Zeren, B., & Özkol, İ. (2012). An improved genetic algorithm for crew pairing optimization. Journal of Intelligent Learning Systems and Applications, 4(1), 70-80.
Zeren, B., & Özkol, I. (2016). A novel column generation strategy for large scale airline crew pairing problems. Expert Systems with Applications, 55, 133-144.
Zhang, Q., & Li, H. (2007). MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712-731.