Simon Girardin
Thomas Hanne
ABSTRACT
The optimization problem of assigning train staff to scheduled train services is called the train staff planning problem. A part of this is the rostering with the aim to create a duty timetable under the consideration of different constraints, preferences etc. The problem is formulated as a biobjective problem considering costs and penalties for violating constraints. In this paper, we analyze the application of the nondominated sorting genetic algorithm II (NSGA-II) for multiobjective optimization in order to propose a solution to the considered train staff planning problem. Numerical experiments are conducted using several example problems. These experiments provide suitable parameters for using NSGA-II and further insights into the adaptation of this algorithm to the problem under consideration.
- R. Dornberger, L. Frey, and T. Hanne. 2008. Single and multiobjective optimization of the train staff planning problem using genetic algorithms. In Proceedings of the 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). IEEE, 970–977.Google Scholar
- A. Caprara, L. Kroon, M. Monaci, M. Peeters, and P. Toth. 2007. Passenger railway optimization. In Handbooks in Operations Research and Management Science 14, 129-187.Google ScholarCross Ref
- J. Heil, K. Hoffmann, and U. Buscher. 2020. Railway crew scheduling: Models, methods and applications. European Journal of Operational Research, 283, 2, 405-425.Google ScholarCross Ref
- N. Bojovic and M. Milenkovic. 2010. Train driver rostering optimization. In Proceedings of the IEEE 8th International Symposium on Intelligent Systems and Informatics. IEEE, 501–504.Google Scholar
- M. Lezaun, G. Pérez, and E. S. De La Maza. 2006. Crew rostering problem in a public transport company. Journal of the Operational Research Society, 57, 10, 1173-1179.Google ScholarCross Ref
- T. Hanne, R. Dornberger, and L. Frey. 2009. Multiobjective and preference-based decision support for rail crew rostering. In Proceedings of the 2009 IEEE Congress on Evolutionary Computation. IEEE, 990–996.Google Scholar
- A. Ernst, H. Jiang, M. Krishnamoorthy, H. Nott, and D. Sier. 2001. Rail crew scheduling and rostering optimization algorithms. In Computer-Aided Scheduling of Public Transport. Springer, Berlin, Heidelberg, 53-71.Google Scholar
- L. Xie. 2013. Metaheuristics approach for solving multi-objective crew rostering problem in public transit. Working paper. University of Paderborn.Google Scholar
- C. H. Chen, T. K. Liu, J. H. Chou, and C. C. Wang. 2012. Multiobjective airline scheduling: An integrated approach. In 2012 Proceedings of SICE Annual Conference (SICE). IEEE, 1266–1270.Google Scholar
- H. Habibnejad-Ledari, M. Rabbani, and N. Ghorbani-Kutenaie. 2019. Solving a multi-objective model toward home care staff planning considering cross-training and staff preferences by NSGA-II and NRGA. Scientia Iranica, 26, 5, 2919-2935.Google Scholar
- K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6, 2, 182-197.Google ScholarDigital Library
- K. Hoffmann. 2017. A hybrid solution approach for railway crew scheduling problems with attendance rates. In Operations Research Proceedings 2015. Springer, Cham, 243–250.Google Scholar
- A. Debabrata. 2019. An implementation of NSGA-II in Java. GitHub repository, https://github.com/onclave/NSGA-IIGoogle Scholar
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