Abstract
This article summarizes the findings of my Ph.D. thesis finished in 2015, whose topic are algorithmic approaches for the solution of network design problems. I focus on the results of a joint project with Deutsche Bahn AG on developing an optimal expansion strategy for the German railway network until 2030 to meet future demands. I have modelled this task as a multi-period network design problem and have derived an efficient decomposition approach to solve it. In a case study on real-world data on the German railway network, I demonstrate both the efficiency of by method as well as the high quality of the solutions it computes.
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References
Bärmann, A.: Solving network design problems via decomposition, aggregation and approximation—with an application to the optimal expansion of railway infrastructure. Ph.D. thesis, Friedrich-Alexander-Universität Erlangen-Nürnberg (2016)
Minoux, M.: Network synthesis and optimum network design problems: models, solution methods and applications. Networks 19(3), 313–360 (1989)
Bärmann, A., Martin, A., Schülldorf, H.: A decomposition method for multi-period railway network expansion—with a case study for Germany. Transp. Sci. (2015) (to appear)
DB Netz AG: Netzkonzeption 2030: Zielnetz der DB Netz AG für die Schieneninfrastruktur im Jahr 2030. Information booklet (2013)
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Bärmann, A. (2018). An Optimal Expansion Strategy for the German Railway Network Until 2030. In: Fink, A., Fügenschuh, A., Geiger, M. (eds) Operations Research Proceedings 2016. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-55702-1_1
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DOI: https://doi.org/10.1007/978-3-319-55702-1_1
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