A Decomposition Method for Multiperiod Railway Network Expansion—With a Case Study for Germany

Andreas Bärmann (), Alexander Martin () and Hanno Schülldorf ()
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Andreas Bärmann: Department Mathematik, Friedrich–Alexander–Universität Erlangen–Nürnberg, 91058 Erlangen, Germany
Alexander Martin: Department Mathematik, Friedrich–Alexander–Universität Erlangen–Nürnberg, 91058 Erlangen, Germany
Hanno Schülldorf: DB Analytics, Deutsche Bahn AG, 60329 Frankfurt am Main, Germany

Transportation Science, 2017, vol. 51, issue 4, 1102-1121

Abstract: In this work, we report about the results of a joint research project between Friedrich–Alexander–Universität Erlangen–Nürnberg and Deutsche Bahn AG on the optimal expansion of the German railway network until 2030. The need to increase the throughput of the network is given by company-internal demand forecasts that indicate an increase in rail freight traffic of about 50% over the next two decades. Our focus is to compute an optimal investment strategy into line capacities given an available annual budget, i.e., we are to choose the most profitable lines to upgrade with respect to the demand scenario under consideration and to provide a schedule according to which the chosen measures are implemented. This leads to a multiperiod network design problem—a class of problems that has received increasing interest over the past decade. We develop a mixed-integer programming formulation to model the situation and solve it via a novel decomposition approach that we call multiple-knapsack decomposition. The method can both be used as a quick heuristic and allows for the extension to an exact algorithm for the problem. We demonstrate its potential by solving a real-world problem instance provided by Deutsche Bahn AG and use the results as the basis for a broad case study for the expansion of the German railway network until 2030.

Keywords: railway transport; multiperiod network design; decomposition; infrastructure planning; mixed-integer programming (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:51:y:2017:i:4:p:1102-1121

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