A Stochastic Optimization Approach to Energy-Efficient Underground Timetabling Under Uncertain Dwell and Running Times

Gemander P, Bärmann A, Martin A (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Transportation Science INFORMS (Institute for Operations Research and Management Sciences)

Book Volume: 57

Pages Range: 1627-1645

Journal Issue: 6

DOI: 10.1287/trsc.2022.0269

Abstract

We consider a problem from the context of energy-efficient underground railway timetabling, in which an existing timetable draft is improved by slightly changing departure and running times. In practice, synchronization between accelerating and braking trains to utilize regenerative braking plays a major role for the energy efficiency of a timetable. Because deviations from a planned timetable may lead to unnecessarily high energy consumption during actual operation, we include operational uncertainties in our model to create a timetable that remains energy efficient, even if deviations from the nominal timetable occur. To solve the problem, we use a scenario expansion model in conjunction with a Benders decomposition approach. As an alternative to solving the Benders subproblems, we present a heuristic sparse cut that can be computed efficiently. The resulting sparse-cut heuristic produces high-quality solutions on a set of real-world instances stemming from the Nürnberg underground system, outperforming the integrated mixed-integer programming approach as well as the basic Benders approach. Additionally, we evaluate two static recovery strategies—shortening dwell times as well as shortening dwell and running times—to determine the cost and benefit of handling delays using a simple static rule. In our experiments, we are able to reduce the energy consumption by up to 9.4% and confirm that delay recovery via shortening dwell times is an energy-efficient and effective way to increase punctuality at low cost in terms of energy.

Authors with CRIS profile

Andreas Bärmann Economics - Discrete Optimization - Mathematics (EDOM) Alexander Martin Economics - Discrete Optimization - Mathematics (EDOM)

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Gemander, P., Bärmann, A., & Martin, A. (2023). A Stochastic Optimization Approach to Energy-Efficient Underground Timetabling Under Uncertain Dwell and Running Times. Transportation Science, 57(6), 1627-1645. https://doi.org/10.1287/trsc.2022.0269

MLA:

Gemander, Patrick, Andreas Bärmann, and Alexander Martin. "A Stochastic Optimization Approach to Energy-Efficient Underground Timetabling Under Uncertain Dwell and Running Times." Transportation Science 57.6 (2023): 1627-1645.

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