TY - CHAP A1 - Borndörfer, Ralf A1 - Löbel, Andreas A1 - Löbel, Fabian A1 - Weider, Steffen T1 - Solving the Electric Bus Scheduling Problem by an Integrated Flow and Set Partitioning Approach T2 - 24th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2024) N2 - Attractive and cost-efficient public transport requires solving computationally difficult optimization problems from network design to crew rostering. While great progress has been made in many areas, new requirements to handle increasingly complex constraints are constantly coming up. One such challenge is a new type of resource constraints that are used to deal with the state-of-charge of battery-electric vehicles, which have limited driving ranges and need to be recharged in-service. Resource constrained vehicle scheduling problems can classically be modelled in terms of either a resource constrained (multi-commodity) flow problem or in terms of a path-based set partition problem. We demonstrate how a novel integrated version of both formulations can be leveraged to solve resource constrained vehicle scheduling with replenishment in general and the electric bus scheduling problem in particular by Lagrangian relaxation and the proximal bundle method. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0030-drops-211992 VL - 123 SP - 11:1 EP - 11:16 PB - Schloss Dagstuhl -- Leibniz-Zentrum für Informatik CY - Dagstuhl, Germany ER - TY - CHAP A1 - Löbel, Fabian A1 - Lindner, Niels ED - Sauer, Jonas ED - Schmidt, Marie T1 - A Geometric Approach to Integrated Periodic Timetabling and Passenger Routing T2 - 25th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2025) N2 - We offer a geometric perspective on the problem of integrated periodic timetabling and passenger routing in public transport. Inside the space of periodic tensions, we single out those regions, where the same set of paths provides shortest passenger routes. This results in a polyhedral subdivision, which we combine with the known decomposition by polytropes. On each maximal region of the common refinement, the integrated problem is solvable in polynomial time. We transform these insights into a new geometry-driven primal heuristic, integrated tropical neighborhood search (ITNS). Computationally, we compare implementations of ITNS and the integrated (restricted) modulo network simplex algorithm on the TimPassLib benchmark set, and contribute better solutions in terms of total travel time for all but one of the twenty-five instances for which a proven optimal solution is not yet known. KW - Periodic Timetabling KW - Passenger Routing KW - Polyhedral Complexes Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0030-drops-247580 SN - 978-3-95977-404-8 SN - 2190-6807 VL - 137 SP - 2:1 EP - 2:19 PB - Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik CY - Dagstuhl, Germany ER - TY - CHAP A1 - Löbel, Fabian A1 - Borndörfer, Ralf A1 - Weider, Steffen ED - Voigt, Guido ED - Fliedner, Malte ED - Haase, Knut ED - Brüggermann, Wolfgang ED - Hoberg, Kai ED - Meissner, Joern T1 - Non-linear Battery Behavior in Electric Vehicle Scheduling Problems T2 - Operations Research Proceedings 2023. OR 2023. N2 - The currently most popular approach to handle non-linear battery behavior for electric vehicle scheduling is to use a linear spline interpolation of the charge curve. We show that this can lead to approximate models that underestimate the charge duration and overestimate the state of charge, which is not desirable. While the error is of second order with respect to the interpolation step size, the associated mixed-integer linear programs do not scale well with the number of spline segments. It is therefore recommendable to use coarse interpolation grids adapted to the curvature of the charge curve, and to include sufficient safety margins to ensure solutions of approximate models remain feasible subjected to the exact charge curve. KW - Electric Vehicle Scheduling KW - Non-Linear Charging Y1 - 2025 SN - 9783031584046 U6 - https://doi.org/10.1007/978-3-031-58405-3_53 SN - 2731-040X SP - 415 EP - 421 PB - Springer Nature Switzerland CY - Cham ER - TY - CHAP A1 - Borndörfer, Ralf A1 - Hoppmann, Heide A1 - Karbstein, Marika A1 - Löbel, Fabian T1 - The Modulo Network Simplex with Integrated Passenger Routing T2 - Operations Research Proceedings 2016 N2 - Periodic timetabling is an important strategic planning problem in public transport. The task is to determine periodic arrival and departure times of the lines in a given network, minimizing the travel time of the passengers. We extend the modulo network simplex method, a well-established heuristic for the periodic timetabling problem, by integrating a passenger (re)routing step into the pivot operations. Computations on real-world networks show that we can indeed find timetables with much shorter total travel time, when we take the passengers' travel paths into consideration. Y1 - 2016 ER - TY - CHAP A1 - Löbel, Fabian A1 - Borndörfer, Ralf A1 - Weider, Steffen T1 - Non-Linear Charge Functions for Electric Vehicle Scheduling with Dynamic Recharge Rates T2 - 23rd Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2023) N2 - The ongoing electrification of logistics systems and vehicle fleets increases the complexity of associated vehicle routing or scheduling problems. Battery-powered vehicles have to be scheduled to recharge in-service, and the relationship between charging time and replenished driving range is non-linear. In order to access the powerful toolkit offered by mixed-integer and linear programming techniques, this battery behavior has to be linearized. Moreover, as electric fleets grow, power draw peaks have to be avoided to save on electricity costs or to adhere to hard grid capacity limits, such that it becomes desirable to keep recharge rates dynamic. We suggest a novel linearization approach of battery charging behavior for vehicle scheduling problems, in which the recharge rates are optimization variables and not model parameters. Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0030-drops-187765 VL - 115 SP - 15:1 EP - 15:6 ER -