TY - GEN A1 - Löbel, Fabian A1 - Borndörfer, Ralf A1 - Weider, Steffen T1 - Non-Linear Battery Behavior in Electric Vehicle Scheduling Problems 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. T3 - ZIB-Report - 23-24 KW - Electric Vehicle Scheduling KW - Non-Linear Charging Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-92441 SN - 1438-0064 ER - TY - GEN A1 - Schlechte, Thomas A1 - Blome, Christian A1 - Gerber, Stefan A1 - Hauser, Stefan A1 - Kasten, Jens A1 - Müller, Gilbert A1 - Schulz, Christof A1 - Thüring, Michel A1 - Weider, Steffen T1 - The Bouquet of Features in Rolling Stock Rotation Planning T2 - Conference Proceedings RailBelgrade 2023 N2 - Rolling stock is one of the major assets for a railway transportation company. Hence, their utilization should be as efficiently and effectively as possible. Railway undertakings are facing rolling stock scheduling challenges in different forms - from rather idealized weekly strategic problems to very concrete operational ones. Thus, a vast of optimization models with different features and objectives exist. Thorlacius et al. (2015) provides a comprehensive and valuable collection on technical requirements, models, and methods considered in the scientific literature. We contribute with an update including recent works. The main focus of the paper is to present a classification and elaboration of the major features which our solver R-OPT is able to handle. Moreover, the basic optimization model and algorithmic ingredients of R-OPT are discussed. Finally, we present computational results for a cargo application at SBB CARGO AG and other railway undertakings for passenger traffic in Europe to show the capabilities of R-OPT. Y1 - 2023 UR - https://easychair.org/publications/preprint/Nms6 ER -