@misc{LoebelBorndoerferWeider, author = {L{\"o}bel, Fabian and Bornd{\"o}rfer, Ralf and Weider, Steffen}, title = {Non-Linear Battery Behavior in Electric Vehicle Scheduling Problems}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-92441}, abstract = {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.}, language = {en} } @misc{SchlechteBlomeGerberetal., author = {Schlechte, Thomas and Blome, Christian and Gerber, Stefan and Hauser, Stefan and Kasten, Jens and M{\"u}ller, Gilbert and Schulz, Christof and Th{\"u}ring, Michel and Weider, Steffen}, title = {The Bouquet of Features in Rolling Stock Rotation Planning}, series = {Conference Proceedings RailBelgrade 2023}, journal = {Conference Proceedings RailBelgrade 2023}, abstract = {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.}, language = {en} }