TY - CHAP A1 - Prause, Felix T1 - A Multi-Swap Heuristic for Rolling Stock Rotation Planning with Predictive Maintenance T2 - Proceedings of the 11th International Network Optimization Conference (INOC), Dublin, Ireland, March 11-23, 2024 N2 - We present a heuristic solution approach for the rolling stock rotation problem with predictive maintenance (RSRP-PdM). The task of this problem is to assign a sequence of trips to each of the vehicles and to schedule their maintenance such that all trips can be operated. Here, the health states of the vehicles are considered to be random variables distributed by a family of probability distribution functions, and the maintenance services should be scheduled based on the failure probability of the vehicles. The proposed algorithm first generates a solution by solving an integer linear program and then heuristically improves this solution by applying a local search procedure. For this purpose, the trips assigned to the vehicles are split up and recombined, whereby additional deadhead trips can be inserted between the partial assignments. Subsequently, the maintenance is scheduled by solving a shortest path problem in a state-expanded version of a space-time graph restricted to the trips of the individual vehicles. The solution approach is tested and evaluated on a set of test instances based on real-world timetables. Y1 - 2024 U6 - https://doi.org/10.48786/inoc.2024.11 SP - 58 EP - 63 ER - TY - JOUR A1 - Prause, Felix A1 - Borndörfer, Ralf A1 - Grimm, Boris A1 - Tesch, Alexander T1 - Approximating rolling stock rotations with integrated predictive maintenance JF - Journal of Rail Transport Planning & Management N2 - We study the solution of the rolling stock rotation problem with predictive maintenance (RSRP-PdM) by an iterative refinement approach that is based on a state-expanded event-graph. In this graph, the states are parameters of a failure distribution, and paths correspond to vehicle rotations with associated health state approximations. An optimal set of paths including maintenance can be computed by solving an integer linear program. Afterwards, the graph is refined and the procedure repeated. An associated linear program gives rise to a lower bound that can be used to determine the solution quality. Computational results for six instances derived from real-world timetables of a German railway company are presented. The results show the effectiveness of the approach and the quality of the solutions. Y1 - 2024 U6 - https://doi.org/10.1016/j.jrtpm.2024.100434 VL - 30 SP - 100434 ER - TY - GEN A1 - Prause, Felix A1 - Borndörfer, Ralf T1 - Construction of a Test Library for the Rolling Stock Rotation Problem with Predictive Maintenance N2 - We describe the development of a test library for the rolling stock rotation problem with predictive maintenance (RSRP-PdM). Our approach involves the utilization of genuine timetables from a private German railroad company. The generated instances incorporate probability distribution functions for modeling the health states of the vehicles and the considered trips possess varying degradation functions. RSRP-PdM involves assigning trips to a fleet of vehicles and scheduling their maintenance based on their individual health states. The goal is to minimize the total costs consisting of operational costs and the expected costs associated with vehicle failures. The failure probability is dependent on the health states of the vehicles, which are assumed to be random variables distributed by a family of probability distributions. Each distribution is represented by the parameters characterizing it and during the operation of the trips, these parameters get altered. Our approach incorporates non-linear degradation functions to describe the inference of the parameters but also linear ones could be applied. The resulting instances consist of the timetables of the individual lines that use the same vehicle type. Overall, we employ these assumptions and utilize open-source data to create a library of instances with varying difficulty. Our approach is vital for evaluating and comparing algorithms designed to solve the RSRP-PdM. T3 - ZIB-Report - 23-20 KW - Rolling Stock Rotation Planning KW - Predictive Maintenance KW - Test Library Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-91734 SN - 1438-0064 ER - TY - CHAP A1 - Prause, Felix A1 - Borndörfer, Ralf T1 - A Bayesian Rolling Horizon Approach for Rolling Stock Rotation Planning with Predictive Maintenance T2 - 24th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2024) N2 - We consider the rolling stock rotation planning problem with predictive maintenance (RSRP-PdM), where a timetable given by a set of trips must be operated by a fleet of vehicles. Here, the health states of the vehicles are assumed to be random variables, and their maintenance schedule should be planned based on their predicted failure probabilities. Utilizing the Bayesian update step of the Kalman filter, we develop a rolling horizon approach for RSRP-PdM, in which the predicted health state distributions are updated as new data become available. This approach reduces the uncertainty of the health states and thus improves the decision-making basis for maintenance planning. To solve the instances, we employ a local neighborhood search, which is a modification of a heuristic for RSRP-PdM, and demonstrate its effectiveness. Using this solution algorithm, the presented approach is compared with the results of common maintenance strategies on test instances derived from real-world timetables. The obtained results show the benefits of the rolling horizon approach. Y1 - 2024 U6 - https://doi.org/10.4230/OASIcs.ATMOS.2024.13 VL - 123 SP - 13:1 EP - 13:19 ER - TY - JOUR A1 - Bestuzheva, Ksenia A1 - Chmiela, Antonia A1 - Müller, Benjamin A1 - Serrano, Felipe A1 - Vigerske, Stefan A1 - Wegscheider, Fabian T1 - Global Optimization of Mixed-Integer Nonlinear Programs with SCIP 8.0 JF - Journal of Global Optimization N2 - For over ten years, the constraint integer programming framework SCIP has been extended by capabilities for the solution of convex and nonconvex mixed-integer nonlinear programs (MINLPs). With the recently published version 8.0, these capabilities have been largely reworked and extended. This paper discusses the motivations for recent changes and provides an overview of features that are particular to MINLP solving in SCIP. Further, difficulties in benchmarking global MINLP solvers are discussed and a comparison with several state-of-the-art global MINLP solvers is provided. Y1 - 2023 U6 - https://doi.org/10.1007/s10898-023-01345-1 ER - TY - CHAP A1 - Chmiela, Antonia A1 - Gleixner, Ambros A1 - Lichocki, Pawel A1 - Pokutta, Sebastian T1 - Online Learning for Scheduling MIP Heuristics T2 - Integration of Constraint Programming, Artificial Intelligence, and Operations Research N2 - Mixed Integer Programming (MIP) is NP-hard, and yet modern solvers often solve large real-world problems within minutes. This success can partially be attributed to heuristics. Since their behavior is highly instance-dependent, relying on hard-coded rules derived from empirical testing on a large heterogeneous corpora of benchmark instances might lead to sub-optimal performance. In this work, we propose an online learning approach that adapts the application of heuristics towards the single instance at hand. We replace the commonly used static heuristic handling with an adaptive framework exploiting past observations about the heuristic’s behavior to make future decisions. In particular, we model the problem of controlling Large Neighborhood Search and Diving – two broad and complex classes of heuristics – as a multi-armed bandit problem. Going beyond existing work in the literature, we control two different classes of heuristics simultaneously by a single learning agent. We verify our approach numerically and show consistent node reductions over the MIPLIB 2017 Benchmark set. For harder instances that take at least 1000 seconds to solve, we observe a speedup of 4%. Y1 - 2023 U6 - https://doi.org/10.1007/978-3-031-33271-5_8 VL - 13884 SP - 114 EP - 123 PB - Springer, Cham ER - TY - CHAP A1 - Chmiela, Antonia A1 - Muñoz, Gonzalo A1 - Serrano, Felipe T1 - Monoidal strengthening and unique lifting in MIQCPs T2 - Integer Programming and Combinatorial Optimization. IPCO 2023. N2 - Using the recently proposed maximal quadratic-free sets and the well-known monoidal strengthening procedure, we show how to improve inter- section cuts for quadratically-constrained optimization problems by exploiting integrality requirements. We provide an explicit construction that allows an efficient implementation of the strengthened cuts along with computational results showing their improvements over the standard intersection cuts. We also show that, in our setting, there is unique lifting which implies that our strengthening procedure is generating the best possible cut coefficients for the integer variables. Y1 - 2023 U6 - https://doi.org/10.1007/978-3-031-32726-1_7 VL - 13904 SP - 87 EP - 99 ER - TY - JOUR A1 - Gorgulla, Christoph A1 - Garza, Alejandro J. A1 - Kapil, Venkat A1 - Fackeldey, Konstantin T1 - QUASAR: A Flexible QM-MM Method for Biomolecular Systems based on Restraining Spheres JF - Computer Physics Communications N2 - Quantum mechanical models of molecules theoretically offer unprecedented accuracy in predicting values associated with these systems, including the free energy of interaction between two molecules. However, high-accuracy quantum mechanical methods are computationally too expensive to be applied to larger systems, including most biomolecular systems such as proteins. To circumvent this challenge, the hybrid quantum mechanics/molecular mechanics (QM/MM) method was developed, allowing one to treat only the most important part of the system on the quantum mechanical level and the remaining part on the classical level. To date, QM/MM simulations for biomolecular systems have been carried out almost exclusively on the electronic structure level, neglecting nuclear quantum effects (NQEs). Yet NQEs can play a major role in biomolecular systems [1]. Here, we present i-QI, a QM/MM client for the path integral molecular dynamics (PIMD) software i-PI [2, 3, 4]. i-QI allows for carrying out QM/MM simulations simultaneously, allowing for the inclusion of electronic as well as nuclear quantum effects. i-QI implements a new QM/MM scheme based on constraining potentials called QUASAR, which allows handling diffusive systems, such as biomolecules solvated in water solvent. The QUASAR method is suitable in particular when the properties of interest are equilibrium properties, such as the free energy of binding. i-QI is freely available and open source, and we demonstrate it on a test system. KW - QM-MM Methods KW - Path Integral KW - biomolecular systems KW - CADD KW - Free Energy KW - Binding Y1 - 2025 U6 - https://doi.org/10.1016/j.cpc.2025.109949 SN - 0010-4655 VL - 320 ER - TY - GEN A1 - Bestuzheva, Ksenia A1 - Chmiela, Antonia A1 - Müller, Benjamin A1 - Serrano, Felipe A1 - Vigerske, Stefan A1 - Wegscheider, Fabian T1 - Global Optimization of Mixed-Integer Nonlinear Programs with SCIP 8.0 N2 - For over ten years, the constraint integer programming framework SCIP has been extended by capabilities for the solution of convex and nonconvex mixed-integer nonlinear programs (MINLPs). With the recently published version~8.0, these capabilities have been largely reworked and extended. This paper discusses the motivations for recent changes and provides an overview of features that are particular to MINLP solving in SCIP. Further, difficulties in benchmarking global MINLP solvers are discussed and a comparison with several state-of-the-art global MINLP solvers is provided. T3 - ZIB-Report - 23-01 Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-89348 SN - 1438-0064 ER -