TY - CHAP A1 - Borndörfer, Ralf A1 - Reuther, Markus A1 - Schlechte, Thomas A1 - Schulz, Christof A1 - Swarat, Elmar A1 - Weider, Steffen T1 - Duty Rostering in Public Transport - Facing Preferences, Fairness, and Fatigue T2 - Proceedings of Conference on Advanced Systems in Public Transport 2015 (CASPT2015) N2 - Duty rostering problems occur in different application contexts and come in different flavors. They give rise to very large scale integer programs which ypically have lots of solutions and extremely fractional LP relaxations. In such a situation, heuristics can be a viable algorithmic choice. We propose an mprovement method of the Lin-Kernighan type for the solution of duty rostering problems. We illustrate its versatility and solution quality on three different applications in public transit, vehicle routing, and airline rostering with a focus on the management of preferences, fairness, and fatigue, respectively. Y1 - 2015 ER - TY - CHAP A1 - Borndörfer, Ralf A1 - Hoppmann, Heide A1 - Karbstein, Marika T1 - Umsteigen ohne Warten T2 - HEUREKA 2017 N2 - Wir stellen einen mathematischen Optimierungsansatz zur Berechnung von periodischen Taktfahrplänen vor, bei dem die Umsteigezeiten unter Berücksichtigung des Passagierverhaltens minimiert werden. Wir untersuchen damit den Einfluss wichtiger Systemparameter und Verhaltensmuster auf die Beförderungsqualität. Y1 - 2017 ER - TY - CHAP A1 - Vierhaus, Ingmar A1 - Fügenschuh, Armin T1 - Global and Local Optimal Control of a Resource Utilization Problem T2 - Proceedings of the 33rd International Conference of the System Dynamics Society N2 - System Dynamic models describe physical, technical, economical, or social systems using differential and algebraic equations. In their purest form, these models are intended to describe the evolution of a system from a given initial state. In many applications, it is possible to intervene with the system in order to obtain a desired dynamic or a certain outcome in the end. On the mathematical side, this leads to control problems, where aside from the simulation one has to find optimal intervention functions over time that maximize a specific objective function. Using a dynamical model for the utilization of a natural nonrenewable resource of Behrens as a demonstrator example, we present two main mathematical solution strategies. They are distinguished by the quality certificate on their respective solution: one leads to proven local optimal solution, and the other technique yields proven global optimal solutions. We present implementational and numerical issues, and a comparison of both methods. Y1 - 2015 ER - TY - CHAP A1 - Hiller, Benjamin A1 - Saitenmacher, René A1 - Walther, Tom T1 - Analysis of operating modes of complex compressor stations T2 - Proceedings of Operations Research 2016 N2 - We consider the modeling of operation modes for complex compressor stations (i.e., ones with several in- or outlets) in gas networks. In particular, we propose a refined model that allows to precompute tighter relaxations for each operation mode. These relaxations may be used to strengthen the compressor station submodels in gas network optimization problems. We provide a procedure to obtain the refined model from the input data for the original model. Y1 - 2017 U6 - https://doi.org/10.1007/978-3-319-55702-1_34 SP - 251 EP - 257 ER - TY - CHAP A1 - Shinano, Yuji A1 - Achterberg, Tobias A1 - Berthold, Timo A1 - Heinz, Stefan A1 - Koch, Thorsten A1 - Winkler, Michael ED - IEEE, T1 - Solving Hard MIPLIP2003 Problems with ParaSCIP on Supercomputers: An Update T2 - IPDPSW'14 Proceedings of the 2014 IEEE, International Parallel & Distributed Processing Symposium Workshops Y1 - 2014 SN - 978-1-4799-4117-9 U6 - https://doi.org/10.1109/IPDPSW.2014.174 SP - 1552 EP - 1561 PB - IEEE Computer Society CY - Washington, DC, USA ER - TY - CHAP A1 - Gamrath, Gerald A1 - Hiller, Benjamin A1 - Witzig, Jakob T1 - Reoptimization Techniques in MIP Solvers N2 - Recently, there have been many successful applications of optimization algorithms that solve a sequence of quite similar mixed-integer programs (MIPs) as subproblems. Traditionally, each problem in the sequence is solved from scratch. In this paper we consider reoptimization techniques that try to benefit from information obtained by solving previous problems of the sequence. We focus on the case that subsequent MIPs differ only in the objective function or that the feasible region is reduced. We propose extensions of the very complex branch-and-bound algorithms employed by general MIP solvers based on the idea to ``warmstart'' using the final search frontier of the preceding solver run. We extend the academic MIP solver SCIP by these techniques to obtain a reoptimizing branch-and-bound solver and report computational results which show the effectiveness of the approach. Y1 - 2015 SN - 978-3-319-20086-6 U6 - https://doi.org/10.1007/978-3-319-20086-6_14 VL - 9125 SP - 181 EP - 192 PB - Springer ET - Experimental Algorithms, Lecture Notes in Computer Science ER - TY - CHAP A1 - Draegert, Andreas A1 - Eisenblätter, Andreas A1 - Gamrath, Inken A1 - Werner, Axel ED - Fujisawa, Katsuki ED - Shinano, Yuji ED - Waki, Hayato T1 - Optimal battery control for smart grid nodes T2 - Optimization in the Real World - Towards Solving Real-World Optimization Problems N2 - Energy storages can be of great value when added to power grids. They introduce the possibility to store and release energy whenever this is favorable. This is particularly relevant, for example, if power supply is volatile (as is the case with renewable energy) and the network is small (so that there are few other nodes that might balance fluctuations in consumption or production). We present models and methods from mathematical optimization for computing an optimized storage schedule for this purpose. We look at alternative optimization objectives, such as smallest possible peak load, low energy costs, or the close approximation of a prescribed load curve. The optimization needs to respect general operational and economic constraints as well as limitations in the use of storage, which are imposed by the chosen storage technology. We therefore introduce alternative approaches for modeling the non-linear properties of energy storages and study their impact on the efficiency of the optimization process. Finally, we present a computational study with batteries as storage devices. We use this to highlight the trade-off between solution quality and computational tractability. A version of the model for the purpose of leveling peaks and instabilities has been implemented into a control system for an office-building smart grid scenario. Y1 - 2015 U6 - https://doi.org/10.1007/978-4-431-55420-2_6 VL - 13 SP - 97 EP - 113 PB - Springer ER - TY - CHAP A1 - Borndörfer, Ralf A1 - Hoppmann, Heide A1 - Karbstein, Marika T1 - Timetabling and Passenger Routing in Public Transport T2 - Proceedings of Conference on Advanced Systems in Public Transport 2015 (CASPT2015) N2 - The task of timetabling is to schedule the trips in a public transport system by determining periodic arrival and departure times at every station. The goal is to provide a service that is both attractive for passengers and can be operated economically. To date, timetable optimization is generally done with respect to fixed passenger routes, i.e., it is assumed that passengers do not respond to changes in the timetable. This is unrealistic and ignores potentially valuable degrees of freedom. We investigate in this paper periodic timetabling models with integrated passenger routing. We propose several models that differ in the allowed passenger paths and the objectives. We compare these models theoretically and report on computations on real-world instances for the city of Wuppertal. KW - Passenger routing KW - Periodic timetabling KW - Public transport Y1 - 2015 ER - TY - CHAP A1 - Vierhaus, Ingmar A1 - Fügenschuh, Armin T1 - A Global Approach to the Optimal Control of System Dynamics Models T2 - Proceedings of the 31st International Conference of the System Dynamics Society N2 - The System Dynamics (SD) methodology is a framework for modeling and simulating the dynamic behavior of socioeconomic systems. Characteristic for the description of such systems is the occurrence of feedback loops together with stocks and flows. The mathematical equations that describe the system are usually ordinary differential equations and nonlinear algebraic constraints. Seemingly simple systems can show a nonintuitive, unpredictable behavior over time. Controlling a dynamical system means to specify potential interventions from outside that should keep the system on the desired track, and to define an evaluation schema to compare different controls among each other, so that a ``best'' control can be defined in a meaningful way. The central question is how to compute such globally optimal control for a given SD model, that allows the transition of the system into a desired state with minimum effort. We propose a mixed-integer nonlinear programming (MINLP) reformulation of the System Dynamics Optimization (SDO) problem. MINLP problems can be solved by linear programming based branch-and-bound approach. We demonstrate that standard MINLP solvers are not able to solve SDO problem. To overcome this obstacle, we introduce a special-tailored bound propagation method. Numerical results for these test cases are presented. Y1 - 2013 ER - TY - CHAP A1 - Vierhaus, Ingmar A1 - Fügenschuh, Armin A1 - Gottwald, Robert Lion A1 - Grösser, Stefan T1 - Modern Nonlinear Optimization Techniques for an Optimal Control of System Dynamics Models T2 - Proceedings of the 32nd International Conference of the System Dynamics Society N2 - We study System Dynamics models with several free parameters that can be altered by the user. We assume that the user's goal is to achieve a certain dynamic behavior of the model by varying these parameters. In order to find best possible combination of parameter settings, several automatic parameter tuning methods are described in the literature and readily available within existing System Dynamic software packages. We give a survey on the available techniques in the market and describe their theoretical background. Some of these methods are already six decades old, and meanwhile newer and more powerful optimization methods have emerged in the mathematical literature. One major obstacle for their direct use are tabled data in System Dynamics models, which are usually interpreted as piecewise linear functions. However, modern optimization methods usually require smooth functions which are twice continuously differentiable. We overcome this problem by a smooth spline interpolation of the tabled data. We use a test set of three complex System Dynamic models from the literature, describe their individual transition into optimization problems, and demonstrate the applicability of modern optimization algorithms to these System Dynamics Optimization problems. Y1 - 2014 ER - TY - CHAP A1 - Borndörfer, Ralf A1 - Reuther, Markus A1 - Schlechte, Thomas T1 - A Coarse-To-Fine Approach to the Railway Rolling Stock Rotation Problem T2 - 14th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems N2 - We propose a new coarse-to-fine approach to solve certain linear programs by column generation. The problems that we address contain layers corresponding to different levels of detail, i.e., coarse layers as well as fine layers. These layers are utilized to design efficient pricing rules. In a nutshell, the method shifts the pricing of a fine linear program to a coarse counterpart. In this way, major decisions are taken in the coarse layer, while minor details are tackled within the fine layer. We elucidate our methodology by an application to a complex railway rolling stock rotation problem. We provide comprehensive computational results that demonstrate the benefit of this new technique for the solution of large scale problems. Y1 - 2014 U6 - https://doi.org/10.4230/OASIcs.ATMOS.2014.79 VL - 42 SP - 79 EP - 91 ER - TY - CHAP A1 - Euler, Ricardo A1 - Borndörfer, Ralf A1 - Puchert, Christian A1 - Takkula, Tuomo T1 - ULD Build-Up Scheduling with Logic-Based Benders Decomposition T2 - Integration of Constraint Programming, Artificial Intelligence, and Operations Research N2 - We study a complex planning and scheduling problem arising from the build-up process of air cargo pallets and containers, collectively referred to as unit load devices (ULD), in which ULDs must be assigned to workstations for loading. Since air freight usually becomes available gradually along the planning horizon, ULD build-ups must be scheduled neither too early to avoid underutilizing ULD capacity, nor too late to avoid resource conflicts with other flights. Whenever possible, ULDs should be built up in batches, thereby giving ground handlers more freedom to rearrange cargo and utilize the ULD's capacity efficiently. The resulting scheduling problem has an intricate cost function and produces large time-expanded models, especially for longer planning horizons. We propose a logic-based Benders decomposition approach that assigns batches to time intervals and workstations in the master problem, while the actual schedule is decided in a subproblem. By choosing appropriate intervals, the subproblem becomes a feasibility problem that decomposes over the workstations. Additionally, the similarity of many batches is exploited by a strengthening procedure for no-good cuts. We benchmark our approach against a time-expanded MIP formulation from the literature on a publicly available data set. It solves 15% more instances to optimality and decreases run times by more than 50% in the geometric mean. This improvement is especially pronounced for longer planning horizons of up to one week, where the Benders approach solves over 50% instances more than the baseline Y1 - 2024 U6 - https://doi.org/10.1007/978-3-031-60597-0_17 VL - 14742 SP - 259 EP - 276 ER - 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 - 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 - 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 - TY - CHAP A1 - Klug, Torsten A1 - Reuther, Markus A1 - Schlechte, Thomas T1 - Does Laziness Pay Off? - A Lazy-Constraint Approach to Timetabling T2 - 22nd Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2022) N2 - Timetabling is a classical and complex task for public transport operators as well as for railway undertakings. The general question is: Which vehicle is taking which route through the transportation network in which order? In this paper, we consider the special setting to find optimal timetables for railway systems under a moving block regime. We directly set up on our work of [8 ], i.e., we consider the same model formulation and real-world instances of a moving block headway system. In this paper, we present a repair heuristic and a lazy-constraint approach utilizing the callback features of Gurobi, see [3]. We provide an experimental study of the different algorithmic approaches for a railway network with 100 and up to 300 train requests. The computational results show that the lazy-constraint approach together with the repair heuristic significantly improves our previous approaches. Y1 - 2022 U6 - https://doi.org/10.4230/OASIcs.ATMOS.2022.11 VL - 106 SP - 11:1 EP - 11:8 PB - Schloss Dagstuhl -- Leibniz-Zentrum für Informatik 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 - 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 - CHAP A1 - Bortoletto, Enrico A1 - Lindner, Niels ED - Voigt, Guido ED - Fliedner, Malte ED - Haase, Knut ED - Brüggemann, Wolfgang ED - Hoberg, Kai ED - Meissner, Jörn T1 - Scaling and Rounding Periodic Event Scheduling Instances to Different Period Times T2 - Operations Research Proceedings 2023 N2 - The Periodic Event Scheduling Problem (PESP) is a notoriously hard combinatorial optimization problem, essential for the design of periodic timetables in public transportation. The coefficients of the integer variables in the standard mixed integer linear programming formulations of PESP are the period time, e.g., 60 for a horizon of one hour with a resolution of one minute. In many application scenarios, lines with different frequencies have to be scheduled, leading to period times with many divisors. It then seems natural to consider derived instances, where the period time is a divisor of the original one, thereby smaller, and bounds are scaled and rounded accordingly. To this end, we identify two rounding schemes: wide and tight. We then discuss the approximation performance of both strategies, in theory and practice. Y1 - 2025 U6 - https://doi.org/10.1007/978-3-031-58405-3_51 SP - 397 EP - 402 ER -