90-XX OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING
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Periodic timetabling is a challenging planning task in public transport.
As safety requirements are crucial, track allocation is indispensable for validating the practical feasibility of a railway timetable. For busy stations with limited capacities, this requires a detailed planning of turnarounds. It is therefore desirable to integrate timetabling not only with track allocation, but also with vehicle scheduling and line planning. This is captured by the Integrated Line Planning and Turn-Sensitive Periodic Timetabling Problem with Track Choice, whose MIP formulation has been demonstrated to be effective for construction site railway rescheduling, as long as a good quality initial solution is available. In this paper, we discuss how to generate such a solution by extending the SAT formulation of the Periodic Event Scheduling Problem with track choice, track occupation, and minimum service frequency components. The SAT approach is superior to pure MIP on real-world instances of the S-Bahn Berlin network.
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. Subse-
quently, 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.
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.
We present incremental heuristics for the Periodic Event Scheduling Problem (PESP), the standard mathematical tool to optimize periodic timetables in public transport. The core of our method is to solve successively larger subinstances making use of previously found solutions. Introducing the technical notion of free stratifications, we formulate a general scheme for incremental heuristics for PESP. More practically, we use line and station information to create heuristics that add lines or stations one by one, and we evaluate these heuristics on instances of the benchmarking library PESPlib. This approach is indeed viable, and leads to new incumbent solutions for six PESPlib instances.
Construction of a Test Library for the Rolling Stock Rotation Problem with Predictive Maintenance
(2023)
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.
Convergence Properties of Newton's Method for Globally Optimal Free Flight Trajectory Optimization
(2023)
The algorithmic efficiency of Newton-based methods for Free Flight Trajectory Optimization is heavily influenced by the size of the domain of convergence. We provide numerical evidence that the convergence radius is much larger in practice than what the theoretical worst case bounds suggest. The algorithm can be further improved by a convergence-enhancing domain decomposition.
The Periodic Event Scheduling Problem (PESP) is the central mathematical tool for periodic timetable optimization in public transport. PESP can be formulated in several ways as a mixed-integer linear program with typically general integer variables.
We investigate the split closure of these formulations and show that split inequalities are identical with the recently introduced flip inequalities. While split inequalities are a general mixed-integer programming technique, flip inequalities are defined in purely combinatorial terms, namely cycles and arc sets of the digraph underlying the PESP instance. It is known that flip inequalities can be separated in pseudo-polynomial time. We prove that this is best possible unless P $=$ NP, but also observe that the complexity becomes linear-time if the cycle defining the flip inequality is fixed.
Moreover, introducing mixed-integer-compatible maps, we compare the split closures of different formulations, and show that reformulation or binarization by subdivision do not lead to stronger split closures. Finally, we estimate computationally how much of the optimality gap of the instances of the benchmark library PESPlib can be closed exclusively by split cuts, and provide better dual bounds for five instances.
The optimization of periodic timetables is an indispensable planning task in public transport. Although the periodic event scheduling problem (PESP) provides an elegant mathematical formulation of the periodic timetabling problem that led to many insights for primal heuristics, it is notoriously hard to solve to optimality. One reason is that for the standard mixed-integer linear programming formulations, linear programming relaxations are weak and the integer variables are of pure technical nature and in general do not correlate with the objective value. While the first problem has been addressed by developing several families of cutting planes, we focus on the second aspect. We discuss integral forward cycle bases as a concept to compute improved dual bounds for PESP instances. To this end, we develop the theory of forward cycle bases on general digraphs. Specifically for the application of timetabling, we devise a generic procedure to construct line-based event-activity networks, and give a simple recipe for an integral forward cycle basis on such networks. Finally, we analyze the 16 railway instances of the benchmark library PESPlib, match them to the line-based structure and use forward cycle bases to compute better dual bounds for 14 out of the 16 instances.
We study the solution of the rolling stock rotation problem with predictive maintenance (RSRP-PM) 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 two 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.
In graphical representations of public transportation networks, there is often some degree of uncertainty in the arc values, due to delays or transfer times. This uncertainty can be expressed as a parameterized weight on the transfer arcs. Classical shortest path algorithms often have difficulty handling parameterized arc weights and a tropical geometry approach has been shown as a possible solution. The connection between the classical shortest path problem and tropical geometry is well establish: Tropically multiplying the n × n adjacency matrix of a graph with itself n − 1 times results in the so-called Kleene star, and is a matrix-form solution to the all-pairs shortest path problem. Michael Joswig and Benjamin Schröter showed in their paper The Tropical Geometry of Shortest Paths that the same method can be used to find the solution to the all-pairs shortest path problem even in the case of variable arc weights and they proposed an algorithm to solve the single-target shortest path problem in such a case. The solution takes the form of a polyhedral subdivision of the parameter space. As the number of variable arc weights grows, the time needed to execute an implementation of this algorithm grows exponentially. As the size of a public transportation network grows, the number of variable arc weights grows exponentially as well. However, it has been observed that in public transportation networks, there are usually only a few possible shortest routes. Geometrically, this means that there should be few polyhedra in the polyhedral subdivision. This algorithm is used on an example of a real-world public transportation network and an analysis of the polyhedral subdivision is made. Then a geometrical approach is used to analyze the impact of limiting the number of transfers, and thereby limiting the number of parameterized arcs used, as an estimation of the solution to the all-pairs shortest path problem
Die Planung vom Zugumläufen ist eine der wichtigsten Aufgaben für Eisenbahnun- ternehmen. Dabei spielt auch die Einhaltung von vorgegebenen Wartungsintervallen eine zentrale Rolle für die Sicherheit und Zuverlässigkeit der Schienenfahrzeuge. Wir zeigen, wie man dieses Umlaufplanungsproblem unter Beachtung von Wartungsbe- dingungen mathematisch formuliert, modelliert und löst — sowohl in der Theorie als auch im Anwendungsfall mit Szenarien der DB Fernverkehr AG, einer Konzern- tochter der Deutschen Bahn für den Schienenpersonenfernverkehr.
Markus Reuther hat sich in seiner Dissertation [11] mit diesem Problem beschäftigt und es mit Hilfe eines passenden Hypergraphen als gemischt-ganzzahliges Programm modelliert. Neben der Modellierung präsentiert Reuther in seiner Arbeit neuartige algorithmische Ideen, darunter den sogenannten Coarse-to-Fine -Ansatz, bei dem zunächst Teile des Problems auf einer weniger detaillierten ( coarse ) Ebene gelöst werden und diese Lösung dann verwendet wird, um auf effiziente Art und Weise eine Lösung für das ursprüngliche Problem zu finden. Zur Wartungsplanung nutzt Reuther einen Fluss im Hypergraphen, der den Ressourcenverbrauch der Fahrzeuge modelliert. In der linearen Relaxierung des Modells führt dies dazu, dass die Zahl der notwendigen Wartungen systematisch unterschätzt wird. Dadurch bleibt in vielen Fällen eine große Lücke zwischen dem Zielfunktionswert einer optimalen Lösung des ganzzahligen Problems und der untere Schranke, die uns die lineare Relaxierung liefert.
Wir nehmen uns in dieser Arbeit dieses Problems an. Wir entwickeln ein auf Pfaden basierendes ganzzahliges Modell für das Umlaufplanungsproblem und zeigen, dass die untere Schranke mindestens so scharf oder schärfer ist als die untere Schranke, die das Modell von Reuther liefert. Um das Modell zu lösen, entwickeln wir einen Algorithmus, der Spaltengenerierung mit dem Coarse-to-Fine-Ansatz von Reuther verbindet. Weiterhin entwickeln wir eine Spaltenauswahlregel zur Beschleunigung des Algorithmus. Das Modell und alle in der Arbeit vorgestellten Algorithmen wur- den im Rahmen der Arbeit implementiert und mit Anwendungsszenarien der DB Fernverkehr AG getestet. Unsere Tests zeigen, dass unser Modell für fast alle Szena- rien deutlich schärfere untere Schranken liefert als das Modell von Reuther. In den getesteten Instanzen konnten wir durch die Verbesserung der unteren Schranke bis zu 99% der Optimalitätslücke schließen. In einem Drittel der Fälle konnten wir durch unseren Ansatz auch für das ganzzahlige Programm verbesserte Zielfunktionswerte erreichen
Algorithms that solve the shortest path problem can largely be split into the two categories of label setting and label correcting. The Multiobjective Shortest Path (MOSP) problem is a generalization of the classical shortest path problem in terms of the dimension of the cost function. We explore the differences of two similar MOSP label setting algorithms. Furthermore, we present and prove a general method of how to derive Fully Polynomial Time Approximation Schemes (FPTAS) for MOSP label setting algorithms. Finally, we explore two pruning techniques for the one to one variants of exact label setting MOSP algorithms and adapt them to their FPTAS variants.
For this thesis we study the Constrained Horizontal Flightplanning Problem (CHFPP) for which one has to find the path of minimum cost between airports s and t in a directed graph that respects a set of boolean constraints. To this end we give a survey of three different multilabel algorithms that all use a domination subroutine. We summarize an approach by Knudsen, Chiarandini and Larsen to define this domination and afterwards present our own method which builds on that approach. We suggest different implementation techniques to speed up the computation time, most notably a Reoptimization for an iterative method to solve the problem. Furthermore we implemented the different versions of the algorithm and present statistics on their computation as well as an overview of statistics on the set of real-world constraints that we were given. Finally we present two alternative approaches that tackle the problem, a heuristic with similarities to a Lagrangian relaxation and an approach that makes use of an algorithm which finds the k shortest path of a graph such as the ones of Epstein or Yen.
Scheduling ist ein wichtiger Forschungsgegenstand im Bereich der diskreten Optimierung. Es geht darum, einen Schedule, d.h. einen Ablaufplan, für gegebene Ereignisse zu finden. Dieser soll optimal hinsichtlich einer Zielfunktion wie zum Beispiel minimaler Dauer oder Kosten sein. Dabei gibt es in der Regel Nebenbedingungen wie Vorrangbeziehungen zwischen den Ereignissen oder zeitliche Einschränkungen, die zu erfüllen sind. Falls die Ereignisse periodisch wiederkehren, spricht man von periodischem Scheduling. Beispiele sind das Erstellen von Zugfahrplänen, die Schaltungvon Ampelsignalen oder die Planung von Produktionsabläufen.
Mathematisch können diese Probleme mit dem Periodic Event Scheduling Problem (PESP) modelliert werden, das als gemischt-ganzzahliges Programm formuliert werden kann. In dieser Bachelorarbeit wird ein Ansatz zur Lösung des PESP mittels Zerlegung und Dualisierung entwickelt. In den Kapiteln 2 und 3 werden zunächst die notwendigen graphentheoretischen Grundlagen und das PESP eingeführt. In Kapitel 4 wird das PESP durch Fixierung der ganzzahligen Variablen in lineare Programme zerlegt. Dieses Unterproblem wird dualisiert und wieder in das PESP eingesetzt. Dafür ist eine weitere Nebenbedingung nötig.
Im fünften Kapitel behandeln wir die Lösung des teildualisierten PESP. Eine Möglichkeit ist es, sich auf eine Teilmenge der Nebenbedingungen zu beschränken. Eine weitere Möglichkeit ist ein Algorithmus, derähnlich wie BendersZerlegung die Nebenbedingungen dynamisch erzeugt. Dieser Algorithmus wird in Kapitel 6 implementiert und an vier Beispielen getestet.
We propose a tropical interpretation of the solution space of the Periodic Event Scheduling Problem as a collection of polytropes, making use of the characterization of tropical cones as weighted digraph polyhedra. General and geometric properties of the polytropal collection are inspected and understood in connection with the combinatorial properties of the underlying periodic event scheduling instance. Novel algorithmic
ideas are presented and tested, making use of the aforementioned theoretical results to solve and optimize the problem.
One of the fundamental steps in the optimization of public transport is line planning. It involves determining lines and assigning frequencies of service such that costs are minimized while also maximizing passenger comfort and satisfying travel demands. We formulate the problem as a mixed integer linear program that considers all circuit-like lines in a graph and allows free passenger routing. Traveler and operator costs are included in a linear scalarization in the objective. We apply said programming problem to the Parametric City, which is a graph model introduced by Fielbaum, Jara-Díaz and Gschwender that exibly represents different cities. In his dissertation, Fielbaum solved the line planning problem for various parameter choices in the Parametric City. In a first step, we therefore review his results and make comparative computations. Unlike Fielbaum we arrive at the conclusion that the optimal line plan for this model indeed depends on the demand. Consequently, we analyze the line planning problem in-depth: We find equivalent, but easier to compute formulations and provide a lower bound by LP-relaxation, which we show to be equivalent to a multi-commodity flow problem. Further, we examine what impact symmetry has on the solutions. Supported both by computational results as well as by theoretical analysis, we reach the conclusion that symmetric line plans are optimal or near-optimal in the Parametric City. Restricting the model to symmetric line plans allows for a \kappa-factor approximation algorithm for the line planning problem in the Parametric City.
Air freight is usually shipped in standardized unit load devices (ULDs).
The planning process for the consolidation of transit cargo from inbound flights or locally emerging shipments into ULDs for outbound flights is called build-up scheduling.
More specifically, outbound ULDs must be assigned a time and a workstation subject to both workstation capacity constraints and the availability of shipments which in turn depends on break-down decisions for incoming ULDs.
ULDs scheduled for the same outbound flight should be built up in temporal and spatial proximity.
This serves both to minimize overhead in transportation times and to allow workers to move freight between ULDs.
We propose to address this requirement by processing ULDs for the same outbound flight in batches.
For the above build-up scheduling problem, we introduce a multi-commodity network design model.
Outbound flights are modeled as commodities; transit cargo is represented by cargo flow volume and unpack and batch decisions are represented as design variables.
The model is solved with standard MIP solvers on a set of benchmark data.
For instances with a limited number of resource conflicts, near-optimal solutions are found in under two hours for a whole week of operations.