90-XX OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING
Refine
Year of publication
Document Type
- ZIB-Report (175)
- Master's Thesis (17)
- Bachelor's Thesis (9)
- Doctoral Thesis (6)
- Article (2)
- In Proceedings (1)
Keywords
Institute
- Mathematical Optimization (156)
- Mathematical Optimization Methods (34)
- Network Optimization (29)
- Applied Algorithmic Intelligence Methods (14)
- AI in Society, Science, and Technology (9)
- Mathematics of Telecommunication (7)
- Mathematics of Transportation and Logistics (6)
- Mathematical Algorithmic Intelligence (4)
- Numerical Mathematics (4)
- ZIB Allgemein (4)
This thesis is about mathematical optimization for the efficient use of railway infrastructure. We address the
optimal allocation of the available railway track capacity - the track allocation problem.
This track allocation problem is a major challenge for a railway company, independent of
whether a free market, a private monopoly, or a public monopoly is given.
Planning and operating railway transportation systems is extremely hard due
to the combinatorial complexity of the underlying discrete optimization problems,
the technical intricacies, and the immense sizes of the problem instances. Mathematical models and optimization
techniques can result in huge gains for both railway customers and operators, e.g.,
in terms of cost reductions or service quality improvements.
We tackle this challenge by developing novel mathematical models and associated innovative algorithmic
solution methods for large scale instances. This allows us to produce for the first time reliable
solutions for a real world instance, i.e., the Simplon corridor in Switzerland.
The opening chapter gives a comprehensive overview on railway planning problems.
This provides insights into the regulatory and technical framework,
it discusses the interaction of several planning steps, and identifies optimization potentials in
railway transportation. The remainder of the thesis is comprised of two major parts.
The first part is concerned with modeling railway systems to allow for resource and capacity analysis.
Railway capacity has basically two dimensions, a space dimension which are the physical
infrastructure elements as well as a time dimension that refers to the train movements, i.e.,
occupation or blocking times, on the physical infrastructure. Railway safety systems operate
on the same principle all over the world. A train has to reserve infrastructure blocks for some time to pass through.
Two trains reserving the same block of the infrastructure within the same point in time is called block conflict.
Therefore, models for railway capacity involve the definition
and calculation of reasonable running and associated reservation and
blocking times to allow for a conflict free allocation.
In the second and main part of the thesis, the optimal track
allocation problem for macroscopic models of the railway system is considered.
The literature for related problems is surveyed.
A graph-theoretic model for the track allocation problem is
developed. In that model optimal track allocations correspond to
conflict-free paths in special time-expanded graphs.
Furthermore, we made considerable progress on solving track allocation problems by two
main features - a novel modeling approach for the macroscopic track
allocation problem and algorithmic improvements based on the
utilization of the bundle method.
Finally, we go back to practice and present in the last chapter several case
studies using the tools netcast and tsopt.
We provide a computational comparison of
our new models and standard packing models used in the literature.
Our computational experience indicates that our approach, i.e.,
``configuration models'', outperforms other models. Moreover, the rapid branching
heuristic and the bundle method enable us to produce high quality solutions for very large scale
instances, which has not been possible before.
In addition, we present results for a theoretical and rather visionary auction framework
for track allocation. We discuss several auction design questions and analyze experiments of
various auction simulations.
The highlights are results for the Simplon corridor in Switzerland.
We optimized the train traffic through this tunnel using our models and
software tools.
To the best knowledge of the author and confirmed by several railway
practitioners this was the first time that fully automatically produced
track allocations on a macroscopic scale fulfill the requirements
of the originating microscopic model, withstand the evaluation in the
microscopic simulation tool OpenTrack, and exploit the infrastructure capacity.
This documents the success of our approach in practice and the usefulness
and applicability of mathematical optimization to railway track allocation.
In this paper we assess to which extent trenching costs of an FTTx network are unavoidable, even if technical side constraints are neglected. For that purpose we present an extended Steiner tree model. Using a variety of realistic problem instances we demonstrate that the total trenching cost can only be reduced by about 5 percent in realistic scenarios. This work has been funded by BMBF (German Federal Ministry of Education and Research) within the program "KMU-innovativ".
Das vom BMBF geförderte Projekt FTTX-PLAN entwickelt mathematische Modelle und Optimierungsverfahren, um automatisiert kostenoptimierte FTTx-Netze berechnen zu können. Wir zeigen anhand einer Praxisstudie in Zusammenarbeit mit der Regensburger R-KOM, wie ein Planer von diesen Verfahren profitieren kann, um die Auswirkungen bestimmter Entscheidungen auf die Netzstruktur und -kosten zu untersuchen. Wir illustrieren dies am Beispiel eines FTTB/FTTH-Vergleichs, der Variation von Kundenanbindungsraten und der gezielten Ausnutzung existierender Leerrohre, um Tiefbau zu vermeiden.
The steel mill slab design problem from the CSPLIB is a combinatorial
optimization problem motivated by an application of the steel industry. It
has been widely studied in the constraint programming community. Several
methods were proposed to solve this problem. A steel mill slab library was
created which contains 380 instances. A closely related binpacking problem
called the multiple knapsack problem with color constraints, originated
from the same industrial problem, was discussed in the integer programming
community. In particular, a simple integer program for this problem has
been given by Forrest et al. The aim of this paper is to bring these
different studies together. Moreover, we adapt the model of Forrest et
al. for the steel mill slab design problem. Using this model and a
state-of-the-art integer program solver all instances of the steel mill
slab library can be solved efficiently to optimality. We improved,
thereby, the solution values of 76 instances compared to previous results.
Finally, we consider a recently introduced variant of the steel mill slab
design problem, where within all solutions which minimize the leftover one
is interested in a solution which requires a minimum number of slabs. For
that variant we introduce two approaches and solve all instances of the
steel mill slab library with this slightly changed objective function to
optimality.
We propose a model for the integrated optimization of vehicle
rotations and vehicle compositions in long distance railway passenger
transport. The main contribution of the paper is a hypergraph model
that is able to handle the challenging technical requirements as
well as very general stipulations with respect to the ``regularity''
of a schedule. The hypergraph model directly generalizes network
flow models, replacing arcs with hyperarcs. Although NP-hard in
general, the model is computationally well-behaved in practice. High
quality solutions can be produced in reasonable time using high
performance Integer Programming techniques, in particular, column
generation and rapid branching. We show that, in this way,
large-scale real world instances of our cooperation partner DB
Fernverkehr can be solved.
Today the railway timetabling process and the track allocation
is one of the most challenging problems to solve by a railway infrastructure provider.
Especially due to the deregulation of the transport market in the recent years several
suppliers of railway traffic have entered the market. This leads to an increase of slot requests
and then it is natural that conflicts occur among them.
Furthermore, railway infrastructure networks consist of very expensive assets, even more
they are rigid due to the long-term upgrade process.
In order to make best use of these valuable infrastructure and to ensure economic operation,
efficient planning of the railway operation is indispensable.
Mathematical optimization models and algorithmic methodology can help
to automatize and tackle these challenges.
Our contribution in this paper is to present a renewed planning process
due to the liberalization in Europe and a general framework to
support the integration of simulation and optimization for
railway capacity allocation.
Many practically relevant problems can be formulated in terms of a mixed integer programming (MIP) model. MIP denotes the
optimization of a linear objective function under a certain number of linear side constraints including the need for
some of the involved variables to take integral solution values.
Applications of MIP based optimization can be found in the area of public transit,
scheduling, automatic vehicle routing,
network design, etc.
From a complexity point of view, MIP solving is known to be NP-hard and most commonly tried to be solved via
Branch-and-Bound based algorithms. Branch-and-Bound algorithms benefit from early and good feasible solutions of a MIP
in various ways.
Primal heuristics are aimed at finding new solutions during the MIP solving process. There are different types of primal heuristics:
while start heuristics are particularly
valuable to find an early solution, improvement heuristics hopefully drive a given solution further towards optimality.
This thesis focusses on primal heuristics which are part of the MIP-solving framework SCIP.
The first chapter comes with basic definitions and a brief description of SCIP and the test set which we used.
The remainder of the first chapter is an overview of the existing heuristics in SCIP which have been implemented by Achterberg
and Berthold.
In the following chapters we introduce three new heuristics which apply rounding or propagation techniques for their specific purpose,
namely the new rounding heuristic ZI Round, taken from Wallace, a 2-Opt improvement
heuristic for MIP and the propagation heuristic Shift-and-Propagate.
It is characteristic of all three heuristics that they mainly apply computationally inexpensive algorithms.
Each of them is presented in an own chapter, starting with an algorithmic description, followed by implementational details.
All chapters close with a discussion of the computational results obtained with the respective implementations in SCIP.
Convergence Analysis of Smoothing Methods for Optimal Control of Stationary Variational Inequalities
(2011)
In the article an optimal control problem subject to a stationary variational inequality
is investigated. The optimal control problem is complemented with pointwise control constraints.
The convergence of a smoothing scheme is analyzed. There, the variational inequality
is replaced by a semilinear elliptic equation. It is shown that solutions of the regularized optimal
control problem converge to solutions of the original one. Passing to the limit in the
optimality system of the regularized problem allows to prove C-stationarity of local solutions of the original problem.
Moreover, convergence rates with respect to the regularization parameter for the error in the control are obtained.
These rates coincide with rates obtained by numerical experiments, which are included in the paper.
Large neighborhood search (LNS) heuristics are an important component of modern branch-and-cut algorithms for solving mixed-integer linear programs (MIPs). Most of these LNS heuristics use the LP relaxation as the basis for their search, which is a reasonable choice in case of MIPs. However, for more general problem classes, the LP relaxation alone may not contain enough information about the original problem to find feasible solutions with these heuristics, e.g., if the problem is nonlinear or not all constraints are present in the current relaxation.
In this paper, we discuss a generic way to extend LNS heuristics that have been developed for MIP to constraint integer programming (CIP), which is a generalization of MIP in the direction of constraint programming (CP). We present computational results of LNS heuristics for three problem classes: mixed-integer quadratically constrained programs, nonlinear pseudo-Boolean optimization instances, and resource-constrained project scheduling problems. Therefore, we have implemented extended versions of the following LNS heuristics in the constraint integer programming framework SCIP: Local Branching, RINS, RENS, Crossover, and DINS. Our results indicate that a generic generalization of LNS heuristics to CIP considerably improves the success rate of these heuristics.