ZIB-Report
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- Mathematical Optimization (429) (remove)
13-77
We provide an overview of new theoretical results that we obtained while further investigating multiband robust optimization, a new model for robust optimization that we recently proposed to tackle uncertainty in mixed-integer linear programming. This new model extends and refines the classical Gamma-robustness model of Bertsimas and Sim and is particularly useful in the common case of arbitrary asymmetric distributions of the uncertainty. Here, we focus on uncertain 0-1 programs and we analyze their robust counterparts when the uncertainty is represented through a multiband set. Our investigations were inspired by the needs of our industrial partners in the research project ROBUKOM.
12-13
10 Jahre TELOTA
(2012)
Das TELOTA-Projekt - zunächst nur für zwei Jahre gestartet - feierte am 15. Juni 2011 sein
10-jähriges Bestehen im Rahmen eines Workshops mit einem abschließenden Festvortrag von
Richard Stallmann zum Thema „Copyright versus community in the age of computer
networks“. Diese Veranstaltung zeigte, wie aktuell die TELOTA-Themen weiterhin sind und
dass diese eine große Resonanz in der allgemeinen Öffentlichkeit finden. Die TELOTA-Aktivitäten
haben sich als wichtiger Bestandteil der IT-Infrastruktur der BBAW erwiesen,
gehen aber weit über reinen Service hinaus. Sie beeinflussen die Forschung selbst und führen
zu neuen interessanten wissenschaftlichen Fragestellungen. Der Rückblick auf die ersten zehn
Jahre der TELOTA-Initiative in diesem Artikel soll einen kleinen Eindruck von dem geben,
was bisher geleistet wurde.
11-19
Base station cooperation in the downlink of cellular systems has been recently suggested as a promising concept towards a
better exploitation of the communication system physical resources. It may offer a high gain in capacity through interference mitigation.
This however,
comes at a cost of high information exchange between cooperating entities and a high computational burden.
Clustering of base stations into subgroups is an alternative to
guarantee such cooperation benefits in a lower scale. The optimal definition of clusters, however, and a systematic way to find a solution to
such problem is not yet available. In this work, we highlight the combinatorial nature of the problem, exploit this to describe the
system of users and base stations as a graph and formulate a pure 0-1 program. Its solution
suggests a cost optimal way to form clusters and assign user subsets to them.
09-12
The Vehicle Positioning Problem (VPP) consists of the assignment of vehicles (buses, trams or trains) of a public transport or railway company to parking positions in a depot and to timetabled trips. Such companies have many different types of vehicles, and each trip can be performed only by vehicles of some of these types. These assignments are non-trivial due to the topology of depots. The parking positions are organized in tracks, which work as one- or two-sided stacks or queues. If a required type of vehicle is not available in the front of any track, shunting movements must be performed in order to change vehicles' positions, which is undesirable and should be avoided. In this text we present integer linear and non-linear programming formulations for some versions of the problem and compare them from a theoretical and a computational point of view.
SC-97-70
A Branch & Cut Algorithm for the Asymmetric Traveling Salesman Problem with Precedence Constraints
(1997)
In this paper we consider a variant of the classical ATSP, namely the asymmetric Hamiltonian path problem (or equivalently ATSP) with precedence constraints. In this problem precedences among the nodes are present, stating that a certain node has to precede others in any feasible sequence. This problem occurs as a basic model in scheduling and routing and has a wide range of applications varying from helicopter routing[Timlin89], sequencing in flexible manufacturing [AscheuerEscuderoGroetschelStoer90,AscheuerEscuderoGroetschelStoer93], to stacker crane routing in an automatic storage system[Ascheuer95]. We give an integer programming model and summarize known classes of valid inequalities. We describe in detail the implementation of a branch&-cut algorithm and give computational results on real world instances and benchmark problems from TSPLIB. The results we achieve indicate that our implementation outperforms other implementations found in the literature. Real world instances up to 174 nodes could be solved to optimality within a few minutes of CPU-time. As a side product we obtained a branch&cut-algorithm for the ATSP. All instances in TSPLIB could be solved to optimality in a reasonable amount of computing time.
12-21
In this paper we present the problem of computing optimal tours of toll inspectors on German motorways. This problem is a special type of vehicle routing problem and builds up an integrated model, consisting of a tour
planning and a duty rostering part. The tours should guarantee a network-wide control whose intensity is proportional to given spatial and time dependent traffic distributions. We model this using a space-time network
and formulate the associated optimization problem by an integer program (IP). Since sequential approaches fail, we integrated the assignment of crews to the tours in our model. In this process all duties of a crew member must fit in a feasible roster. It is modeled as a Multi-Commodity Flow Problem in a directed acyclic graph, where specific paths correspond to
feasible rosters for one month. We present computational results in a
case-study on a German subnetwork which documents the practicability of our approach.
11-51
We show that a class of semidefinite programs (SDP) admits a solution that is a positive semidefinite
matrix of rank at most $r$, where $r$ is the rank of the matrix involved in the objective function of the SDP.
The optimization problems of this class are semidefinite packing problems,
which are the SDP analogs to vector packing problems.
Of particular interest is the case in which our result guarantees the existence of a solution
of rank one: we show that the computation of this solution actually reduces to a
Second Order Cone Program (SOCP).
We point out an application in statistics, in the optimal design of experiments.
14-26
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.
17-27
We state purely combinatorial proofs for König- and Hall-type theorems for a wide class of combinatorial optimization problems. Our methods rely on relaxations of the matching and vertex cover problem and, moreover, on the strong coloring properties admitted by bipartite graphs and their generalizations.
19-07
We introduce a concurrent solver for the periodic event scheduling problem (PESP). It combines mixed integer programming techniques, the modulo network simplex method, satisfiability approaches, and a new heuristic based on maximum cuts. Running these components in parallel speeds up the overall solution process. This enables us to significantly improve the current upper and lower bounds for all benchmark instances of the library PESPlib.