90C10 Integer programming
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In \emph{classical optimization} it is assumed that full information about the problem to be solved is given. This, in particular, includes that all data are at hand. The real world may not be so nice'' to optimizers. Some problem constraints may not be known, the data may be corrupted, or some data may not be available at the moments when decisions have to be made. The last issue is the subject of \emph{online optimization} which will be addressed here. We explain some theory that has been developed to cope with such situations and provide examples from practice where unavailable information is not the result of bad data handling but an inevitable phenomenon.
Anwendungen der Mathematik in der Verkehrs- und Transporttechnologie haben eine große und bedeutende Tradition. Natürlich wurden die ersten Fahrzeuge mit der ingenieurmäßigen Methode von Versuch, Irrtum und Verbesserung entworfen. Aber schon sehr bald kamen mathematische Berechnungen hinzu, mit denen mechanische Eigenschaften von Fahrzeugteilen ermittelt und zum Teil optimiert wurden. Die hierzu erforderliche Mathematik wurde in diesem Jahrhundert zu einem mächtigen Werkzeugkasten ausgebaut. Mit diesem kann man heute z.B. hocheffiziente Motoren mit geringem Schadstoffausstoß entwerfen, aerodynamisch günstige Fahrzeugprofile ermitteln und Flugzeugflügel berechnen, die die gewünschte Last sicher und mit geringem Treibstoffaufwand tragen. Die Mathematik unterstützt die Technologie des Verkehrs beginnend bei globalen Designfragen bis hin zur Spezifizierung von Materialeigenschaften kleinster Bauteile; sie berechnet mit hoher Präzision energieoptimale Bahnen von Raumflugkörpern oder zeitoptimale Trajektorien für Flugzeuge, steuert automatische Roboteranlagen oder innerbetriebliche Transportsysteme.
Der Schnellste Weg zum Ziel
(1999)
Many optimization problems have several equivalent mathematical models. It is often not apparent which of these models is most suitable for practical computation, in particular, when a certain application with a specific range of instance sizes is in focus. Our paper addresses the Asymmetric Travelling Salesman Problem with time windows (ATSP-TW) from such a point of view. The real--world application we aim at is the control of a stacker crane in a warehouse. We have implemented codes based on three alternative integer programming formulations of the ATSP-TW and more than ten heuristics. Computational results for real-world instances with up to 233 nodes are reported, showing that a new model presented in a companion paper outperforms the other two models we considered --- at least for our special application --- and that the heuristics provide acceptable solutions.
Mobile telecommunication systems establish a large number of communication links with a limited number of available frequencies; reuse of the same or adjacent frequencies on neighboring links causes interference. The task to find an assignment of frequencies to channels with minimal interference is the frequency assignment problem. The frequency assignment problem is usually treated as a graph coloring problem where the number of colors is minimized, but this approach does not model interference minimization correctly. We give in this paper a new integer programming formulation of the frequency assignment problem, the orientation model, and develop a heuristic two-stage method to solve it. The algorithm iteratively solves an outer and an inner optimization problem. The outer problem decides for each pair of communication links which link gets the higher frequency and leads to an acyclic subdigraph problem with additional longest path restrictions. The inner problem to find an optimal assignment respecting an orientation leads to a min-cost flow problem.