@misc{KrumkeRambau2002, author = {Krumke, Sven and Rambau, J{\"o}rg}, title = {Probieren geht {\"u}ber Studieren? Entscheidungshilfen f{\"u}r kombinatorische Online-Optimierungsprobleme in der innerbetrieblichen Logistik}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-6723}, number = {02-05}, year = {2002}, abstract = {Die Automatisierung von innerbetrieblicher Logistik erfordert -- {\"u}ber die physikalische Steuerung von Ger{\"a}ten hinaus -- auch eine effiziente Organisation der Transporte: ein Aufgabenfeld der kombinatorischen Optimierung. Dieser Artikel illustriert anhand von konkreten Aufgabenstellungen die Online-Problematik (unvollst{\"a}ndiges Wissen) sowie die Echtzeit-Problematik (beschr{\"a}nkte Rechenzeit), auf die man in der innerbetrieblichen Logistik trifft. Der Text gibt einen {\"U}berblick {\"u}ber allgemeine Konstruktionsprinzipien f{\"u}r Online-Algorithmen und Bewertungsmethoden, die bei der Entscheidung helfen, welche Algorithmen f{\"u}r eine vorliegende Problemstellung geeignet sind.}, language = {de} } @article{GroetschelKrumkeRambauetal.2002, author = {Gr{\"o}tschel, Martin and Krumke, Sven and Rambau, J{\"o}rg and Torres, Luis Miguel}, title = {Making the Yellow Angels Fly}, volume = {35}, journal = {SIAM News}, number = {4}, pages = {1,10,11}, year = {2002}, language = {en} } @article{GroetschelKrumkeRambau1999, author = {Gr{\"o}tschel, Martin and Krumke, Sven and Rambau, J{\"o}rg}, title = {Wo bleibt der Aufzug?}, volume = {5}, journal = {OR News}, pages = {11 -- 13}, year = {1999}, language = {en} } @article{GroetschelKrumkeRambau2006, author = {Gr{\"o}tschel, Martin and Krumke, Sven and Rambau, J{\"o}rg}, title = {Wo bleibt der Aufzug?}, volume = {Sonderausgabe}, journal = {OR News}, pages = {70 -- 72}, year = {2006}, language = {en} } @inproceedings{AscheuerGroetschelKrumkeetal.1999, author = {Ascheuer, Norbert and Gr{\"o}tschel, Martin and Krumke, Sven and Rambau, J{\"o}rg}, title = {Combinatorial Online Optimization}, booktitle = {Operations Research Proceedings 1998. Selected Papers of the International Conference on Operations Research Zurich, August 31 - September 3, 1998}, editor = {Kall, Peter and L{\"u}thi, Hans-Jakob}, publisher = {Springer}, pages = {21 -- 37}, year = {1999}, language = {en} } @inproceedings{HeinzKrumkeMegowetal.2006, author = {Heinz, Stefan and Krumke, Sven and Megow, Nicole and Rambau, J{\"o}rg and Tuchscherer, Andreas and Vredeveld, Tjark}, title = {The Online Target Date Assignment Problem}, volume = {3879}, booktitle = {Proc. 3rd Workshop on Approximation and Online Algorithms}, editor = {Erlebach, Thomas and Persiano, Giuseppe}, publisher = {Springer}, pages = {230 -- 243}, year = {2006}, language = {en} } @inproceedings{HillerKrumkeSalibaetal.2009, author = {Hiller, Benjamin and Krumke, Sven and Saliba, Sleman and Tuchscherer, Andreas}, title = {Randomized Online Algorithms for Dynamic Multi-Period Routing Problems}, booktitle = {Proceedings of MAPSP}, pages = {71 -- 73}, year = {2009}, language = {en} } @inproceedings{HuelsermannJaegerPoensgenetal.2003, author = {H{\"u}lsermann, Ralf and J{\"a}ger, Monika and Poensgen, Diana and Krumke, Sven and Rambau, J{\"o}rg and Tuchscherer, Andreas}, title = {Dynamic routing algorithms in transparent optical networks}, booktitle = {Proceedings of the 7th IFIP Working Conference on Optical Network Design \& Modelling (ONDM 2003)}, editor = {Cinkler, Tibor and Jakab, Tivadar and Tapolcai, J{\`a}nos and G{\`a}sp{\`a}r, Csaba}, publisher = {Kluwer Academic Press}, pages = {293 -- 312}, year = {2003}, language = {en} } @misc{HillerKrumkeSalibaetal.2009, author = {Hiller, Benjamin and Krumke, Sven and Saliba, Sleman and Tuchscherer, Andreas}, title = {Randomized Online Algorithms for Dynamic Multi-Period Routing Problems}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-11132}, number = {09-03}, year = {2009}, abstract = {The Dynamic Multi-Period Routing Problem DMPRP introduced by Angelelli et al. gives a model for a two-stage online-offline routing problem. At the beginning of each time period a set of customers becomes known. The customers need to be served either in the current time period or in the following. Postponed customers have to be served in the next time period. The decision whether to postpone a customer has to be done online. At the end of each time period, an optimal tour for the customers assigned to this period has to be computed and this computation can be done offline. The objective of the problem is to minimize the distance traveled over all planning periods assuming optimal routes for the customers selected in each period. We provide the first randomized online algorithms for the DMPRP which beat the known lower bounds for deterministic algorithms. For the special case of two planning periods we provide lower bounds on the competitive ratio of any randomized online algorithm against the oblivious adversary. We identify a randomized algorithm that achieves the optimal competitive ratio of \$\frac{1+\sqrt{2}}{2}\$ for two time periods on the real line. For three time periods, we give a randomized algorithm that is strictly better than any deterministic algorithm.}, language = {en} } @phdthesis{Krumke2002, author = {Krumke, Sven}, title = {Online Optimization: Competitive Analysis and Beyond}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-6925}, number = {02-25}, year = {2002}, abstract = {Traditional optimization techniques assume, in general, knowledge of all data of a problem instance. There are many cases in practice, however, where decisions have to be made before complete information about the data is available. In fact, it may be necessary to produce a part of the problem solution as soon as a new piece of information becomes known. This is called an \emph{online situation}, and an algorithm is termed \emph{online}, if it makes a decision (computes a partial solution) whenever a new piece of data requests an action. \emph{Competitive analysis} has become a standard yardstick to measure the quality of online algorithms. One compares the solution produced by an online algorithm to that of an optimal (clairvoyant) offline algorithm. An online algorithm is called \$c\$-competitive if on every input the solution it produces has cost'' at most \$c\$~times that of the optimal offline algorithm. This situation can be imagined as a game between an online player and a malicious adversary. Although competitive analysis is a worst-case analysis and henceforth pessimistic, it often allows important insights into the problem structure. One can obtain an idea about what kind of strategies are promising for real-world systems and why. On the other hand there are also cases where the offline adversary is simply too powerful and allows only trivial competitiveness results. This phenomenon is called hitting the triviality barrier''. We investigate several online problems by means of competitive analysis. We also introduce new concepts to overcome the weaknesses of the standard approach and to go beyond the triviality barrier.}, language = {en} }