TY - GEN A1 - Krumke, Sven A1 - Rambau, Jörg T1 - Probieren geht über Studieren? Entscheidungshilfen für kombinatorische Online-Optimierungsprobleme in der innerbetrieblichen Logistik N2 - Die Automatisierung von innerbetrieblicher Logistik erfordert -- über die physikalische Steuerung von Geräten hinaus -- auch eine effiziente Organisation der Transporte: ein Aufgabenfeld der kombinatorischen Optimierung. Dieser Artikel illustriert anhand von konkreten Aufgabenstellungen die Online-Problematik (unvollständiges Wissen) sowie die Echtzeit-Problematik (beschränkte Rechenzeit), auf die man in der innerbetrieblichen Logistik trifft. Der Text gibt einen Überblick über allgemeine Konstruktionsprinzipien für Online-Algorithmen und Bewertungsmethoden, die bei der Entscheidung helfen, welche Algorithmen für eine vorliegende Problemstellung geeignet sind. T3 - ZIB-Report - 02-05 KW - Logistik KW - Hochregallagerbediengeräte KW - Kommissioniermobile KW - Aufzüge KW - Online-Optimierung KW - Echtzeit-Optimierung KW - Simulation KW - kompetitive Analyse Y1 - 2002 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-6723 ER - TY - JOUR A1 - Grötschel, Martin A1 - Krumke, Sven A1 - Rambau, Jörg A1 - Torres, Luis Miguel T1 - Making the Yellow Angels Fly JF - SIAM News Y1 - 2002 VL - 35 IS - 4 SP - 1,10,11 ER - TY - JOUR A1 - Grötschel, Martin A1 - Krumke, Sven A1 - Rambau, Jörg T1 - Wo bleibt der Aufzug? JF - OR News Y1 - 1999 VL - 5 SP - 11 EP - 13 ER - TY - JOUR A1 - Grötschel, Martin A1 - Krumke, Sven A1 - Rambau, Jörg T1 - Wo bleibt der Aufzug? JF - OR News Y1 - 2006 VL - Sonderausgabe SP - 70 EP - 72 ER - TY - CHAP A1 - Ascheuer, Norbert A1 - Grötschel, Martin A1 - Krumke, Sven A1 - Rambau, Jörg ED - Kall, Peter ED - Lüthi, Hans-Jakob T1 - Combinatorial Online Optimization T2 - Operations Research Proceedings 1998. Selected Papers of the International Conference on Operations Research Zurich, August 31 – September 3, 1998 Y1 - 1999 SP - 21 EP - 37 PB - Springer ER - TY - CHAP A1 - Heinz, Stefan A1 - Krumke, Sven A1 - Megow, Nicole A1 - Rambau, Jörg A1 - Tuchscherer, Andreas A1 - Vredeveld, Tjark ED - Erlebach, Thomas ED - Persiano, Giuseppe T1 - The Online Target Date Assignment Problem T2 - Proc. 3rd Workshop on Approximation and Online Algorithms Y1 - 2006 UR - http://opus.kobv.de/zib/volltexte/2005/894/ VL - 3879 SP - 230 EP - 243 PB - Springer ER - TY - CHAP A1 - Hiller, Benjamin A1 - Krumke, Sven A1 - Saliba, Sleman A1 - Tuchscherer, Andreas T1 - Randomized Online Algorithms for Dynamic Multi-Period Routing Problems T2 - Proceedings of MAPSP Y1 - 2009 SP - 71 EP - 73 ER - TY - CHAP A1 - Hülsermann, Ralf A1 - Jäger, Monika A1 - Poensgen, Diana A1 - Krumke, Sven A1 - Rambau, Jörg A1 - Tuchscherer, Andreas ED - Cinkler, Tibor ED - Jakab, Tivadar ED - Tapolcai, Jànos ED - Gàspàr, Csaba T1 - Dynamic routing algorithms in transparent optical networks T2 - Proceedings of the 7th IFIP Working Conference on Optical Network Design & Modelling (ONDM 2003) Y1 - 2003 UR - http://opus.kobv.de/zib/volltexte/2002/703/ SP - 293 EP - 312 PB - Kluwer Academic Press ER - TY - GEN A1 - Hiller, Benjamin A1 - Krumke, Sven A1 - Saliba, Sleman A1 - Tuchscherer, Andreas T1 - Randomized Online Algorithms for Dynamic Multi-Period Routing Problems N2 - 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. T3 - ZIB-Report - 09-03 KW - Online-Optimierung KW - Randomisierte Algorithmen KW - Zweistufiges Problem KW - Traveling-Salesman-Problem KW - online optimization KW - randomized algorithm KW - two-stage problem KW - traveling salesman problem Y1 - 2009 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-11132 SN - 1438-0064 ER - TY - THES A1 - Krumke, Sven T1 - Online Optimization: Competitive Analysis and Beyond N2 - 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. T3 - ZIB-Report - 02-25 KW - competitive analysis KW - online optimization KW - online algorithm KW - approximation algorithm Y1 - 2002 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-6925 ER -