TY - GEN A1 - Konjevod, Goran A1 - Krumke, Sven A1 - Marathe, Madhav T1 - Budget Constrained Minimum Cost Connected Medians N2 - Several practical instances of network design problems require the network to satisfy multiple constraints. In this paper, we address the \emph{Budget Constrained Connected Median Problem}: We are given an undirected graph $G = (V,E)$ with two different edge-weight functions $c$ (modeling the construction or communication cost) and $d$ (modeling the service distance), and a bound~$B$ on the total service distance. The goal is to find a subtree~$T$ of $G$ with minimum $c$-cost $c(T)$ subject to the constraint that the sum of the service distances of all the remaining nodes $v \in V\setminus T$ to their closest neighbor in~$T$ does not exceed the specified budget~$B$. This problem has applications in optical network design and the efficient maintenance of distributed databases. We formulate this problem as bicriteria network design problem, and present bicriteria approximation algorithms. We also prove lower bounds on the approximability of the problem that demonstrate that our performance ratios are close to best possible T3 - ZIB-Report - 00-10 KW - NP-hardness KW - Approximation Algorithms KW - Network Y1 - 2000 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-5783 ER - TY - GEN A1 - Grötschel, Martin A1 - Krumke, Sven A1 - Rambau, Jörg T1 - Wo bleibt der Aufzug? N2 - Dieser Artikel gibt eine allgemeinverständliche Einführung in die spezielle Problematik kombinatorischer Online-Problem am Beispiel der Fahrstuhlsteuerung. T3 - ZIB-Report - SC-99-29 KW - Aufzugsteuerung KW - Online-Optimierung KW - Echtzeit-Optimierung KW - kompetitive Analyse Y1 - 1999 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-4175 ER - TY - GEN A1 - Hauptmeier, Dietrich A1 - Krumke, Sven A1 - Rambau, Jörg A1 - Wirth., Hans-Christoph T1 - Euler is Standing in Line N2 - In this paper we study algorithms for ``Dial-a-Ride'' transportation problems. In the basic version of the problem we are given transportation jobs between the vertices of a graph and the goal is to find a shortest transportation that serves all the jobs. This problem is known to be NP-hard even on trees. We consider the extension when precedence relations between the jobs with the same source are given. Our results include a polynomial time algorithm on paths and an approximation algorithm on general graphs with a performance of~$9/4$. For trees we improve the performance to~$5/3$. T3 - ZIB-Report - SC-99-06 KW - NP-completeness KW - polynomial-time approximation algorithms KW - stacker-crane problem KW - vehicle routing KW - elevator system KW - Eulerian Cycle Y1 - 1999 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-3947 ER - TY - GEN A1 - Hauptmeier, Dietrich A1 - Krumke, Sven A1 - Rambau, Jörg T1 - The Online Dial-a-Ride Problem under Reasonable Load N2 - In this paper, we analyze algorithms for the online dial-a-ride problem with request sets that fulfill a certain worst-case restriction: roughly speaking, a set of requests for the online dial-a-ride problem is reasonable if the requests that come up in a sufficiently large time period can be served in a time period of at most the same length. This new notion is a stability criterion implying that the system is not overloaded. The new concept is used to analyze the online dial-a-ride problem for the minimization of the maximal resp.\ average flow time. Under reasonable load it is possible to distinguish the performance of two particular algorithms for this problem, which seems to be impossible by means of classical competitive analysis. T3 - ZIB-Report - SC-99-08 KW - online optimization KW - competitive analysis KW - elevator Y1 - 1999 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-3961 ER - TY - GEN A1 - Grötschel, Martin A1 - Hauptmeier, Dietrich A1 - Krumke, Sven A1 - Rambau, Jörg T1 - Simulation Studies for the Online-Dial-a-Ride Problem N2 - In a large distribution center of Herlitz AG, Berlin, we invesigated the elevator subsystem of the fully automated pallet transportation system. Each elevator may carry one pallet and has to serve eight levels. The goal is to minimize the average resp.\ the maximum flow time. The variants of this elevator control problem have been subject of recent theoretical research and are known as online-dial-a-ride problems. In this paper we investigate several online algorithms for several versions of online-dial-a-ride problems by means of a simulation program, developed on the basis of the simulation library AMSEL. We draw statistics from samples of randomly generated data providing for different load situations. Moreover, we provide preliminary studies with real production data for a system of five elevators connected by a conveyor circuit, as can be found at the Herlitz plant. We show which algorithms are best under certain load situations and which lead to break downs under particular circumstances. T3 - ZIB-Report - SC-99-09 KW - online optimization KW - competitive analysis KW - elevator KW - simulation studies Y1 - 1999 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-3976 ER - TY - GEN A1 - Hiller, Benjamin A1 - Krumke, Sven A1 - Rambau, Jörg T1 - Reoptimization Gaps versus Model Errors in Online-Dispatching of Service Units for ADAC N2 - Under high load, the automated dispatching of service vehicles for the German Automobile Association (ADAC) must reoptimize a dispatch for 100--150 vehicles and 400 requests in about ten seconds to near optimality. In the presence of service contractors, this can be achieved by the column generation algorithm ZIBDIP. In metropolitan areas, however, service contractors cannot be dispatched automatically because they may decline. The problem: a model without contractors yields larger optimality gaps within ten seconds. One way-out are simplified reoptimization models. These compute a short-term dispatch containing only some of the requests: unknown future requests will influence future service anyway. The simpler the models the better the gaps, but also the larger the model error. What is more significant: reoptimization gap or reoptimization model error? We answer this question in simulations on real-world ADAC data: only the new model ZIBDIP{\footnotesize dummy} can keep up with ZIBDIP. T3 - ZIB-Report - 04-17 KW - vehicle dispatching KW - soft time windows KW - online KW - real-time KW - ADAC KW - optimality gap KW - high load Y1 - 2004 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-7928 ER - TY - GEN A1 - Krumke, Sven A1 - Noltemeier, Hartmut A1 - Wirth, Hans-Christoph T1 - Graphentheoretische Konzepte und Algorithmen N2 - Das vorliegende Skript bietet eine Einf{ü}hrung in die Graphentheorie und graphentheoretische Algorithmen. Im zweiten Kapitel werden Grundbegriffe der Graphentheorie vorgestellt. Das dritte Kapitel besch{ä}ftigt sich mit der Existenz von Wegen in Graphen. Hier wird auch die L{ö}suung des ber{ü}hmten K{ö}nigsberger Br{ü}ckenproblems aufgezeigt und der Satz von Euler bewiesen. Im vierten Kapitel wird gezeigt, wie man auf einfache Weise die Zusammenhangskomponenten eines Graphen bestimmen kann. Im Kapitel sechs wird dann sp{ä}ter mit der Tiefensuche ein Verfahren vorgestellt, das schneller arbeitet und mit dessen Hilfe man noch mehr Informationen {ü}ber die Struktur eines Graphen gewinnen kann. In den folgenden Kapiteln werden Algorithmen vorgestellt, um minimale aufspannenden B{ä}ume, k{ü}rzeste Wege und maximale Fl{ü}sse in Graphen zu bestimmen. Am Ende des Skripts wird ein kurzer Einblick in die planaren Graphen und Graphhomomorphismen geboten. T3 - ZIB-Report - 00-19 KW - Graphen KW - Algorithmen KW - Komplexit{ä}t KW - kombinatorische Optimierung Y1 - 2000 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-5873 ER - TY - GEN A1 - Krumke, Sven A1 - Rambau, Jörg A1 - Weider, Steffen T1 - An Approximation Algorithm for the Non-Preemptive Capacitated Dial-a-Ride Problem N2 - In the Capacitated Dial-a-Ride Problem (CDARP) we are given a transportation network and a finite set of transportation jobs. Each job specifies the source and target location which are both part of the network. A server which can carry at most $C$~objects at a time can move on the transportation network in order to process the transportation requests. The problem CDARP consists of finding a shortest transportation for the jobs starting and ending at a designated start location. In this paper we are concerned with the restriction of CDARP to graphs which are simple paths. This setting arises for instance when modelling applications in elevator transportation systems. It is known that even for this restricted class of graphs CDARP is NP-hard to solve. We provide a polynomial time approximation algorithm that finds a transportion of length at most thrice the length of the optimal transportation. T3 - ZIB-Report - 00-53 KW - NP-completeness KW - polynomial-time approximation algorithms KW - stacker-crane problem KW - vehicle Y1 - 2000 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-6217 ER - TY - GEN A1 - Krumke, Sven A1 - Rambau, Jörg T1 - Online Optimierung N2 - Wie soll man einen Aufzug steuern, wenn man keine Informationen über zukünftige Fahraufträge besitzt? Soll man eine Bahncard kaufen, wenn die nächsten Bahnreisen noch unbekannt sind? In der klassischen kombinatorischen Optimierung geht man davon aus, daß die Daten jeder Probleminstanz vollständig gegeben sind. In vielen Fällen modelliert diese \emph{Offline-Optimierung} jedoch die Situationen aus Anwendungen nur ungenügend. Zahlreiche Problemstellungen in der Praxis sind in natürlicher Weise \emph{online}: Sie erfordern Entscheidungen, die unmittelbar und ohne Wissen zukünftiger Ereignisse getroffen werden müssen. Als ein Standardmittel zur Beurteilung von Online-Algorithmen hat sich die \emph{kompetitive Analyse} durchgesetzt. Dabei vergleicht man den Zielfunktionswert einer vom Online-Algorithmus generierten Lösung mit dem Wert einer optimalen Offline-Lösung. Mit Hilfe der kompetitiven Analyse werden im Skript Algorithmen zum Caching, Netzwerk-Routing, Scheduling und zu Transportaufgaben untersucht. Auch die Schwächen der kompetitiven Analyse werden aufgezeigt und alternative Analysekonzepte vorgestellt. Neben der theoretischen Seite werden auch die Anwendungen der Online-Optimierung in der Praxis, vor allem bei Problemen der innerbetrieblichen Logistik, beleuchtet. Bei der Steuerung automatischer Transportsysteme tritt eine Fülle von Online-Problemen auf. Hierbei werden an die Algorithmen oftmals weitere Anforderungen gestellt. So müssen Entscheidungen unter strikten Zeitbeschränkungen gefällt werden (Echtzeit-Anforderungen). Dieses Skript ist aus dem Online-Teil der Vorlesung -Ausgewählte Kapitel aus der ganzzahligen Optimierung- (Wintersemester~1999/2000) und der Vorlesung -Online Optimierung- (Sommersemester~2000) an der Technischen Universität Berlin entstanden. T3 - ZIB-Report - 00-55 KW - Kompetitive Analyse KW - Online Optimierung KW - Online Algorithmen Y1 - 2000 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-6238 ER - TY - GEN A1 - Grötschel, Martin A1 - Krumke, Sven A1 - Rambau, Jörg A1 - Winter, Thomas A1 - Zimmermann, Uwe T1 - Combinatorial Online Optimization in Real Time N2 - Optimization is the task of finding an optimum solution to a given problem. When the decision variables are discrete we speak of a combinatorial optimization problem. Such a problem is online when decisions have to be made before all data of the problem are known. And we speak of a real-time online problem when online decisions have to be computed within very tight time bounds. This paper surveys the are of combinatorial online and real-time optimization, it discusses, in particular, the concepts with which online and real-time algorithms can be analyzed. T3 - ZIB-Report - 01-16 KW - Online Optimization KW - Realtime Optimization KW - Competitive Analysis KW - Heuristics KW - Survey Y1 - 2001 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-6424 ER - TY - GEN A1 - Grötschel, Martin A1 - Krumke, Sven A1 - Rambau, Jörg T1 - Online Optimization of Complex Transportation Systems N2 - This paper discusses online optimization of real-world transportation systems. We concentrate on transportation problems arising in production and manufacturing processes, in particular in company internal logistics. We describe basic techniques to design online optimization algorithms for such systems, but our main focus is decision support for the planner: which online algorithm is the most appropriate one in a particular setting? We show by means of several examples that traditional methods for the evaluation of online algorithms often do not suffice to judge the strengths and weaknesses of online algorithms. We present modifications of well-known evaluation techniques and some new methods, and we argue that the selection of an online algorithm to be employed in practice should be based on a sound combination of several theoretical and practical evaluation criteria, including simulation. T3 - ZIB-Report - 01-17 Y1 - 2001 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-6438 ER - TY - GEN A1 - Krumke, Sven A1 - Rambau, Jörg A1 - Torres, Luis Miguel T1 - Real-Time Dispatching of Guided and Unguided Automobile Service Units with Soft Time Windows N2 - Given a set of service requests (events), a set of guided servers (units), and a set of unguided service contractors (conts), the vehicle dispatching problem {\sl vdp} is the task to find an assignment of events to units and conts as well as tours for all units starting at their current positions and ending at their home positions (dispatch) such that the total cost of the dispatch is minimized. The cost of a dispatch is the sum of unit costs, cont costs, and event costs. Unit costs consist of driving costs, service costs and overtime costs; cont costs consist of a fixed cost per service; event costs consist of late costs linear in the late time, which occur whenever the service of the event starts later than its deadline. The program \textsf{ZIBDIP} based on dynamic column generation and set partitioning yields solutions on heavy-load real-world instances (215 events, 95 units) in less than a minute that are no worse than 1\% from optimum on state-of-the-art personal computers. T3 - ZIB-Report - 01-22 KW - vehicle dispatching KW - soft time windows KW - real-time KW - column generation KW - pricing KW - branch and bound KW - real world data KW - ADAC Y1 - 2001 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-6484 ER - TY - GEN A1 - Krumke, Sven A1 - Paepe, Willem de A1 - Rambau, Jörg A1 - Stougie, Leen T1 - Online Bin-Coloring N2 - We introduce a new problem that was motivated by a (more complicated) problem arising in a robotized assembly enviroment. The bin coloring problem is to pack unit size colored items into bins, such that the maximum number of different colors per bin is minimized. Each bin has size~$B\in\mathbb{N}$. The packing process is subject to the constraint that at any moment in time at most $q\in\mathbb{N}$ bins may be partially filled. Moreover, bins may only be closed if they are filled completely. An online algorithm must pack each item must be packed without knowledge of any future items. We investigate the existence of competitive online algorithms for the online uniform binpacking problem. We show upper bounds for the bin coloring problem. We prove an upper bound of $3q$ - 1 and a lower bound of $2q$ for the competitive ratio of a natural greedy-type algorithm, and show that surprisingly a trivial algorithm which uses only one open bin has a strictly better competitive ratio of $2q$ - 1. Morever, we show that any deterministic algorithm has a competitive ratio $\Omega (q)$ and that randomization does not improve this lower bound even when the adversary is oblivious. T3 - ZIB-Report - 01-07 KW - Online Optimization KW - randomized algorithms KW - lower bounds KW - competitive analysis Y1 - 2001 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-6338 ER - 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 -