@misc{KrumkeRambauWeider2000, author = {Krumke, Sven and Rambau, J{\"o}rg and Weider, Steffen}, title = {An Approximation Algorithm for the Non-Preemptive Capacitated Dial-a-Ride Problem}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-6217}, number = {00-53}, year = {2000}, abstract = {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.}, language = {en} } @misc{KrumkeRambau2000, author = {Krumke, Sven and Rambau, J{\"o}rg}, title = {Online Optimierung}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-6238}, number = {00-55}, year = {2000}, abstract = {Wie soll man einen Aufzug steuern, wenn man keine Informationen {\"u}ber zuk{\"u}nftige Fahrauftr{\"a}ge besitzt? Soll man eine Bahncard kaufen, wenn die n{\"a}chsten Bahnreisen noch unbekannt sind? In der klassischen kombinatorischen Optimierung geht man davon aus, daß die Daten jeder Probleminstanz vollst{\"a}ndig gegeben sind. In vielen F{\"a}llen modelliert diese \emph{Offline-Optimierung} jedoch die Situationen aus Anwendungen nur ungen{\"u}gend. Zahlreiche Problemstellungen in der Praxis sind in nat{\"u}rlicher Weise \emph{online}: Sie erfordern Entscheidungen, die unmittelbar und ohne Wissen zuk{\"u}nftiger Ereignisse getroffen werden m{\"u}ssen. 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 L{\"o}sung mit dem Wert einer optimalen Offline-L{\"o}sung. Mit Hilfe der kompetitiven Analyse werden im Skript Algorithmen zum Caching, Netzwerk-Routing, Scheduling und zu Transportaufgaben untersucht. Auch die Schw{\"a}chen 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 F{\"u}lle von Online-Problemen auf. Hierbei werden an die Algorithmen oftmals weitere Anforderungen gestellt. So m{\"u}ssen Entscheidungen unter strikten Zeitbeschr{\"a}nkungen gef{\"a}llt werden (Echtzeit-Anforderungen). Dieses Skript ist aus dem Online-Teil der Vorlesung -Ausgew{\"a}hlte Kapitel aus der ganzzahligen Optimierung- (Wintersemester~1999/2000) und der Vorlesung -Online Optimierung- (Sommersemester~2000) an der Technischen Universit{\"a}t Berlin entstanden.}, language = {de} } @misc{GroetschelKrumkeRambauetal.2001, author = {Gr{\"o}tschel, Martin and Krumke, Sven and Rambau, J{\"o}rg and Winter, Thomas and Zimmermann, Uwe}, title = {Combinatorial Online Optimization in Real Time}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-6424}, number = {01-16}, year = {2001}, abstract = {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.}, language = {en} } @misc{GroetschelKrumkeRambau2001, author = {Gr{\"o}tschel, Martin and Krumke, Sven and Rambau, J{\"o}rg}, title = {Online Optimization of Complex Transportation Systems}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-6438}, number = {01-17}, year = {2001}, abstract = {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.}, language = {en} } @misc{KrumkeRambauTorres2001, author = {Krumke, Sven and Rambau, J{\"o}rg and Torres, Luis Miguel}, title = {Real-Time Dispatching of Guided and Unguided Automobile Service Units with Soft Time Windows}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-6484}, number = {01-22}, year = {2001}, abstract = {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.}, language = {en} } @misc{KrumkePaepeRambauetal.2001, author = {Krumke, Sven and Paepe, Willem de and Rambau, J{\"o}rg and Stougie, Leen}, title = {Online Bin-Coloring}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-6338}, number = {01-07}, year = {2001}, abstract = {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.}, language = {en} } @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} } @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} }