@inproceedings{HillerKrumkeRambau2004, author = {Hiller, Benjamin and Krumke, Sven and Rambau, J{\"o}rg}, title = {Reoptimization Gaps versus Model Errors in Online-Dispatching of Service Units for ADAC}, volume = {18}, booktitle = {Latin-American Conference on Combinatorics, Graphs and Applications, Santiago de Chile 2004}, editor = {Liebling, T. and Duran, G. and Matamala, M.}, pages = {157 -- 163}, year = {2004}, language = {en} } @article{HillerKrumkeRambau2006, author = {Hiller, Benjamin and Krumke, Sven and Rambau, J{\"o}rg}, title = {Reoptimization Gaps versus Model Errors in Online-Dispatching of Service Units for ADAC}, volume = {154}, journal = {DISAM}, number = {13}, pages = {1897 -- 1907}, year = {2006}, language = {en} } @misc{AscheuerGroetschelKrumkeetal.1998, author = {Ascheuer, Norbert and Gr{\"o}tschel, Martin and Krumke, Sven and Rambau, J{\"o}rg}, title = {Combinatorial Online Optimization}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-3674}, number = {SC-98-24}, year = {1998}, abstract = {In ``classical'' optimization, all data of a problem instance are considered given. The standard theory and the usual algorithmic techniques apply to such cases only. Online optimization is different. Many decisions have to be made before all data are available. In addition, decisions once made cannot be changed. How should one act ``best'' in such an environment? In this paper we survey online problems coming up in combinatorial optimization. We first outline theoretical concepts, such as competitiveness against various adversaries, to analyze online problems and algorithms. The focus, however, lies on real-world applications. We report, in particular, on theoretical investigations and our practical experience with problems arising in transportation and the automatic handling of material.}, language = {en} } @misc{AscheuerKrumkeRambau1998, author = {Ascheuer, Norbert and Krumke, Sven and Rambau, J{\"o}rg}, title = {The Online Transportation Problem: Competitive Scheduling of Elevators}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-3779}, number = {SC-98-34}, year = {1998}, abstract = {In this paper we consider the following online transportation problem (\textsc{Oltp}): Objects are to be transported between the vertices of a given graph. Transportation requests arrive online, specifying the objects to be transported and the corresponding source and target vertex. These requests are to be handled by a server which commences its work at a designated origin vertex and which picks up and drops objects at their starts and destinations. After the end of its service the server returns to its start. The goal of \textsc{Oltp} is to come up with a transportation schedule for the server which finishes as early as possible. We first show a lower bound of~\$5/3\$ for the competitive ratio of any deterministic algorithm. We then analyze two simple and natural strategies which we call \textsf{REPLAN} and \textsf{IGNORE}. \textsf{REPLAN} completely discards its schedule and recomputes a new one when a new request arrives. \textsf{IGNORE} always runs a (locally optimal) schedule for a set of known requests and ignores all new requests until this schedule is completed. We show that both strategies, \textsf{REPLAN} and \textsf{IGNORE}, are \$5/2\$-competitive. We also present a somewhat less natural strategy \textsf{SLEEP}, which in contrast to the other two strategies may leave the server idle from time to time although unserved requests are known. We also establish a competitive ratio of~\$5/2\$ for the algorithm \textsf{SLEEP}. Our results are extended to the case of ``open schedules'' where the server is not required to return to its start position at the end of its service.}, language = {en} } @incollection{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}, booktitle = {Online Optimization of Large Scale Systems}, editor = {Gr{\"o}tschel, Martin and Krumke, Sven and Rambau, J{\"o}rg}, publisher = {Springer}, pages = {679 -- 704}, year = {2001}, language = {en} } @incollection{GroetschelKrumkeRambau2001, author = {Gr{\"o}tschel, Martin and Krumke, Sven and Rambau, J{\"o}rg}, title = {Online Optimization of Complex Transportation Systems}, booktitle = {Online Optimization of Large Scale Systems}, editor = {Gr{\"o}tschel, Martin and Krumke, Sven and Rambau, J{\"o}rg}, publisher = {Springer}, pages = {705 -- 729}, year = {2001}, language = {en} } @misc{CojaOghlanKrumkeNierhoff2003, author = {Coja-Oghlan, Amin and Krumke, Sven and Nierhoff, Till}, title = {Average Case Analysis of a Hard Dial-a-Ride Problem}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-7348}, number = {03-12}, year = {2003}, abstract = {In the dial-a-ride-problem (DARP) objects have to be moved between given sources and destinations in a transportation network by means of a server. The goal is to find a shortest transportation for the server. We study the DARP when the underlying transportation network forms a caterpillar. This special case is strongly NP-hard in the worst case. We prove that in a probabilistic setting there exists a polynomial time algorithm which almost surely finds an optimal solution. Moreover, with high probability the optimality of the solution found can be certified efficiently. We also examine the complexity of the DARP in a semi-random setting and in the unweighted case.}, language = {en} } @misc{KrumkeRambauTorres2002, author = {Krumke, Sven and Rambau, J{\"o}rg and Torres, Luis Miguel}, title = {Online-Dispatching of Automobile Service Units}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-7117}, number = {02-44}, year = {2002}, abstract = {We present an online algorithm for a real-world vehicle dispatching problem at ADAC, the German Automobile Association.}, language = {en} } @misc{HuelsermannJaegerKrumkeetal.2002, author = {H{\"u}lsermann, Ralf and J{\"a}ger, Monika and Krumke, Sven and Poensgen, Diana and Rambau, J{\"o}rg and Tuchscherer, Andreas}, title = {Dynamic Routing Algorithms in Transparent Optical Networks An Experimental Study Based on Real Data}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-7025}, number = {02-35}, year = {2002}, abstract = {Today's telecommunication networks are configured statically. Whenever a connection is established, the customer has permanent access to it. However, it is observed that usually the connection is not used continuously. At this point, dynamic provisioning could increase the utilization of network resources. WDM based Optical Transport Networks (OTNs) will shortly allow for fast dynamic network reconfiguration. This enables optical broadband leased line services on demand. Since service requests competing for network resources may lead to service blocking, it is vital to use appropriate strategies for routing and wavelength assignment in transparent optical networks. We simulate the service blocking probabilities of various dynamic algorithms for this problem using a well-founded traffic model for two realistic networks. One of the algorithms using shortest path routings performs best on all instances. Surprisingly, the tie-breaking rule between equally short paths in different wavelengths decides between success or failure.}, language = {en} } @misc{KrumkeLauraLipmannetal.2002, author = {Krumke, Sven and Laura, Luigi and Lipmann, Maarten and Marchetti-Spaccamela, Alberto and Paepe, Willem de and Poensgen, Diana and Stougie, Leen}, title = {Non-Abusiveness Helps: An O(1)-Competitive Algorithm for Minimizing the Maximum Flow Time in the Online Traveling Salesman Problem}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-7038}, number = {02-36}, year = {2002}, abstract = {In the online traveling salesman problem \$OLTSP\$ requests for visits to cities arrive online while the salesman is traveling. We study the \$F{\_max}-OLTSP\$ where the objective is to minimize the maximum flow time. This objective is particularly interesting for applications. Unfortunately, there can be no competitive algorithm, neither deterministic nor randomized. Hence, competitive analysis fails to distinguish online algorithms. Not even resource augmentation which is helpful in scheduling works as a remedy. This unsatisfactory situation motivates the search for alternative analysis methods. We introduce a natural restriction on the adversary for the \$F{\_max}-OLTSP\$ on the real line. A \emph{non-abusive adversary} may only move in a direction if there are yet unserved requests on this side. Our main result is an algorithm which achieves a constant competitive ratio against the non-abusive adversary.}, language = {en} } @misc{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: Online Dispatching Of Service Vehicles in Real Time}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-6858}, number = {02-18}, year = {2002}, abstract = {Combinatorial online optimization is an area with lots of applications and potential for significant progress, both in theory and practice. In this short note we sketch the ADACproblem, a typical large-scale online optimization problem, discuss some theoretical and pratical issues coming up, and explain, very briefly, how we approach this problem mathematically. Online problems are a battlefield of heuristics with many strong claims about their solution quality. We indicate that a stronger problem orientation and the use of a little more mathematics may yield.}, language = {en} } @misc{KrumkePoensgen2002, author = {Krumke, Sven and Poensgen, Diana}, title = {Online Call Admission in Optical Networks with Larger Wavelength Demands}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-6890}, number = {02-22}, year = {2002}, abstract = {In the problem of \emph{Online Call Admission in Optical Networks}, briefly called \textsc{oca}, we are given a graph \$G=(V,E)\$ together with a set of wavelengths~\$W\$ and a finite sequence \$\sigma=r_1,r_2,\dots\$ of calls which arrive in an online fashion. Each call~\$r_j\$ specifies a pair of nodes to be connected and an integral demand indicating the number of required lightpaths. A lightpath is a path in~\$G\$ together with a wavelength~\$\lambda \in W\$. Upon arrival of a call, an online algorithm must decide immediately and irrevocably whether to accept or to reject the call without any knowledge of calls which appear later in the sequence. If the call is accepted, the algorithm must provide the requested number of lightpaths to connect the specified nodes. The essential restriction is the wavelength conflict constraint: each wavelength is available only once per edge, which implies that two lightpaths sharing an edge must have different wavelengths. Each accepted call contributes a benefit equal to its demand to the overall profit. The objective in \textsc{oca} is to maximize the overall profit. Competitive algorithms for \textsc{oca} have been known for the special case where every call requests just a single lightpath. In this paper we present the first competitive online algorithms for the general case of larger demands.}, language = {en} } @misc{KrumkeLipmannPaepeetal.2002, author = {Krumke, Sven and Lipmann, Maarten and Paepe, Willem de and Poensgen, Diana and Rambau, J{\"o}rg and Stougie, Leen and Woeginger, Gerhard}, title = {How to Cut a Cake Almost Fairly}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-6905}, number = {02-23}, year = {2002}, abstract = {In the cake cutting problem, \$n\ge2\$ players want to cut a cake into \$n\$ pieces so that every player gets a ``fair'' share of the cake by his own measure. We describe a protocol with \$n-1\$~cuts in which each player can enforce to get a share of at least~\$1/(2n-2)\$. Moreover we show that no protocol with \$n-1\$~cuts can guarantee a better fraction.}, language = {en} } @misc{KrumkeMarathePoensgenetal.2002, author = {Krumke, Sven and Marathe, Madhav and Poensgen, Diana and Ravi, Sekharipuram S. and Wirth, Hans-Christoph}, title = {Budgeted Maximal Graph Coverage}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-6918}, number = {02-24}, year = {2002}, abstract = {An instance of the \emph{maximum coverage} problem is given by a set of weighted ground elements and a cost weighted family of subsets of the ground element set. The goal is to select a subfamily of total cost of at most that of a given budget maximizing the weight of the covered elements. We formulate the problem on graphs: In this situation the set of ground elements is specified by the nodes of a graph, while the family of covering sets is restricted to connected subgraphs. We show that on general graphs the problem is polynomial time solvable if restricted to sets of size at most~\$2\$, but becomes NP-hard if sets of size~\$3\$ are permitted. On trees, we prove polynomial time solvability if each node appears in a fixed number of sets. In contrast, if vertices are allowed to appear an unbounded number of times, the problem is NP-hard even on stars. We finally give polynomial time algorithms for special cases where the subgraphs form paths and the host graph is a line, a cycle or a star.}, language = {en} } @misc{Krumke2000, author = {Krumke, Sven}, title = {News from the Online Traveling Repairman}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-5767}, number = {00-08}, year = {2000}, abstract = {The traveling repairman problem (TRP) is a variant of the famous traveling salesman problem (TSP). The objective for the TRP is to minimize the latency, that is the the weighted sum of completion times of the cities, where the completion time of a city is defined to be the time in the tour before the city is reached. In the online traveling repairman problem (OLTRP) requests for visits to cities (points in a metric space) arrive online while the repairman is traveling. We analyze the performance of algorithms using competitive analysis, where the cost of an online algorithm is compared to that of an optimal offline algorithm. An optimal offline algorithm knows the entire request sequence in advance and can serve it with minimum cost. Recently, Feuerstein and Stougie presented a \$9\$-competitive algorithm for the OLTRP on the real line. In this paper we show how to use techniques from online-scheduling to obtain an \$8\$-competitive deterministic algorithm which works for any metric space. We also present a randomized algorithm which has a competitive ratio of \$\frac{4}{\ln 2}\approx 5.7708\$ against an oblivious adversary. All of our results also hold for the ``dial-a-ride'' generalization of the OLTRP, where objects have to be picked up and delivered by a server.}, language = {en} } @misc{BlomKrumkePaepeetal.2000, author = {Blom, Michiel and Krumke, Sven and Paepe, Willem de and Stougie, Leen}, title = {The Online-TSP Against Fair Adversaries}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-5779}, number = {00-09}, year = {2000}, abstract = {In the online traveling salesman problem requests for visits to cities (points in a metric space) arrive online while the salesman is traveling. The salesman moves at no more than unit speed and starts and ends his work at a designated origin. The objective is to find a routing for the salesman which finishes as early as possible. Performance of algorithms is measured through their competitive ratio, comparing the outcome of the algorithms with that of an adversary who provides the problem instance and therefore is able to achieve the optimal offline solution. Objections against such omnipotent adversaries have lead us to devise an adversary that is in a natural way, in the context of routing problems, more restricted in power. For the exposition we consider the online traveling salesman problem on the metric space given by the non-negative part of the real line. We show that a very natural strategy is~\$3/2\$-competitive against the conventional adversary, which matches the lower bound on competitive ratios achievable for algorithms for this problem. Against the more ``\emph{fair adversary}'', that we propose, we show that there exists an algorithm with competitive ratio \$\frac{1+\sqrt{17}}{4}\approx 1.28\$ and provide a matching lower bound. We also show competitiveness results for a special class of algorithms (called zealous algorithms) that do not allow waiting time for the server as long as there are requests unserved.}, language = {en} } @misc{KonjevodKrumkeMarathe2000, author = {Konjevod, Goran and Krumke, Sven and Marathe, Madhav}, title = {Budget Constrained Minimum Cost Connected Medians}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-5783}, number = {00-10}, year = {2000}, abstract = {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}, language = {en} } @misc{GroetschelKrumkeRambau1999, author = {Gr{\"o}tschel, Martin and Krumke, Sven and Rambau, J{\"o}rg}, title = {Wo bleibt der Aufzug?}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-4175}, number = {SC-99-29}, year = {1999}, abstract = {Dieser Artikel gibt eine allgemeinverst{\"a}ndliche Einf{\"u}hrung in die spezielle Problematik kombinatorischer Online-Problem am Beispiel der Fahrstuhlsteuerung.}, language = {de} } @misc{HauptmeierKrumkeRambauetal.1999, author = {Hauptmeier, Dietrich and Krumke, Sven and Rambau, J{\"o}rg and Wirth., Hans-Christoph}, title = {Euler is Standing in Line}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-3947}, number = {SC-99-06}, year = {1999}, abstract = {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\$.}, language = {en} } @misc{HauptmeierKrumkeRambau1999, author = {Hauptmeier, Dietrich and Krumke, Sven and Rambau, J{\"o}rg}, title = {The Online Dial-a-Ride Problem under Reasonable Load}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-3961}, number = {SC-99-08}, year = {1999}, abstract = {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.}, language = {en} } @misc{GroetschelHauptmeierKrumkeetal.1999, author = {Gr{\"o}tschel, Martin and Hauptmeier, Dietrich and Krumke, Sven and Rambau, J{\"o}rg}, title = {Simulation Studies for the Online-Dial-a-Ride Problem}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-3976}, number = {SC-99-09}, year = {1999}, abstract = {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.}, language = {en} } @misc{HillerKrumkeRambau2004, author = {Hiller, Benjamin and Krumke, Sven and Rambau, J{\"o}rg}, title = {Reoptimization Gaps versus Model Errors in Online-Dispatching of Service Units for ADAC}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-7928}, number = {04-17}, year = {2004}, abstract = {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.}, language = {en} }