KLUEDO RSS FeedNeueste Dokumente / Latest documents
https://kluedo.ub.uni-kl.de/index/index/
Sun, 02 Oct 2011 10:37:35 +0100Sun, 02 Oct 2011 10:37:35 +0100Efficient Computation of Equilibria in Bottleneck Games via Game Transformation
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2283
Thomas L. Werth; Heike Sperber; Sven O. Krumkereporthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2283Thu, 10 Feb 2011 10:37:35 +0100Train Marshalling Problem - Algorithms and Bounds -
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2263
The Train Marshalling Problem consists of rearranging an incoming train in a marshalling yard in such a way that cars with the same destinations appear consecutively in the final train and the number of needed sorting tracks is minimized. Besides an initial roll-in operation, just one pull-out operation is allowed. This problem was introduced by Dahlhaus et al. who also showed that the problem is NP-complete. In this paper, we provide a new lower bound on the optimal objective value by partitioning an appropriate interval graph. Furthermore, we consider the corresponding online problem, for which we provide upper and lower bounds on the competitiveness and a corresponding optimal deterministic online algorithm. We provide an experimental evaluation of our lower bound and algorithm which shows the practical tightness of the results.Katharina Beygang; Sven O. Krumke; Florian Dahmsreporthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2263Mon, 10 Jan 2011 10:26:59 +0100Dynamic Multi-Period Routing With Two Classes
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2228
In the Dynamic Multi-Period Routing Problem, one is given a new set of requests at the beginning of each time period. The aim is to assign requests to dates such that all requests are fulfilled by their deadline and such that the total cost for fulling the requests is minimized. We consider a generalization of the problem which allows two classes of requests: The 1st class requests can only be fulfilled by the 1st class server, whereas the 2nd class requests can be fulfilled by either the 1st or 2nd class server. For each tour, the 1st class server incurs a cost that is alpha times the cost of the 2nd class server, and in each period, only one server can be used. At the beginning of each period, the new requests need to be assigned to service dates. The aim is to make these assignments such that the sum of the costs for all tours over the planning horizon is minimized. We study the problem with requests located on the nonnegative real line and prove that there cannot be a deterministic online algorithm with a competitive ratio better than alpha. However, if we require the difference between release and deadline date to be equal for all requests, we can show that there is a min{2*alpha, 2 + 2/alpha}-competitive algorithm.Sven O. Krumke; Christiane Zeckreporthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2228Mon, 16 Aug 2010 15:41:41 +0200Online Delay Management
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2197
We present extensions to the Online Delay Management Problem on a Single Train Line. While a train travels along the line, it learns at each station how many of the passengers wanting to board the train have a delay of delta. If the train does not wait for them, they get delayed even more since they have to wait for the next train. Otherwise, the train waits and those passengers who were on time are delayed by delta. The problem consists in deciding when to wait in order to minimize the total delay of all passengers on the train line. We provide an improved lower bound on the competitive ratio of any deterministic online algorithm solving the problem using game tree evaluation. For the extension of the original model to two possible passenger delays delta_1 and delta_2, we present a 3-competitive deterministic online algorithm. Moreover, we study an objective function modeling the refund system of the German national railway company, which pays passengers with a delay of at least Delta a part of their ticket price back. In this setting, the aim is to maximize the profit. We show that there cannot be a deterministic competitive online algorithm for this problem and present a 2-competitive randomized algorithm.Sven O. Krumke; Clemens Thielen; Christiane Zeckreporthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2197Thu, 08 Jul 2010 08:52:34 +0200Generalized Max Flow in Series-Parallel Graphs
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2179
In the generalized max flow problem, the aim is to find a maximum flow in a generalized network, i.e., a network with multipliers on the arcs that specify which portion of the flow entering an arc at its tail node reaches its head node. We consider this problem for the class of series-parallel graphs. First, we study the continuous case of the problem and prove that it can be solved using a greedy approach. Based on this result, we present a combinatorial algorithm that runs in O(m*m) time and a dynamic programming algorithm with running time O(m*log(m)) that only computes the maximum flow value but not the flow itself. For the integral version of the problem, which is known to be NP-complete, we present a pseudo-polynomial algorithm.Katharina Beygang; Sven O. Krumke; Christiane Zeckreporthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2179Tue, 09 Mar 2010 07:57:55 +0100Selfish Bin Coloring
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2145
We introduce a new game, the so-called bin coloring game, in which selfish players control colored items and each player aims at packing its item into a bin with as few different colors as possible. We establish the existence of Nash and strong as well as weakly and strictly Pareto optimal equilibria in these games in the cases of capacitated and uncapacitated bins. For both kinds of games we determine the prices of anarchy and stability concerning those four equilibrium concepts. Furthermore, we show that extreme Nash equilibria, those with minimal or maximal number of colors in a bin, can be found in time polynomial in the number of items for the uncapcitated case.Leah Epstein; Sven O. Krumke; Asaf Levin; Heike Sperberreporthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2145Fri, 16 Oct 2009 08:55:08 +0200