@misc{GroetschelKrumkeRambauetal., 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}, 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{KrumkeRambauTorres, 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}, 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., 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}, 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{BorndoerferGroetschelLoebel, author = {Bornd{\"o}rfer, Ralf and Gr{\"o}tschel, Martin and L{\"o}bel, Andreas}, title = {Duty Scheduling in Public Transit}, doi = {10.1007/978-3-642-55753-8_50}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-6286}, number = {01-02}, abstract = {This article is about \emph{adaptive column generation techniques} for the solution of duty scheduling problems in public transit. The current optimization status is exploited in an adaptive approach to guide the subroutines for duty generation, LP resolution, and schedule construction toward relevant parts of a large problem. Computational results for three European scenarios are reported.}, language = {en} }