@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{HeinzKaibelPeinhardtetal., author = {Heinz, Stefan and Kaibel, Volker and Peinhardt, Matthias and Rambau, J{\"o}rg and Tuchscherer, Andreas}, title = {LP-Based Local Approximation for Markov Decision Problems}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-9131}, number = {06-20}, abstract = {The standard computational methods for computing the optimal value functions of Markov Decision Problems (MDP) require the exploration of the entire state space. This is practically infeasible for applications with huge numbers of states as they arise, e.\,g., from modeling the decisions in online optimization problems by MDPs. Exploiting column generation techniques, we propose and apply an LP-based method to determine an \$\varepsilon\$-approximation of the optimal value function at a given state by inspecting only states in a small neighborhood. In the context of online optimization problems, we use these methods in order to evaluate the quality of concrete policies with respect to given initial states. Moreover, the tools can also be used to obtain evidence of the impact of single decisions. This way, they can be utilized in the design of policies.}, language = {en} } @misc{GroetschelKrumkeRambauetal., 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}, 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} }