TY - GEN A1 - Hiller, Benjamin A1 - Krumke, Sven A1 - Saliba, Sleman A1 - Tuchscherer, Andreas T1 - Randomized Online Algorithms for Dynamic Multi-Period Routing Problems N2 - 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. T3 - ZIB-Report - 09-03 KW - Online-Optimierung KW - Randomisierte Algorithmen KW - Zweistufiges Problem KW - Traveling-Salesman-Problem KW - online optimization KW - randomized algorithm KW - two-stage problem KW - traveling salesman problem Y1 - 2009 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-11132 SN - 1438-0064 ER - TY - GEN A1 - Grötschel, Martin A1 - Hiller, Benjamin A1 - Tuchscherer, Andreas T1 - Combinatorial Online Optimization: Elevators & Yellow Angels N2 - In \emph{classical optimization} it is assumed that full information about the problem to be solved is given. This, in particular, includes that all data are at hand. The real world may not be so nice'' to optimizers. Some problem constraints may not be known, the data may be corrupted, or some data may not be available at the moments when decisions have to be made. The last issue is the subject of \emph{online optimization} which will be addressed here. We explain some theory that has been developed to cope with such situations and provide examples from practice where unavailable information is not the result of bad data handling but an inevitable phenomenon. T3 - ZIB-Report - 07-36 KW - Online-Optimierung KW - Aufzugssteuerung KW - Dispatching von Fahrzeugen KW - online optimization KW - elevator control KW - vehicle dispatching Y1 - 2007 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-10360 SN - 1438-0064 ER - TY - GEN A1 - Hiller, Benjamin A1 - Tuchscherer, Andreas T1 - Real-Time Destination-Call Elevator Group Control on Embedded Microcontrollers N2 - Algorithmic control of elevator systems has been studied for a long time. More recently, a new paradigm for elevator control has emerged. In destination call systems, the passenger specifies not only the direction of his ride, but the destination floor. Such a destination call system is very interesting from an optimization point of view, since more information is available earlier, which should allow improved planning. However, the real-world destination call system envisioned by our industry partner requires that each destination call (i.e. passenger) is assigned to a serving elevator immediately. This early assignment restricts the potential gained from the destination information. Another aspect is that there is no way to specify the destination floor in the cabin. Therefore, the elevator has to stop on every destination floor of an assigned call, although the passenger may not have boarded the cabin, e.g. due to insufficient capacity. In this paper we introduce a new destination call control algorithm suited to this setting. Since the control algorithm for an entire elevator group has to run on embedded microprocessors, computing resources are very scarce. Since exact optimization is not feasible on such hardware, the algorithm is an insertion heuristic using a non-trivial data structure to maintain a set of tours. To assess the performance of our algorithm, we compare it to similar and more powerful algorithms by simulation. We also compare to algorithms for a conventional system and with a more idealized destination call system. This gives an indication of the relative potentials of these systems. In particular, we assess how the above real-world restrictions influence performance. The algorithm introduced has been implemented by our industry partner for real-world use. T3 - ZIB-Report - 07-26 KW - Aufzugsgruppensteuerung KW - Zielrufsteuerung KW - Echzeitoptimierung KW - Online-Optimierung KW - elevator group control KW - destination call system KW - real time optimation KW - online optimization Y1 - 2007 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-10108 SN - 1438-0064 ER - TY - THES A1 - Tuchscherer, Andreas T1 - Dynamical Configuration of Transparent Optical Telecommunication Networks N2 - All-optical telecommunication networks allow for switching connections by lightpaths which can pass several network links without any opto-electronic conversion. Upon arrival of a connection request, it must be decided online, i.e., without knowledge of future requests, if it is accepted and in that case on which lightpaths the connection is routed. This online problem with the goal of maximizing the total profit gained by accepted requests is called Dynamic Singleclass Call Admission Problem (DSCA). We present existing and new algorithms for the DSCA as well as their theoretical and practical evaluation. KW - Online-Optimierung KW - Online Optimization Y1 - 2003 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-10214 ER - TY - GEN A1 - Krumke, Sven A1 - Rambau, Jörg T1 - Probieren geht über Studieren? Entscheidungshilfen für kombinatorische Online-Optimierungsprobleme in der innerbetrieblichen Logistik N2 - Die Automatisierung von innerbetrieblicher Logistik erfordert -- über die physikalische Steuerung von Geräten hinaus -- auch eine effiziente Organisation der Transporte: ein Aufgabenfeld der kombinatorischen Optimierung. Dieser Artikel illustriert anhand von konkreten Aufgabenstellungen die Online-Problematik (unvollständiges Wissen) sowie die Echtzeit-Problematik (beschränkte Rechenzeit), auf die man in der innerbetrieblichen Logistik trifft. Der Text gibt einen Überblick über allgemeine Konstruktionsprinzipien für Online-Algorithmen und Bewertungsmethoden, die bei der Entscheidung helfen, welche Algorithmen für eine vorliegende Problemstellung geeignet sind. T3 - ZIB-Report - 02-05 KW - Logistik KW - Hochregallagerbediengeräte KW - Kommissioniermobile KW - Aufzüge KW - Online-Optimierung KW - Echtzeit-Optimierung KW - Simulation KW - kompetitive Analyse Y1 - 2002 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-6723 ER - TY - GEN A1 - Grötschel, Martin A1 - Krumke, Sven A1 - Rambau, Jörg T1 - Wo bleibt der Aufzug? N2 - Dieser Artikel gibt eine allgemeinverständliche Einführung in die spezielle Problematik kombinatorischer Online-Problem am Beispiel der Fahrstuhlsteuerung. T3 - ZIB-Report - SC-99-29 KW - Aufzugsteuerung KW - Online-Optimierung KW - Echtzeit-Optimierung KW - kompetitive Analyse Y1 - 1999 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-4175 ER -