@misc{AscheuerKrumkeRambau, 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}, 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} } @misc{HauptmeierKrumkeRambau, 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}, 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., 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}, 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} }