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  • 2000-2004  (5)
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  • 1
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    Publication Date: 2020-08-05
    Language: English
    Type: masterthesis , doc-type:masterThesis
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  • 2
    Publication Date: 2020-03-09
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 3
    Publication Date: 2020-08-05
    Language: English
    Type: other , doc-type:Other
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  • 4
    Publication Date: 2020-08-05
    Description: This thesis deals with a Dial-a-Ride problem on trees and considers both offline and online versions of this problem. We study the behavior of certain algorithms on random instances, i.e. we do probabilistic analysis. The focus is on results describing the typical behavior of the algorithms, i.e. results holding with (asymptotically) high probability. For the offline version, we present a simplified proof of a result of Coja-Oghlan, Krumke und Nierhoff. The results states that some heuristic using a minimum spanning tree to approximate a Steiner tree gives optimal results with high probability. This explains why this heuristic produces optimal solutions quite often. In the second part, probabilistic online versions of the problem are introduced. We study the online strategies REPLAN and IGNORE. Regarding the IGNORE strategy we can show that it works almost optimal under high load with high probability.
    Keywords: ddc:510
    Language: English
    Type: masterthesis , doc-type:masterThesis
    Format: application/pdf
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  • 5
    Publication Date: 2020-11-13
    Description: Under high load, the automated dispatching of service vehicles for the German Automobile Association (ADAC) must reoptimize a dispatch for 100--150 vehicles and 400 requests in about ten seconds to near optimality. In the presence of service contractors, this can be achieved by the column generation algorithm ZIBDIP. In metropolitan areas, however, service contractors cannot be dispatched automatically because they may decline. The problem: a model without contractors yields larger optimality gaps within ten seconds. One way-out are simplified reoptimization models. These compute a short-term dispatch containing only some of the requests: unknown future requests will influence future service anyway. The simpler the models the better the gaps, but also the larger the model error. What is more significant: reoptimization gap or reoptimization model error? We answer this question in simulations on real-world ADAC data: only the new model ZIBDIP{\footnotesize dummy} can keep up with ZIBDIP.
    Keywords: ddc:000
    Language: English
    Type: reportzib , doc-type:preprint
    Format: application/pdf
    Format: application/postscript
    Format: application/postscript
    Format: application/pdf
    Format: application/pdf
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