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AuthorLutz, Romandc.contributor.author
Date of accession2016-03-15T10:40:04Zdc.date.accessioned
Available in OPARU since2016-03-15T10:40:04Zdc.date.available
Year of creation2014dc.date.created
AbstractThere has already been a lot of research on Local Search Heuristics in Computer Science. Local Search improves an initial solution by manipulating rather small parts of the solution. Large Neighborhood Search (LNS) has a similar approach, but instead of marginal changes, huge parts of the solutions are changed. This is done by the combination of so-called destroy and repair methods. LNS is especially useful for problems with a tightly constrained search space, such as the Rich Pickup and Delivery Problem with Time Windows (RPDPTW). The RPDPTW is a logistic problem, in which a number of pickup and delivery requests have to be served by a fleet of vehicles. Furthermore, certain time, capacity and feasibility constraints have to be met. Adaptive Large Neighborhood Search (ALNS) is an extension of Large Neighborhood Search, that does not commit to one destroy and repair heuristic. Instead, it chooses in every iteration from a pool of heuristics based on past success. Even though it is a general heuristic, ALNS can compete with most specialized heuristics. Therefore, the goal of this thesis is a detailed description of the ALNS heuristic. A closer look at an application of ALNS is provided by the adaption to the Rich Pickup and Delivery Problem with Time Windows. The ALNS heuristic has also been implemented, extended, tuned and tested for different problem instances. The test results confirm the promising results obtained by other papers.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseStandarddc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_v3dc.rights.uri
KeywordDestroydc.subject
KeywordLarge Neighborhood Searchdc.subject
KeywordLocal Searchdc.subject
Dewey Decimal GroupDDC 004 / Data processing & computer sciencedc.subject.ddc
LCSHRepairingdc.subject.lcsh
TitleAdaptive Large Neighborhood Searchdc.title
Resource typeAbschlussarbeit (Bachelor)dc.type
DOIhttp://dx.doi.org/10.18725/OPARU-3237dc.identifier.doi
PPN1657048209dc.identifier.ppn
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-vts-95684dc.identifier.urn
GNDHeuristikdc.subject.gnd
GNDLogistikdc.subject.gnd
GNDOptimierungdc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften und Informatikuulm.affiliationGeneral
Date of activation2015-05-28T11:24:00Zuulm.freischaltungVTS
Peer reviewneinuulm.peerReview
DCMI TypeTextuulm.typeDCMI
VTS ID9568uulm.vtsID
CategoryPublikationenuulm.category
Bibliographyuulmuulm.bibliographie


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