Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10373
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dc.contributor.authorAktaş, Y.F.-
dc.contributor.authorÖzbayoğlu, A.M.-
dc.date.accessioned2023-04-16T10:01:20Z-
dc.date.available2023-04-16T10:01:20Z-
dc.date.issued2022-
dc.identifier.isbn9781665488945-
dc.identifier.urihttps://doi.org/10.1109/ASYU56188.2022.9925480-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/10373-
dc.description2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022 -- 7 September 2022 through 9 September 2022 -- 183936en_US
dc.description.abstractWith the spread of online platforms, problems such as manned/unmanned food delivery, cargo delivery, raw material delivery, are increasing the importance of logistics day by day. Vehicle routing problem, which is one of the most important problems in the field of logistics, is a combinatorial problem and as the problem space grows, it takes a long time to find a solution with human effort and in most cases it is not even possible. Thus, it becomes essential for the solution of this problem to be autonomous. Although it is possible to solve the problem with classical heuristic optimization methods, it takes a long time and sometimes does not give a good enough solution. Deep reinforcement learning models with attention mechanisms have great potential in this regard. However,in case of insufficient training in large problem space, it is possible to get away from the optimal solution. In this study, better results are taken in an acceptable time by using the deep reinforcement learning models with attention-model and heuristic methods in a hybrid way. © 2022 IEEE.en_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectattentionen_US
dc.subjectCVRPen_US
dc.subjectDeep reinforcement learningen_US
dc.subjectheuristicsen_US
dc.subjectoptimizationen_US
dc.subjectDeep learningen_US
dc.subjectHeuristic methodsen_US
dc.subjectLearning systemsen_US
dc.subjectReinforcement learningen_US
dc.subjectVehicle routingen_US
dc.subjectAttentionen_US
dc.subjectCVRPen_US
dc.subjectDeep reinforcement learningen_US
dc.subjectHeuristicen_US
dc.subjectHybrid methoden_US
dc.subjectOptimisationsen_US
dc.subjectProblem spaceen_US
dc.subjectReinforcement learning approachen_US
dc.subjectReinforcement learning modelsen_US
dc.subjectReinforcement learningsen_US
dc.subjectOptimizationen_US
dc.titleHybrid Method by Integrating Deep Reinforcement Learning and Heuristics Approach for Capacitated Vehicle Routing Problemen_US
dc.title.alternativeDerin Pekiştirmeli Öğrenme ve Sezgisel Yöntemlerin Kapasite Kisitli Araç Rotalama Probleminde Entegre Kullanimien_US
dc.typeConference Objecten_US
dc.departmentTOBB ETÜen_US
dc.identifier.scopus2-s2.0-85142682349en_US
dc.institutionauthor-
dc.identifier.doi10.1109/ASYU56188.2022.9925480-
dc.authorscopusid57982502400-
dc.authorscopusid6505999525-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairetypeConference Object-
item.languageiso639-1tr-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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