Please use this identifier to cite or link to this item: http://elar.urfu.ru/handle/10995/131099
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dc.contributor.authorChen, Y.en
dc.date.accessioned2024-04-05T16:38:49Z-
dc.date.available2024-04-05T16:38:49Z-
dc.date.issued2023-
dc.identifier.citationChen, Y 2023, Approximation Schemes for Capacity Vehicle Routing Problems: A Survey. в Proceedings - 2023 2nd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2023: book. Institute of Electrical and Electronics Engineers Inc., стр. 277-282, 2023 2nd International Conference on Computational Modelling, Simulation and Optimization (ICCMSO), 23/06/2023. https://doi.org/10.1109/ICCMSO59960.2023.00059harvard_pure
dc.identifier.citationChen, Y. (2023). Approximation Schemes for Capacity Vehicle Routing Problems: A Survey. в Proceedings - 2023 2nd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2023: book (стр. 277-282). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCMSO59960.2023.00059apa_pure
dc.identifier.isbn9798350326666-
dc.identifier.otherFinal2
dc.identifier.otherAll Open Access, Green3
dc.identifier.otherhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85181172283&doi=10.1109%2fICCMSO59960.2023.00059&partnerID=40&md5=f24e85db0e8ce863d2acdf7e0f49fd2f1
dc.identifier.otherhttps://arxiv.org/pdf/2306.01826pdf
dc.identifier.urihttp://elar.urfu.ru/handle/10995/131099-
dc.description.abstractThe Capacity Vehicle Routing Problem (CVRP) pertains to the combinatorial optimization problem of identifying the optimal route for vehicles with a capacity constraint of k to travel from the depots to customers, which minimizes the total distance traveled. The Capacitated Vehicle Routing Problem (CVRP), which is essential to modelling logistics networks, has drawn a lot of interest in the field of combinatorial optimization. It has been established that the CVRP (Capacitated Vehicle Routing Problem) with a value of k greater than or equal to three exhibits computational complexity that is classified as NP-hard. Furthermore, it has been established that the problem is APXhard. It has been previously established that the solution is not approximatable in a metric space. Furthermore, this constitutes the principal challenge among the array of issues that confront Arora's approximation algorithm. The outstanding matter concerns the presence of a (1+$\epsilon$) PTAS (polynomial time approximation scheme) for the capacity vehicle routing problem in Euclidean space, regardless of the vehicle's capacity. The objective of this manuscript is to furnish a thorough and all-encompassing survey of the research progressions in the domain, encompassing the evolution of the field from its inception to the most recent cutting-edge discoveries. © 2023 IEEE.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.source2023 2nd International Conference on Computational Modelling, Simulation and Optimization (ICCMSO)2
dc.sourceProceedings - 2023 2nd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2023en
dc.subjectAPPROXIMATION ALGORITHMSen
dc.subjectCAPACITY VEHICLE ROUTING PROBLEMSen
dc.subjectCOMBINATORIAL OPTIMIZATIONen
dc.subjectPOLYNOMIAL-TIME APPROXIMATION SCHEMEen
dc.subjectAPPROXIMATION ALGORITHMSen
dc.subjectPOLYNOMIAL APPROXIMATIONen
dc.subjectROUTING ALGORITHMSen
dc.subjectVEHICLE ROUTINGen
dc.subjectVEHICLESen
dc.subjectAPPROXIMATION SCHEMEen
dc.subjectCAPACITATED VEHICLE ROUTING PROBLEMen
dc.subjectCAPACITY CONSTRAINTSen
dc.subjectCAPACITY VEHICLE ROUTING PROBLEMSen
dc.subjectCLASSIFIEDSen
dc.subjectCOMBINATORIAL OPTIMIZATION PROBLEMSen
dc.subjectLOGISTICS NETWORKen
dc.subjectOPTIMAL ROUTESen
dc.subjectPOLYNOMIAL TIME APPROXIMATION SCHEMESen
dc.subjectTOTAL DISTANCESen
dc.subjectCOMBINATORIAL OPTIMIZATIONen
dc.titleApproximation Schemes for Capacity Vehicle Routing Problems: A Surveyen
dc.typeConference Paperen
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dc.typeinfo:eu-repo/semantics/submittedVersionen
dc.conference.name2nd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2023en
dc.conference.date23 June 2023 through 25 June 2023-
dc.identifier.doi10.1109/ICCMSO59960.2023.00059-
dc.identifier.scopus85181172283-
local.contributor.employeeChen, Y., Ural Federal University, Department of Mathematics and Computer Science, Yekaterinburg, Russian Federationen
local.description.firstpage277-
local.description.lastpage282-
local.contributor.departmentUral Federal University, Department of Mathematics and Computer Science, Yekaterinburg, Russian Federationen
local.identifier.pure50622584-
local.identifier.eid2-s2.0-85181172283-
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