Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144433
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPandi, Ramesh Ramasamyen_US
dc.contributor.authorHo, Song Guangen_US
dc.contributor.authorNagavarapu, Sarat Chandraen_US
dc.contributor.authorDauwels, Justinen_US
dc.date.accessioned2020-11-05T04:50:39Z-
dc.date.available2020-11-05T04:50:39Z-
dc.date.issued2018-
dc.identifier.citationPandi, R. R., Ho, S. G., Nagavarapu, S. C., & Dauwels, J. (2018). Deterministic annealing for depot optimization : applications to the dial-a-ride problem. Proceedings of 2018 IEEE Symposium Series on Computational Intelligence (SSCI), 88-95. doi:10.1109/SSCI.2018.8628642en_US
dc.identifier.isbn978-1-5386-9276-9-
dc.identifier.urihttps://hdl.handle.net/10356/144433-
dc.description.abstractThis paper introduces a novel meta-heuristic approach to optimize depot locations for multi-vehicle shared mobility systems. Dial-a-ride problem (DARP) is considered as a case study here, in which routing and scheduling for door to- door passenger transportation are performed while satisfying several constraints related to user convenience. Existing literature has not addressed the fundamental problem of depot location optimization for DARP, which can reduce cost, and in turn promote the use of shared mobility services to minimize carbon footprint. Thus, there is a great need for fleet management systems to employ a multi-depot vehicle dispatch mechanism with intrinsic depot location optimization. In this work, we propose a deterministic annealing meta-heuristic to optimize depot locations for the dial-a-ride problem. Numerical experiments are conducted on several DARP benchmark instances from the literature, which can be categorized as small, medium and large based on their problem size. For all tested instances, the proposed algorithm attains solutions with travel cost better than that of the best-known solutions. It is also observed that the travel cost is reduced up to 6.13% when compared to the best-known solutions.en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationCRP-2en_US
dc.rights© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/SSCI.2018.8628642en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleDeterministic annealing for depot optimization : applications to the dial-a-ride problemen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.conference2018 IEEE Symposium Series on Computational Intelligence (SSCI)en_US
dc.contributor.organizationSingapore Technologies Engineering Ltden_US
dc.identifier.doi10.1109/SSCI.2018.8628642-
dc.description.versionAccepted versionen_US
dc.identifier.spage88en_US
dc.identifier.epage95en_US
dc.subject.keywordsShared Mobility Systemen_US
dc.subject.keywordsDepot Location Optimizationen_US
dc.citation.conferencelocationBengaluru, Indiaen_US
dc.description.acknowledgementThe research was partially supported by the ST EngineeringNTU Robotics Corporate Laboratory through the National Research Foundation (NRF) corporate lab@university scheme.en_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:EEE Conference Papers
Files in This Item:
File Description SizeFormat 
Conf paper IEEE SSCI 2018 (Author copy).pdfAuthor Accepted Version412.64 kBAdobe PDFThumbnail
View/Open

Page view(s)

213
Updated on Mar 2, 2024

Download(s) 50

104
Updated on Mar 2, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.