Show simple item record

dc.contributor.authorXia, H
dc.contributor.authorHan, J
dc.contributor.authorMilisavljevic-Syed, J
dc.date.accessioned2023-06-21T14:31:35Z
dc.date.issued2023-11-21
dc.date.updated2023-06-20T13:23:33Z
dc.description.abstractEnd-of-life product recycling is crucial for achieving sustainability in circular supply chains and improving resource utilization. Forecasting the quantity of recycled end-of-life products is essential for planning and managing reverse supply chain operations. Decision-makers and practitioners can benefit from this information when designing reverse logistics networks, managing tactical disposal, planning capacity, and operational production. To address the challenge of small sample data with multiple factors influencing the recycling number, and to deal with the randomness and nonlinearity of the recycling quantity, a hybrid predictive model has been developed in this research. The model is based on k-nearest neighbor mega-trend dif usion (KNNMTD), particle swarm optimization (PSO), and support vector regression (SVR) using the data from the field of end-of-life vehicles as a case study. Unlike existing literature, this research incorporates the data augmentation method to build an SVR-based model for end-oflife product recycling. The study shows that developing the predictive model using artificial virtual samples supported by the KNNMTD method is feasible, the PSO algorithm ef ectively brings strong approximation ability to the SVR-based model, and the KNNMTD-PSO-SVR model perform well in predicting the recycled end-of-life products quantity. These research findings could be considered a fundamental component of the smart system for circular supply chains, which will enable the smart platform to achieve supply chain sustainability through resource allocation and regional industry deploymenten_GB
dc.description.sponsorshipCranfield Universityen_GB
dc.identifier.citationASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 20-23 August 2023, Boston, Massachusetts, US, paper number DETC2023-114718en_GB
dc.identifier.doi10.1115/DETC2023-114718
dc.identifier.urihttp://hdl.handle.net/10871/133461
dc.identifierORCID: 0000-0003-3240-4942 (Han, Ji)
dc.language.isoenen_GB
dc.publisherAmerican Society of Mechanical Engineers (ASME)en_GB
dc.rights© 2023 by ASME. This version is made available under the CC-BY 4.0 license: https://creativecommons.org/licenses/by/4.0/  en_GB
dc.subjectReverse Supply Chainen_GB
dc.subjectEnd-of-life Productsen_GB
dc.subjectMachine Learningen_GB
dc.subjectPredictive Analysisen_GB
dc.subjectCircular Supply Chainen_GB
dc.subjectSustainabilityen_GB
dc.titlePredicting the quantity of recycled end-of-life products using a hybrid SVR-based modelen_GB
dc.typeConference paperen_GB
dc.date.available2023-06-21T14:31:35Z
exeter.locationBoston, Massachusetts
dc.descriptionThis is the author accepted manuscript. The final version is available from ASME via the DOI in this recorden_GB
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/  en_GB
dcterms.dateAccepted2023-05-12
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2023-05-12
rioxxterms.typeConference Paper/Proceeding/Abstracten_GB
refterms.dateFCD2023-06-20T13:23:35Z
refterms.versionFCDAM
refterms.dateFOA2024-01-19T15:44:32Z
refterms.panelCen_GB
pubs.name-of-conferenceInternational Design Engineering Technical Conferences and Computers and Information in Engineering Conference


Files in this item

This item appears in the following Collection(s)

Show simple item record

© 2023 by ASME. This version is made available under the CC-BY 4.0 license: https://creativecommons.org/licenses/by/4.0/  
Except where otherwise noted, this item's licence is described as © 2023 by ASME. This version is made available under the CC-BY 4.0 license: https://creativecommons.org/licenses/by/4.0/