Predicting the quantity of recycled end-of-life products using a hybrid SVR-based model

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dc.contributor.authorXia, Hanbing
dc.contributor.authorHan, Ji
dc.contributor.authorMilisavljevic-Syed, Jelena
dc.date.accessioned2024-01-04T12:07:48Z
dc.date.available2024-01-04T12:07:48Z
dc.date.issued2023-11-21
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 diffusion (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-of-life product recycling. The study shows that developing the predictive model using artificial virtual samples supported by the KNNMTD method is feasible, the PSO algorithm effectively 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 deployment.en_UK
dc.identifier.citationXia H, Han J, Milisavljevic-Syed J. (2023) Predicting the quantity of recycled end-of-life products using a hybrid SVR-based model. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 20 August 2023, Boston, Massachusetts, USA. Volume 5: number 87223en_UK
dc.identifier.isbn978-0-7918-8733-2
dc.identifier.urihttps://doi.org/10.1115/DETC2023-114718
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20599
dc.language.isoenen_UK
dc.publisherAmerican Society of Mechanical Engineersen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectreverse supply chainen_UK
dc.subjectend-of-life productsen_UK
dc.subjectmachine learningen_UK
dc.subjectpredictive analysisen_UK
dc.subjectcircular supply chainen_UK
dc.subjectsustainabilityen_UK
dc.titlePredicting the quantity of recycled end-of-life products using a hybrid SVR-based modelen_UK
dc.typeConference paperen_UK
dcterms.dateAccepted2023-11-21

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