Session: DFMLC-02-01 Design for Supply Chain, end of Life Recovery, and Large Systems

Paper Number: 114718

114718 - Predicting the Quantity of Recycled End-of-Life Products Using a Hybrid Svr-Based Model 

End-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. 

Presenting Author: Hanbing Xia Cranfield University

Presenting Author Biography: Hanbing earned her MSc in 2019 and BSc in 2017 in Logistics Management from the Jilin University, China respectively. Hanbing Xia is a PhD candidate specializing in Optimization of Automotive Reverse Supply Chain based on AI Techniques.She moved to University of Liverpool as a PhD research student, where she undertook her research within the Systems Realization Laboratory. She has now moved to Cranfield University to continue her research at the School of Aerospace, Transport and Manufacturing.

Authors:

Hanbing Xia Cranfield University
Ji Han University of Exeter Business School
Jelena Milisavljevic-Syed Cranfield University