An Improved Method for Disassembly Depth Optimization of End-of-Life Smartphones Based on PSO-BP Neural Network Predictive Model

Shengqiang Jiao, Lin Li (), Fengfu Yin and Yang Yu
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Shengqiang Jiao: College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
Lin Li: College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
Fengfu Yin: College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
Yang Yu: College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China

Sustainability, 2025, vol. 17, issue 20, 1-23

Abstract: Disassembly is a crucial step in the remanufacturing of end-of-life (EoL) electronic products. Disassembly depth refers to the disassembly stop point determined by the disassembly sequence. For the disassembly depth optimization of EoL electronic products, a feasibility model with a fast convergence and low mean squared error (MSE) is needed to improve optimization accuracy. However, the use of a backpropagation neural network (BPNN) model or mathematical model often results in a slow convergence and high MSE due to the randomness of the initial weights and biases. In this study, an improved method for the disassembly depth optimization of smartphones based on a Particle Swarm Optimization-BPNN (PSO-BPNN) predictive model is proposed. Compared with the traditional BPNN optimization method, the proposed method in this study is that the BPNN predictive model is optimized by using PSO, which shows a superior predictive performance and reduces the MSE. The case of ‘Huawei P7’ is used to verify the feasibility of the method. The results show that the method maintains disassembly profit while reducing the disassembly time and carbon emissions by 17.1% and 7.8%, respectively. Compared with the BPNN model, the PSO-BPNN model converges 18.6%, 32.8%, and 16.6% faster in predicting the disassembly time, profit, and carbon emissions, respectively, with MSE reductions of 92.95%, 96.51%, and 92.74%, respectively.

Keywords: disassembly depth optimization; EoL smartphones; PSO-BPNN; MSE; pareto solutions (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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