Multi-Objective Disassembly Depth Optimization for End-of-Life Smartphones Considering the Overall Safety of the Disassembly Process

Abstract

The disassembly of end-of-life (EoL) products is of high concern in sustainability research. It is important to obtain reasonable disassembly depth during the disassembly process. However, the overall safety of the disassembly process is not considered during the disassembly depth optimization process, which leads to an inability to accurately obtain a reasonable disassembly depth. Considering this, a multi-objective disassembly depth optimization method for EoL smartphones considering the overall safety of the disassembly process is proposed to accurately determine a reasonable disassembly depth in this study. The feasible disassembly depth for EoL smartphones is first determined. The reasonable disassembly process for EoL smartphones is then established. A multi-objective function for disassembly depth optimization for EoL smartphones is established based on the disassembly profit per unit time, the disassembly energy consumption per unit time and the overall safety rate of the disassembly process. In order to increase solution accuracy and avoid local optimization, an improved teaching–learning-based optimization algorithm (ITLBO) is proposed. The overall safety of the disassembly process, disassembly time, disassembly energy consumption and disassembly profit are used as the criteria for the fuzzy analytic hierarchy process (AHP) to evaluate the disassembly depth solution. A case of the ‘Xiaomi 4’ smartphone is used to verify the applicability of the proposed method. The results show that the searchability of the non-inferior solution and the optimal solution of the proposed method are improved. The convergence speeds of the ITLBO algorithm are 50.00%, 33.33% and 30.43% higher than those of the TLBO algorithm, and the optimal solution values of the ITLBO algorithm are 3.91%, 5.10% and 3.45% higher than those of the TLBO algorithm in three experiments of single objective optimization.