Open AccessMulti-objective disassembly sequence optimization aiming at quality uncertainty of end-of-life product
Author(s) -
Shengqiang Li,
Hua Zhang,
Wei Yan,
Zhigang Jiang,
Han Wang,
Weijie Wei
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/631/3/032015
Subject(s) - remanufacturing , particle swarm optimization , sequence (biology) , reliability engineering , product (mathematics) , quality (philosophy) , weibull distribution , computer science , artificial neural network , component (thermodynamics) , engineering , mathematical optimization , manufacturing engineering , artificial intelligence , algorithm , mathematics , philosophy , statistics , physics , geometry , thermodynamics , epistemology , biology , genetics
Remanufacturing plays a vital role in circular economy due to its enormous contribution in promoting resources recycling and utilizing. Disassembly of end of life (EOL) products, as a prerequisite of remanufacturing, is an effective means to improve resource utilization and reduce environmental impact. However, because of the complex quality conditions of EOL products, different disassembly method and sequence for components may lead to different effects. Based on this, a multi-objective disassembly sequence optimization model considering the quality uncertainty of EOL products is proposed in this paper. Firstly, remaining life of each component of an EOL product is calculated by using the Weibull distribution and artificial neural networks (ANN), and then the disassembly modes could be chosen according to their quality conditions. Secondly, a multi-objective disassembly sequence optimization model which takes minimum disassembly time and cost as the objective is established, and the particle swarm optimization (PSO) algorithm is employed to solve this model. Finally, a case study of drum washing machine disassembly is provided to verify the feasibility and superiority of the proposed methodology.