Multi-objective collaborative optimization of green disassembly planning and recovery option decision considering the learning effect

Abstract

With the continuous improvement of environmental awareness, the recovery of end-of-life products has received widespread attention. Rational decision-making on the recovery options of product parts is an effective way to achieve environmental goals. Meanwhile, manual disassembly is very important in the recycling process, and the learning effect of workers has a great influence on disassembly. Therefore, a collaborative selective disassembly planning and end-of-life products recovery option decision model considering the learning effect is proposed. The objective is to minimize disassembly time, and carbon emissions and maximize disassembly profit. To obtain a high-quality disassembly scheme, an improved multi-objective genetic algorithm based on Q-learning is proposed. To improve the quality of the initial solution, a three-layer encoding strategy including disassembly sequence, disassembly decision sequence, and recovery option decision sequence is designed. Four search strategies are designed as actions for Q-learning, and the state is constructed based on population fitness. This way can enable the algorithm to dynamically adjust the optimization search strategy during the iterative process. Then, the accuracy and effectiveness of the algorithm are verified by two test cases. Next, the proposed model and algorithm are applied to a real refrigerator disassembly case. The results show that due to the learning effect, the efficiency of the disassembly can be increased by 31.66 %, the cost can be reduced by 30.44 %, and the carbon emissions can be reduced by 30.07 %. In addition, carbon emissions can be reduced by 34.82 % by co-optimizing disassembly planning and recovery option decisions.