An Improved Q-Learning Algorithm for Solving Disassembly Line Balancing Problem Considering Carbon Emission

The remanufacturing, recycling, and reusing of waste products are particularly important to solve the problem of the resource shortage. Disassembly is a key step in the recycling process. How to minimize the negative impact of greenhouse gases on the environment has attracted extensive attention. This paper studies the disassembly line balancing problem to minimize the carbon emissions generated in the disassembly process. A Q-learning algorithm in reinforcement learning is applied to solve the disassembly line balancing problem. Through the analysis and comparison with the state-action-reward-state’-action algorithm to address the same real-life cases, it is proved that the Q-learning algorithm has good performance in most cases. In terms of solution speed, the proposed method is faster in both small-scale and large-scale cases.