Disassembly planning and sequencing play an important role in recycling a fast-growing number of end-of-life products. Optimal sequences can effectively reduce carbon emissions and save natural resources in the remanufacturing industry. Considering the development of intelligent manufacturing technology, this work deals with the optimization problem of selective disassembly sequences with an objective of maximizing disassembly profit. Disassembly sequences are generated based on AND/OR graphs. After setting up an environment matrix based on such graphs, this proposes a Q-learning technique to find an selective optimal disassembly sequence. The algorithm is applied to real-life disassembly cases. Experimental results show that the algorithm is superior a popularly-used genetic algorithm (GA) in both computing speed and solution quality through their various comparisons.

With more and more waste products are discarded, how to recycle them has become an urgent issue. Disassembling these discarded products is a critical step to take. With disassembly, we can maximize resource utilization and greatly save manufacturing costs. There are many influencing factors in a disassembly process. In this paper we consider the impact of disassembly tools deterioration rate on disassembly time and establish a mathematical model to minimize the disassembly time. We use the advantage actor-critic algorithm in reinforcement learning to solve this model. The correctness and superiority of the algorithm are verified by comparing with the actor-critic algorithm.

With the development of science and technology, a large number of electronic products have been discarded and become waste products. To obtain economic benefits and protect the environment, disassembly lines are designed to disassemble valuable parts from waste products. This paper proposes a mathematical model for the human-robot collaborative multiproduct hybrid disassembly line balancing problem with the disassembly revenue being the objective. A hybrid line combines a single-row line and a U-shaped line. We use the multi-neighborhood parallel greedy search algorithm to solve the model. Based on the algorithm, an alternate neighborhood search scheme consisting of different actions is designed. Some real-world cases are used to examine the feasibility of the proposed algorithm. The experimental results show that the multi-neighborhood parallel greedy search algorithm can solve the multi-product hybrid disassembly line balancing problem effectively.

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.

Nowadays, the recycling of waste products has attracted extensive attention in academia and industry. In the layout design of disassembly lines, single-row and U-shaped hybrid disassembly lines have different application scenarios. Considering workstation resource configuration, disassembly line cycle time, and disassembly task precedence relationship, we address a Multi-product Hybrid-disassembly-line-balancing Problem (MHP), and establish its mathematical model with the objective of disassembly profit maximization. In addition, the union variable neighborhood descent (U-VND) algorithm is used to solve the problem, in which two kinds of neighborhood structures composed of different actions is designed. Experimental results and comparative analysis show that the proposed algorithm can quickly obtain stable and high-quality solutions, which verifies the validity of the neighborhood structure and the correctness of the model.