Energy-Efficient Robotic Parallel Disassembly Sequence Planning for End-of-Life Products

End-of-life (EOL) products contain many valuable parts and materials. The timely disassembly and recycling of EOL products can bring considerable economic benefits and reduce environmental pollution. To reduce the cost of manual disassembly, robotic disassembly has become one of the main methods used to dismantle EOL products. In addition, parallel disassembly can shorten the makespan of EOL products. Therefore, this article focuses on a new problem involving robotic parallel disassembly sequence planning for EOL products and establishes a corresponding multiobjective model to minimize the makespan and energy consumption. To obtain high-quality disassembly schemes, a discrete artificial bee colony algorithm based on problem characteristics is proposed. The performance of the proposed algorithm is verified by solving 32 benchmark problems and a comparison with three well-known multiobjective algorithms. The proposed model and method are applied to a real-world LCD TV disassembly case, and multiple preferred disassembly schemes are obtained. The results show that the proposed model and method can effectively shorten the makespan and reduce energy consumption.
Note to Practitioners
—Parallel disassembly is one of the most efficient disassembly methods, and robotic disassembly will be one of the main disassembly methods in the future. Therefore, this article focuses on the robotic parallel disassembly planning to achieve an efficient and environmentally friendly treatment of end-of-life products. With the importance of production efficiency and energy saving, it is essential to simultaneously assess the makespan and energy consumption. The results demonstrate that the proposed artificial bee colony algorithm can provide multiple disassembly schemes that balance the makespan and energy consumption. The obtained disassembly schemes can provide references for the disassembly planning of the disassembly enterprise and expand the decision-making space for decision-makers.