Disassembly sequence planning based on a modified grey wolf optimizer

Disassembly sequence planning (DSP) can effectively increase the disassembly efficiency, shorten the disassembly cycle, reduce disassembly costs, and reduce environmental hazards of end-of-life (EOL) products, playing an important role in manufacturing industries. Thus, it is urgent to propose an approach to solve the DSP problem. DSP is a famous NP-hard combinatorial optimization problem. As the size of components increases, exact algorithms can hardly obtain the optimal disassembly sequence. Therefore, we propose a promising intelligence algorithm, modified grey wolf optimizer (MGWO), to solve the DSP problem. MGWO inherits the main idea of the hierarchy and hunting mechanism of the original grey wolf optimizer (GWO). Three new operators are designed in MGWO to ensure the feasibility of solutions under the complex constraint of disassembly precedence. The feasible solution generator (FSG) is designed to obtain feasible disassembly sequences, the neighborhood search operator (NSO) is developed to make wolves (solutions) self-evolving, and the guided search operator (GSO) is used to make the wolf group guided by three leaders of wolves. Two engineering cases are applied to validate the effectiveness of the proposed operators. Then, they and two real-world applications are used to compare the MGWO with other reported methods. The results demonstrate that MGWO can solve the DSP problem effectively.