A disassembly sequence planning method with improved discrete grey wolf optimizer for equipment maintenance in hydropower station

The foundation of hydropower station equipment maintenance is parts disassembly, thus a reasonable disassembly sequence can optimize the maintenance efficiency. To this end, a disassembly sequence planning method based on improved discrete grey wolf optimizer (IDGWO) is proposed in this paper. Firstly, in the modeling, a directed graph with combination nodes is adopted to represent the priority constraint relationship of parts. In addition, a sequence evaluation index based on operator moving distance is added to the fitness function. Subsequently, in algorithm design, we improve the optimization mechanism of traditional grey wolf optimizer and propose a self-renewal (SR) mechanism and an exchange optimization operator (EOO) to enhance the optimization efficiency and stability. Finally, two experiments are conducted using five actual maintenance items. The first experiment is performed to verify the effectiveness of the proposed SR mechanism and EOO. The second experiment is adopted to verify the superiority of the proposed IDGWO compared with four well-known algorithms. The experimental results show that in five actual maintenance items, the proportion of the optimal sequence found by the IDGWO reach to 100%, 32%, 29%, 100% and 100%, respectively, which is higher than comparison algorithms. In addition, IDGWO has a prominent performance in stability and convergence speed than other comparison algorithms.