Abstract: Considering the influence of the number of maintenance personnel on the maintenance cost and downtime of complex systems,a multi-objective maintenance decision optimization method with two-layer nested particle swarm structure for complex systems was proposed for the periodic maintenance strategy of multiple maintenance personnel.In this method,the bottom Particle Swarm Optimization (PSO) was used to solve the problem of parallel scheduling in sorting maintenance task,therefore the minimum and maximum maintenance time of repairing nodes was worked out.To work out the Pareto front solution set of the three-objective model,the top-level multi-objective PSO was used to search and optimize the system reliability,availability and maintenance cost models.The equilibrium relationship between the front particles and the three targets was analyzed,and the optimal maintenance plan was recommended.The feasibility of the proposed method was verified by the examples,which could improve the reliability and availability of the system effectively,and reduce the maintenance cost.At the same time,it could also provide decision support for the decision maker to determine the maintaining level of components and the assignment of maintenance personnel tasks.

Key words: nested particle swarm optimization, maintenance decision-making, parallel scheduling, maintenance personnel, multi-objective optimization

摘要： 针对复杂系统的多维修人员定期检修策略,考虑维修人员数量对系统维修成本及停机时间的影响,提出一种两层嵌套粒子群结构的复杂系统多目标维修决策优化方法。该方法利用底层粒子群算法求解维修任务排序的并行调度问题,得出检修节点的最小化最大维修时间,再通过顶层多目标粒子群算法,对系统可靠度、可用度及维修成本模型进行搜索优化,得到该三目标模型的Pareto前沿解集,并分析前沿粒子在三个目标的平衡关系,推荐出最优的维修方案,通过算例验证了该方法的可行性。所提方法在有效地提高系统可靠度和可用度、降低维修成本的同时,还可以为决策者对组件维修等级的确定、维修人员任务的分配等问题提供决策支持。

关键词: 嵌套粒子群, 维修决策, 并行调度, 维修人员, 多目标优化