基于改进MOPSO的多级系统备件配置优化研究

Optimization for spare parts allocation in multi-echelon support system based on improved MOPSO

备件是装备保障的重要物质基础,合理规划备件的配置方案是提高装备保障效能的关键。针对多级保障系统备件配置优化的高维、非线性问题,构建了以备件保障度最大、保障费用最小为目标函数,以其他准则为约束条件的优化配置模型。面向优化模型求解的难题,在传统粒子群算法的基础上,提出了一种改进的粒子群求解算法,给出了该算法的设计思路和优化流程,采用基于准则的方法以及改进惯性权重等措施,以两个目标作为引导,在备件配置方案生成时可以避免长时间的无效搜索,提高了粒子群优化算法的求解效率,最后通过算例证明该方法的可行性和有效性。

Spare parts is a key component of equipment support,and a reasonable allocation plan of spare parts is the key factor for improving the efficacy of equipment support.Spare parts allocation and optimization in a multi-echelon support system presents difficult problems,which involves non-linear objective function and in-teger variables to be optimized.A multi-objective optimization model is developed,which maximizes support probability and minimizes support costs.In order to obtain the solution of the model,an improved multi-objec-tive particle swarm optimization （MOPSO）method is employed,based on the traditional particle swarm meth-od.The design idea and optimization procedure of this algorithm are put forward,rule based and inertia weigh improving method are introduced.In this method,dimensions reduction and rules-based multi-objective optimi-zation are employed,which can improve the solving efficiency for the MOPSO method.At last,a numerical ex-ample is given,which examines the feasibility and validity of this method.