基于改进MOPSO的多级系统备件配置优化研究

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。

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

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 integer 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-objective particle swarm optimization (MOPSO) method is employed, based on the traditional particle swarm method. 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 optimization are employed, which can improve the solving efficiency for the MOPSO method. At last, a numerical example is given, which examines the feasibility and validity of this method.