改进PSO算法的多级可修备件库存优化
Multi Level Inventory Optimization of Repairable Spare Parts Based on Improved Particle Swarm Optimization
翁卫兵 1李晋暘 1吴坚 1李苏建2
作者信息
- 1. 浙江科技学院机械与能源工程学院,浙江 杭州 310023
- 2. 北京科技大学机械工程学院,北京 100083
- 折叠
摘要
针对可修备件多级库存的METRIC模型边际优化算法效果不高问题,提出了一种改进粒子群算法来求解模型.在分析可修备件供应保障过程的基础上,建立了METRIC模型,以保障费用最低为优化目标,以系统可用度为约束条件,用改进的粒子群算法寻找备件在各级仓库间的最优分配方案.改进的粒子群算法对惯性权重和学习因子进行了动态调整,并加入罚函数提高了算法收敛速度和寻优结果,最后通过算例分析对比了边际优化算法.结果表明,改进的粒子群算法对比边际优化算法,在同样的可用度约束下,保障费用降低了5.8%,在解决装备可修备件多级库存优化中具有一定的优越性.
Abstract
Aiming at the problem that marginal optimization algorithm of METRIC model for Multi-level Inventory of repair-able spare parts is not effective,an improved particle swarm optimization algorithm is proposed to solve the model.Based on the analysis of the supply and support process of repairable spare parts,a METRIC model is established.With the minimum support cost as the optimization objective and the system availability as the constraint condition,an improved particle swarm optimization algorithm is used to find the optimal allocation scheme of spare parts among all levels of warehouses.The improved particle swarm optimization algorithm dynamically adjusts the inertia weight and learning factor,and adds a penalty function to im-prove the convergence speed and optimization results.Finally,the marginal optimization algorithm is compared with an example.The results show that the support cost of the improved PSO algorithm is reduced by 5.8%compared with the marginal optimiza-tion algorithm under the same availability constraints,which has certain advantages in solving the Multi-level inventory optimi-zation of equipment repairable spare parts.
关键词
可修备件/多级库存/METRIC模型/库存优化Key words
Repairable Spare Parts/Multi Level Inventory/METRIC Model/Inventory Optimization引用本文复制引用
基金项目
国家重点研发计划(2017YFC0806303)
出版年
2024
NETL
NSTL
维普
万方数据