基于改进粒子群的民用飞机航材库存配置方法

doi:10.12305/j.issn.1001-506X.2025.11.15

摘要/Abstract

摘要：

为解决现有民用飞机航材库存配置方法在大规模配置中易陷入局部最优解问题，提出通过可维修备件多级保障库存技术理论建立民用飞机两级航材库存模型。在此基础上，基于改进粒子群优化（particle swarm optimization, PSO）算法进行航材配置优化。该算法基于航材库存模型简化优化目标，并改进粒子群初始投点策略和速度更新函数，克服陷入局部最优解的缺陷。以民用飞机航材配置方案为案例，改进PSO算法配置金额相较于传统的边际分析法减少16.3%，说明所提改进PSO算法可为民用飞机航材配置中的有效性。改进PSO算法可为民机航材配置提供理论参考。

关键词: 航材, 可维修备件多级保障库存技术, 改进粒子群优化算法, 配置方案

Abstract:

In order to solve the problem that the existing civil aircraft aviation spare parts inventory configuration method is prone to fall into the local optimal solution in large-scale configuration, it is proposed to establish a two-level civil aircraft aviation spare parts inventory model through the multi-echelon technique for recoverable item control theory. On this basis, the aviation spare parts configuration optimization is performed based on the improved particle swarm optimization （PSO） algorithm. The algorithm simplifies the optimization objective based on the aviation spare parts inventory model, and improves the particle swarm initial point strategy and speed update function to overcome the defect of falling into the local optimal solution. Taking civil aircraft aviation spare parts configuration plan as an example, the configuration amount of the improved PSO algorithm is reduced by 16.3% compared with the traditional marginal analysis method, which shows the effectiveness of the proposed improved PSO in civil aircraft aviation spare parts configuration. Improved PSO algorithm provides a theoretical reference for civil aircraft aviation spare parts configuration.

Key words: aviation spare parts, multi-echelon technique for recoverable item control, improved particle swarm optimization algorithm, configuration plan

中图分类号:

V 25

冯蕴雯, 许嘉炜, 路成, 薛小锋. 基于改进粒子群的民用飞机航材库存配置方法[J]. 系统工程与电子技术, 2025, 47(11): 3663-3671.

Yunwen FENG, Jiawei XU, Cheng LU, Xiaofeng XUE. Civil aircraft aviation spare parts inventory configuration method based on improved particle swarm[J]. Systems Engineering and Electronics, 2025, 47(11): 3663-3671.

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航材名称	航材数据				周转数据
航材名称	航材单价/元	MTBUR/h	单机安装数/个	航材预测值/个	运输时间/年	维修时间/年
空调系统控制组件	1123	4000	1	10	0.12	0.1
压调系统控制组件	40050	8700	5	2	0.12	0.32
应急通风活门	302806	11500	2	1	0.12	0.1
单向活门	18070	22900	2	4	0.12	0.15
座舱压力控制器	787500	16326	4	4	0.12	0.12
排气活门	124200	8947	2	1	0.12	0.12
座舱压力信号单元	108000	7826	2	3	0.12	0.1
安全活门	55440	3000	1	4	0.12	0.15
流量控制活门	110898	28455	21	4	0.12	0.1
制冷包（右）	3457836	4000	1	1	0.12	0.15
制冷包（左）	3466926	4000	1	2	0.12	0.12

参数	取值
粒子群维度	11×10
种群规模/个	100
最大迭代数	100
惩罚系数	8×10⁸
惯性权重参数	w₁=0.9，w₂=0.4
粒子群速度范围	（−2，2）

配置数量	A	B	C	a	b	c	d	e	f	g
空调系统控制组件	3	0	0	3	2	2	2	2	2	2
压调系统控制组件	2	0	0	1	1	0	0	0	0	0
应急通风活门	0	0	0	0	0	0	0	0	0	0
单向活门	2	0	0	1	1	1	1	0	1	1
座舱压力控制器	2	0	0	1	1	1	1	1	1	0
排气活门	0	0	0	0	0	0	0	0	0	0
座舱压力信号单元	2	0	0	1	1	1	1	1	0	0
安全活门	1	0	0	1	1	1	0	0	1	1
流量控制活门	2	0	0	1	1	1	0	1	1	1
制冷包（右）	0	0	0	0	0	0	0	0	0	0
制冷包（左）	1	0	1	1	0	0	0	0	0	0

配置数量	A	B	C	a	b	c	d	e	f	g
空调系统控制组件	3	0	0	3	2	2	2	2	2	2
压调系统控制组件	2	0	0	1	1	0	0	0	0	0
应急通风活门	0	0	0	0	0	0	0	0	0	0
单向活门	2	0	0	1	1	1	1	0	1	1
座舱压力控制器	2	0	0	1	1	1	1	1	1	0
排气活门	0	0	0	0	0	0	0	0	0	0
座舱压力信号单元	2	0	0	1	1	1	1	1	0	0
安全活门	1	0	0	1	1	2	0	0	1	1
流量控制活门	2	0	0	1	1	1	0	1	1	1
制冷包（右）	0	0	0	0	0	0	0	0	0	0
制冷包（左）	2	0	1	1	0	0	0	0	0	0

方法	配置金额		可用度
方法	金额/元	下降幅度/%	值	是否满足保障要求
边际分析	22523949.36	—	0.953	是
改进PSO	18890685.36	16.3	0.952	是