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

Abstract

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

CLC Number:

V 25

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.

Figures/Tables 13

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References 32

1	SHERBROOKE C C. METRIC: a multi-echelon technique for recoverable item control[J]. Operations Research, 1968, 16 (1): 122- 141. doi: 10.1287/opre.16.1.122
2	BURNS J F, SIVAZLIAN B D. Dynamic analysis of multi-echelon supply systems[J]. Computers & Industrial Engineering, 1978, 2 (4): 181- 193.
3	SLAY F M. VARI-METRIC: an approach to modeling multi-echelon resupply when the demand process is Poisson with a gamma prior, Report AF301-3[R]. Washington D. C. : Logistics Management Institute, 1984.
4	孙蕾, 左洪福, 刘伟, 等. 基于VARI-METRIC的民机关键LRU多级库存优化配置模型[J]. 南京航空航天大学学报, 2013, 45(4): 532−537.
	SUN L, ZUO H F, LIU W, et al. LRU Multi-echelon inventory optimal model for aircraft parts based on VARI-METRIC model. Journal of Nanjing University of Aeronautics & Astronautics, 2013, 45(4): 532−537.
5	MUCKSTADT J A. A model for a multi-item, multi-echelon, multi-indenture inventory system[J]. Management Science, 1973, 20 (4): 472- 481.
6	冯蕴雯, 李永凯, 薛小锋, 等. 考虑不完全维修的民机可修件多级库存规划[J]. 西北工业大学学报, 2017, 35 (5): 827- 833. doi: 10.3969/j.issn.1000-2758.2017.05.013
	FENG Y W, LI Y K, XUE X F, et al. Multi-echelon inventory allocation under imperfect repair for repairable spares of civil aircraft[J]. Journal of Northwestern Polytechnical University, 2017, 35 (5): 827- 833. doi: 10.3969/j.issn.1000-2758.2017.05.013
7	COSTANTINO F, GRAVIO D G, PATRIARCA R, et al. Spare parts management for irregular demand items[J]. Omega, 2018, 81, 57- 66. doi: 10.1016/j.omega.2017.09.009
8	李京峰, 陈云翔, 项华春, 等. 考虑随机冲击影响的多部件系统视情维修与备件库存联合优化[J]. 系统工程与电子技术, 2022, 44 (3): 875- 883.
	LI J F, CHEN Y X, XIANG H C, et al. Joint optimization of condition-based maintenance and spare part inventoryfor multi-component system considering random shock effect[J]. Systems Engineering and Electronics, 2022, 44 (3): 875- 883.
9	杨伟铁, 徐常凯, 张英峰. 基于OPUS的空军航材随机备件决策模型研究[J]. 舰船电子工程, 2017, 37 (8): 122- 127. doi: 10.3969/j.issn.1672-9730.2017.08.029
	YANG W T, XU C K, ZHANG Y F. Aviation equipment carried spares decision model based on OPUS[J]. Ship Electronic Engineering, 2017, 37 (8): 122- 127. doi: 10.3969/j.issn.1672-9730.2017.08.029
10	CHMIELOWIEC A, KLICH L, WOS W, et al. Global spare parts exploitation costs optymization and its reduction to rectangular knapsack problem[C]//Proc. of the 24th International Conference on Enterprise Information Systems, 2022.
11	GAREY M R, JOHNSON D S. Computers and intractability[M]. New York: W. H Freeman and Comany, 1979.
12	王俊龙, 楼京俊, 阮旻智, 等. 面向满足率与利用率的通用备件优化配置方法[J]. 航空学报, 2022, 43 (5)
	WANG J L, LOU J J, RUAN M Z, et al. Optimization configuration method of common spares fill rate and utilization rate[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43 (5)
13	王乃超, 康锐. 基于备件保障概率的多级库存优化模型[J]. 航空学报, 2009, 30 (6): 1043- 1047. doi: 10.3321/j.issn:1000-6893.2009.06.012
	WANG N C, KANG R. Optimization of multi-echelon repairable item inventory systems with fill rate as objective[J]. Acta Aeronautica et Astronautica Sinica, 2009, 30 (6): 1043- 1047. doi: 10.3321/j.issn:1000-6893.2009.06.012
14	BATCHOUN P, FERLAND J A, CLEROUX R. Allotment of aircraft spare parts using genetic alorithms[J]. Pesquisa Operacional, 2003, 23 (1): 141- 159. doi: 10.1590/S0101-74382003000100011
15	MARSEGUERRA M, ZIO E, PODOFILLINI L. Multiobjective spare part allocation by means of genetic algorithms and Monte Carlo simulation[J]. Reliability Engineering & System Safety, 2005, 87 (3): 325- 335.
16	李圆芳, 樊玮. 基于遗传算法的航材库存控制优化模型[J]. 计算机技术与发展, 2014, 24 (11): 186- 189.
	LI Y F, FAN W. Optimization model of aviation spares inventory control based on genetic algorithm[J]. Computer Technology and Development, 2014, 24 (11): 186- 189.
17	MOHAMMADITABAR D, GHODSYPOUR S H, OBRIEN C. Inventory control system design by integrating inventory classification and policy selection[J]. International Journal of Production Economics, 2012, 140 (2): 655- 659. doi: 10.1016/j.ijpe.2011.03.012
18	DURAN O, PEREZ L. Optimization of the multiechelon system for repairable spare parts using swarm intelligence combined with a local search strategy[C]//Proc. of the International Conference on Computational Science and its Applications, 2014.
19	田静, 张恩豪, 付维方. 改进量子粒子群算法在航材初始配置中的应用[J]. 中国民航大学学报, 2018, 36 (5): 48- 51. doi: 10.3969/j.issn.1674-5590.2018.05.010
	TIAN J, ZHANG E H, FU W F. Application of improved quantum particle swarm algorithm in initial configuration of aircraft spare parts[J]. Journal of Civil Aviation University of China, 2018, 36 (5): 48- 51. doi: 10.3969/j.issn.1674-5590.2018.05.010
20	ALI E S, ELAZIM S M A, ABDELAZIZ A Y. Optimal allocation and sizing of renewable distributed generation using ant lion optimization algorithm[J]. Electrical Engineering, 2018, 100, 99- 109. doi: 10.1007/s00202-016-0477-z
21	张家维, 李昊. 多目标蚁狮算法在航材配置优化中的应用研究[J]. 计算机仿真, 2019, 36 (7): 71- 74，115. doi: 10.3969/j.issn.1006-9348.2019.07.014
	ZHANG J W, LI H. Application of multi-objective ant lion optimizer in optimization of aviation parts allocation[J]. Computer Simulation, 2019, 36 (7): 71- 74，115. doi: 10.3969/j.issn.1006-9348.2019.07.014
22	冯蕴雯, 马红亮, 魏严锋. 民用飞机航材工程与管理[M]. 北京: 科学出版社, 2020.
	FENG Y W, MA H L, WEI Y F. Spare parts engineering and management for civil aircraft[M]. Beijing: Science Press, 2020.
23	孙蕾. 民用飞机多级库存配置方法与管理研究[D]. 南京: 南京航空航天大学, 2013.
	SUN L. Research on multi-echelon inventory allocation method and management for civil aircraft[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2013.
24	陶永才, 杨晨, 马建红, 等. 一种结合IPSO-BP神经网络的备件供应模型研究[J]. 小型微型计算机系统, 2022, 43 (5): 913- 920.
	TAO Y C, YANG C, MA J H, et al. Spare parts supply model based on IPSO BP neural network is studied[J]. Journal of Chinese Computer Systems, 2022, 43 (5): 913- 920.
25	柴志君, 王宗波. 改进粒子群算法在舰船备件配置优化问题中的应用[J]. 计算机与数字工程, 2021, 49 (3): 493- 495, 588. doi: 10.3969/j.issn.1672-9722.2021.03.014
	CAI Z J, WANG Z B. Application of improved particle swarm optimization in the optimization of ship spare parts configuration[J]. Computer & Digital Engineering, 2021, 49 (3): 493- 495, 588. doi: 10.3969/j.issn.1672-9722.2021.03.014
26	卢发兴, 姚鸿鹤, 史浩然. 基于方差分析变量约减的指令制导回路误差分配方法[J]. 系统工程与电子技术, 2020, 42 (5): 1131- 1138.
	LU F X, YAO H H, SHI H R. Error allocation method of instruction guidance loop based onvariance analysis variable reduction[J]. Systems Engineering and Electronics, 2020, 42 (5): 1131- 1138.
27	YANG J M, CHEN Y P, HORNG J T, et al. Applying family competition to evolution strategies for constrained optimization[C]//Proc. of the International Conference on Evolutionary Programming, 1997.
28	SHI Y, EBERHART R C. Empirical study of particle swarm optimization[C]// Proc. of the Congress on Evolutionary Computation, 1999.
29	JAIN M, SAIHJPAL V, SINGH N, et al. An overview of variants and advancements of PSO algorithm[J]. Applied Sciences, 2022, 12 (17): 8392. doi: 10.3390/app12178392
30	TIAN D P, SHI Z Z. MPSO: modified particle swarm optimization and its applications[J]. Swarm and Evolutionary Computation, 2018, 41, 49- 68. doi: 10.1016/j.swevo.2018.01.011
31	陈贵敏, 贾建援, 韩琪. 粒子群优化算法的惯性权值递减策略研究[J]. 西安交通大学学报, 2006, 40 (1): 53- 56. doi: 10.3321/j.issn:0253-987X.2006.01.013
	CHEN G M, JIA J Y, HAN Y. Study on the strategy of decreasing inertia weight in particle swarm optimization algorithm[J]. Journal of Xi’an Jiaotong University, 2006, 40 (1): 53- 56. doi: 10.3321/j.issn:0253-987X.2006.01.013
32	徐生兵. 基于动态调整惯性权重下改进学习因子的粒子群算法[J]. 信息安全与技术, 2014, 5 (4): 26- 28.
	XU S B. A new modified acceleration coefficient in PSO base on dynamic adjustment of inertia weights[J]. Cyberspace Security, 2014, 5 (4): 26- 28.

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

Civil aircraft aviation spare parts inventory configuration method based on improved particle swarm

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