Resource allocation of aircraft final assembly logistics distribution system based on game theory

doi:10.3785/j.issn.1008-973X.2025.01.012

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (1): 120-129 DOI: 10.3785/j.issn.1008-973X.2025.01.012

Resource allocation of aircraft final assembly logistics distribution system based on game theory

Yulong DONG(

),Lu CHEN*(

),Zhongkai BAO

Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai 200240, China

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Abstract

A resource allocation model was established based on game theory, aiming at the problem of resource allocation competition between storage and distribution subsystems in an aircraft final assembly logistics distribution system. The storage subsystems such as package storage area in parts warehouse, line warehouse and station storage area, and the distribution subsystems such as truck and automatic guided vehicle (AGV) were abstracted as game players respectively. Two utility value indexes such as resource input cost and competition efficiency were introduced to evaluate the game strategy combination. A particle swarm optimization algorithm with dynamic adjustment of strategy space was proposed to solve the model, and the feasibility information of the current game strategy combination was effectively utilized to accelerate the utility value calculation. The numerical experiments results show that the obtained resource allocation scheme has Nash equilibrium and the adaptability to increased aircraft production capacity, and the proposed algorithm has better performance in Nash equilibrium, total input cost and single iteration time than comparison algorithms.

Key words： logistics distribution resource allocation game theory strategy space aircraft final assembly

Received: 28 November 2023 Published: 18 January 2025

CLC:	TP 301.6
	F 406

Fund: 国家自然科学基金资助项目（52175475）.

Corresponding Authors: Lu CHEN E-mail: dongyulong@sjtu.edu.cn;chenlu@sjtu.edu.cn

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Cite this article:

Yulong DONG,Lu CHEN,Zhongkai BAO. Resource allocation of aircraft final assembly logistics distribution system based on game theory. Journal of ZheJiang University (Engineering Science), 2025, 59(1): 120-129.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.01.012 OR https://www.zjujournals.com/eng/Y2025/V59/I1/120

基于博弈论的飞机总装物流配送系统资源配置

在飞机总装物流配送系统中，仓储与配送多级子系统之间存在资源配置竞争问题，为此基于博弈论建立资源配置模型. 将包括零件总库料包存储区、线边库与工位暂存区在内的仓储子系统和包括货车、自动引导车(AGV)在内的配送子系统抽象为博弈主体. 引入2个效用值指标：资源投入成本和竞争效率，对博弈策略组合进行评价. 提出融合策略空间动态调整的粒子群优化算法进行模型求解，有效利用当前博弈策略组合的可行性信息，加速效用值计算. 算例实验结果表明，所得资源配置方案具有纳什均衡性和飞机产能提升适应能力，所提算法在纳什均衡性、总投入成本和单次迭代时间上的性能均优于对比算法.

关键词： 物流配送, 资源配置, 博弈论, 策略空间, 飞机总装

Fig.1 Layout of final assembly workshop and parts warehouse

Fig.2 Distribution process of aircraft final assembly logistics distribution system

Fig.3 Game relationship diagram

Fig.4 Particle coding scheme

Fig.5 Flow chart of proposed particle swarm optimization algorithm

$ \left\| I \right\| $	算法	$ N $	$ f $	$ S $/千元	$ K $	$ {t}_{{\mathrm{C}}}/{\mathrm{s}} $	$ t/{\mathrm{s}} $	$ G_f/{\text{%}} $	$ G_S/{\text{%}} $	$ G_K/{\text{%}} $	$ G_{{t}_{{\mathrm{C}}}}/{\text{%}} $	$ G_t/{\text{%}} $
100	PSO	{80,24,12,1,7}	0.10	912	23	1.24	0.05	0.00	0.00	17.39	?8.06	?20.00
	BreedPSO	{80,24,12,1,7}	0.10	912	25	1.15	0.05	0.00	0.00	8.00	?0.87	?20.00
	SimuAPSO	{80,24,12,1,7}	0.10	912	25	1.17	0.05	0.00	0.00	8.00	?2.56	?20.00
	本研究	{80,24,12,1,7}	0.10	912	27	1.14	0.04	—	—	—	—	—
200	PSO	{180,20,20,1,3}	0.10	1356	24	9.50	0.40	?100.00	3.54	62.50	?6.21	?42.50
	BreedPSO	{180,28,20,1,3}	0.00	1404	26	7.13	0.27	0.00	0.00	50.00	24.96	?14.81
	SimuAPSO	{180,20,20,1,3}	0.10	1356	31	7.88	0.25	?100.00	3.54	25.81	13.07	?8.00
	本研究	{180,28,20,1,3}	0.00	1404	39	8.91	0.23	—	—	—	—	—
300	PSO	{280,36,32,1,3}	0.10	2076	48	27.29	0.57	?100.00	?19.08	10.42	?29.83	?36.85
	BreedPSO	{280,20,20,1,8}	0.10	1716	31	21.00	0.68	?100.00	?2.10	70.97	?8.81	?47.06
	SimuAPSO	{280.20,20,1,5}	0.00	1680	45	25.37	0.56	0.00	0.00	17.78	?24.52	?35.71
	本研究	{280,20,20,1,5}	0.00	1680	53	19.15	0.36	—	—	—	—	—
400	PSO	{298,60,24,1,1}	0.10	2034	30	70.44	2.35	?100.00	?3.83	63.33	?72.25	?82.98
	BreedPSO	{281,72,20,1,10}	0.11	2055	30	43.82	1.46	?100.00	?4.82	63.33	?55.39	?72.60
	SimuAPSO	{292,64,20,1,3}	0.00	1956	32	59.81	1.87	0.00	0.00	53.13	?67.32	?78.61
	本研究	{292,64,20,1,3}	0.00	1956	49	19.55	0.40	—	—	—	—	—
500	PSO	{252,104,68,1,3}	0.22	3372	33	133.66	4.05	?54.55	?21.62	54.55	?66.74	?78.52
	BreedPSO	{292,68,40,3,10}	0.13	2844	39	103.96	2.67	?23.08	?7.07	30.77	?57.23	?67.42
	SimuAPSO	{296,68,44,1,5}	0.10	2664	44	129.69	2.95	0.00	?0.79	15.91	?65.72	?70.51
	本研究	{281,72,44,1,5}	0.10	2643	51	44.46	0.87	—	—	—	—	—
600	PSO	{299,72,64,2,10}	0.11	3417	34	229.27	6.74	?9.09	?14.93	41.18	?69.96	?78.78
	BreedPSO	{294,76,52,1,7}	0.10	2946	40	160.49	4.01	0.00	?1.32	20.00	?57.09	?64.34
	SimuAPSO	{294,92,48,1,10}	0.10	2970	45	226.84	5.04	11.11	?2.12	6.67	?69.64	?71.63
	本研究	{285,108,44,1,8}	0.10	2907	48	68.87	1.43	—	—	—	—	—
700	PSO	{339,32,64,2,10}	0.11	3297	33	330.45	10.01	?100.00	?23.93	78.79	?46.92	?70.33
	BreedPSO	{347,76,32,1,10}	0.10	2601	36	262.04	7.28	?100.00	?3.58	63.89	?33.06	?59.20
	SimuAPSO	{347,100,20,1,6}	0.11	2373	39	356.77	9.15	?100.00	5.69	51.28	?50.83	?67.54
	本研究	{348,76,32,1,2}	0.00	2508	59	175.42	2.97	—	—	—	—	—
812	PSO	{352,32,52,2,4}	0.13	2940	38	365.65	9.62	?100.00	?25.82	36.84	?19.73	?41.37
	BreedPSO	{396,20,32,1,10}	0.10	2412	37	302.97	8.19	?100.00	?9.58	40.54	?3.13	?31.14
	SimuAPSO	{399,44,20,1,5}	0.00	2181	44	382.62	8.70	0.00	0.00	18.18	?23.29	?35.17
	本研究	{399,44,20,1,5}	0.00	2181	52	293.50	5.64	—	—	—	—	—

Tab.1 Optimization and solving speed analysis of proposed particle swarm optimization algorithm

Fig.6 Variation of utility value obtained by unilateral change of strategy of game players

Tab.2 Storage resource utilization rate under different production takts


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