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.
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.
Fig.1Layout of final assembly workshop and parts warehouse
Fig.2Distribution process of aircraft final assembly logistics distribution system
Fig.3Game relationship diagram
Fig.4Particle coding scheme
Fig.5Flow 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.1Optimization and solving speed analysis of proposed particle swarm optimization algorithm
Fig.6Variation of utility value obtained by unilateral change of strategy of game players
TP/d
U%
料包存储区
线边库
工位暂存区
14
51.72
76.07
51.45
13
54.30
79.88
54.02
12
57.15
84.08
56.86
11
60.33
88.75
60.02
10
63.88
93.98
63.55
Tab.2Storage resource utilization rate under different production takts
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