Intelligent Optimization Method of Cloud Manufacturing Swarm Based on Incomplete Information Game

doi:10.16182/j.issn1004731x.joss.22-1500

Abstract

Abstract:

In the process of cloud manufacturing, the incomplete information status and the mutual competition and restriction relationship between cloud platform operator and demander lead to the difficult choice of manufacturing services. A cloud manufacturing swarm intelligent optimization method based on incomplete information game model is proposed. A static game model based on incomplete information is established for the interest competition between demand-side and cloud platform, with the goal of rationally pursuing the maximization of their own revenue function. The competition rules between demand-side and cloud platform are proposed, which are introduced into nature through Harsanyi transformation and converted into a dynamic game under complete information to obtain Bayesian extended formula, and the existence and uniqueness of Bayesian Nash equilibrium are proved. A particle swarm optimization algorithm based on the update of Gaussian function and perturbation strategy is proposed to solve the above model. The simulation shows that the improved algorithm has faster convergence rate and higher total revenue of cloud manufacturing system compared with the other algorithms, and the incomplete information game model can take into account different types of demand side to improve the total revenue of cloud manufacturing system.

Key words: cloud manufacturing, the game, incomplete information, Bayesian Nash equilibrium, income function, PSO

CLC Number:

TP391.9

Zhang Kunpeng, Wang Yan, Ji Zhicheng. Intelligent Optimization Method of Cloud Manufacturing Swarm Based on Incomplete Information Game[J]. Journal of System Simulation, 2024, 36(4): 915-928.

Figures/Tables 23

Fig. 1

Fig. 2

Table 1

Table 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Table 3

Task information processing

候选资源	$O 1$	$O 2$	$O 3$	$O 4$	$O 5$	$O 6$
$R 1$	20/125/0.75	—	30/100/0.70	—	25/115/0.85	—
$R 2$	—	35/100/0.70	25/100/0.60	20/110/0.50	—	30/130/0.80
$R 3$	30/110/0.60	—	30/120/0.85	—	30/110/0.80	40/120/0.85
$R 4$	—	20/130/0.70	—	60/120/0.90	—	—
$R 5$	20/120/0.70	—	—	50/100/0.70	—	40/110/0.90
$R 6$	—	30/130/0.80	40/120/0.85	—	40/110/0.90	—
$R 7$	—	25/110/0.60	—	—	—	35/100/0.70
$R 8$	—	—	25/100/0.60	—	50/120/0.95	—
$R 9$	—	30/110/0.60	—	40/130/0.80	—	—
$R 10$	35/100/0.80	—	20/130/0.80	—	40/120/0.95	35/120/0.90
$R 11$	30/100/0.60	35/120/0.80	—	45/130/0.85	—	—
$R 12$	—	—	35/120/0.80	—	30/100/0.75	40/130/0.95
$R 13$	40/90/0.70	—	—	35/110/0.65	—	—
$R 14$	20/120/0.70	—	30/110/0.60	—	20/120/0.60	—
$R 15$	—	—	—	35/120/0.70	—	30/120/0.85
$R 16$	—	25/110/0.65	—	50/110/0.80	—	25/100/0.60

Table 3

Fig. 8

Table 4

Table 5

Table 6

Optimal solution

算法	最优解方案
文献[7]	$R 5 → R 4 → R 10 → R 2 → R 1 → R 16$
文献[10]	$R 5 → R 3 → R 6 → R 2 → R 8 → R 16$
本文	$R 5 → R 4 → R 10 → R 2 → R 1 → R 16$

Table 6

Table 7

Table 8

Fig. 9

Fig. 10

Table 9

Candidate resource set

任务	候选资源集
$O 1$	${R 1, R 3, R 5, R 10, R 11, R 13, R 14}$
$O 2$	${R 6, R 7, R 9, R 11, R 16}$
$O 3$	${R 1, R 3, R 6, R 8, R 10, R 12, R 13}$
$O 4$	${R 5, R 9, R 11, R 13, R 15, R 16}$
$O 5$	${R 1, R 3, R 6, R 8, R 10, R 12, R 14}$
$O 6$	${R 3, R 5, R 7, R 10, R 12, R 15, R 16}$

Table 9

Table 10

Optimal solution

算法	资源选择方案
文献[7]	$R 5 → R 7 → R 10 → R 13 → R 1 → R 16$
文献[10]	$R 5 → R 3 → R 6 → R 2 → R 15 → R 16$
本文	$R 5 → R 7 → R 10 → R 13 → R 1 → R 16$

Table 10

Fig. 11

Table 11

Table 12

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关系	拒绝	接受
竞争	(a, b)	(c, d)
不竞争	(0, 0)	(0, 0.1v₂)

关系	拒绝	接受
竞争	(e, f )	(g, h)
不竞争	(0, 0)	(0, 0.1v₂)

算法	收益	C/元	t/min	Q	CPU/s
文献[7]	0.156	4 005	700	20.5	42.8
文献[10]	0.162	3 980	725	20.1	31.4
本文	0.177	3 935	675	20.5	30.6

算法	收益	C/元	S	CPU/s
文献[7]	0.116	852.5	0.8	42.8
文献[10]	0.115	877.5	0.8	31.4
本文	0.128	812.5	0.8	30.6

算法	总收益	C/元	t/min	Q	CPU/s
文献[7]	0.276	4 015	720	20.4	44.5
文献[10]	0.279	3 995	715	20.5	32.1
本文	0.314	3 945	680	20.4	31.5