基于超启发式算法的备件供应网络结构优化

doi:10.3969/j.issn.1001-506X.2020.03.016

Abstract

Abstract:

In order to improve the efficiency and effectiveness of spare parts supply by optimizing the structure of supply network, three kinds of network structures of spare parts supply are studied, which are the traditional forward supply network, emergency lateral supply network and closed-loop supply network considering maintenance. A multi-objective optimization model with the objectives of the minimum supply cost and the shortest supply time and the constraints of satisfaction rate and inventory is proposed. A hyper-heuristic multi-objective evolutionary algorithm based on the ordering choice function is proposed to solve models in different network structures. By comparing the proposed algorithm with other evolutionary algorithms on the ZDT benchmarks, the superiority of the proposed hyper heuristic algorithm in convergence and distribution is verified. The numerical example shows that, on the one hand, compared with the traditional forward supply network, the lateral and closed-loop supply network can improve the timeliness and economy of spare parts supply. On the other hand, the hyper heuristic algorithm is superior to other meta heuristic algorithms in solving the models.

Key words: spare parts supply, network structure optimization, closed-loop supply network, multi-objective optimization, hyper heuristic algorithm

CLC Number:

Yadong WANG, Quan SHI, Wei XIA, Cai CHEN. Structure optimization of spare parts supply network based on hyper heuristic algorithm[J]. Systems Engineering and Electronics, 2020, 42(3): 620-629.

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References 27

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测试函数	表达式	变量取值范围
ZDT1	f₁=y₁ g=1+9$\sum\limits_{i = 1}^k {{z_i}} $/k h=1-$\sqrt {{f_1}/g} $ f₂(y, z)=g(z)h(f₁(y)g(z))	[0, 1]
ZDT2	f₁=y₁ g=1+9$\sum\limits_{i = 1}^k {{z_i}} $/k h=1-($ {{f_1}/g} $)² f₂(y, z)=g(z)h(f₁(y)g(z))	[0, 1]
ZDT3	f₁=y₁ g=1+9$\sum\limits_{i = 1}^k {{z_i}} $/k h=1-$\sqrt {{f_1}/g} $-($\sqrt {{f_1}/g} $)sin(10πf₁) f₂(y, z)=g(z)h(f₁(y)g(z))	[0, 1]
ZDT4	f₁=y₁ g=1+10k+$\sum\limits_{i = 1}^k {} $(z_i²-10cos(4πf₁)) h=1-($\sqrt {{f_1}/g} $)^1/2 f₂(y, z)=g(z)h(f₁(y)g(z))	y₁∈[0, 1]z_{1, 2, …, k}∈[-5, 5]
ZDT6	f₁=1-exp(-4y₁)sin⁶(6πy₁) g=1+9($\sum\limits_{i = 1}^k {{z_i}} $/k)^1/4 h=1-($\sqrt {{f_1}/g} $)² f₂(y, z)=g(z)h(f₁(y)g(z))	[0, 1]

开放成本/千元	库存容量/个	库存成本/(元/个)	节点
中转仓库1	5	20	50
中转仓库2	8	32	40
中转仓库3	12	40	45
中转仓库4	6	28	50

节点	备件需求/个	缺件损失/(元/个)
用装单位1	18	1 000
用装单位2	15	1 500
用装单位3	22	1 200
用装单位4	16	1 600
用装单位5	25	1 500
用装单位6	18	1 300

算法	适应度函数1			适应度函数2
算法	最大值	最小值	均值	最大值	最小值	均值
HHMOEA	48 250	40 000	43 010	3 538	2 613	3 050
MODE/D	52 200	42 200	46 260	3 840	2 790	3 252
NSGA-Ⅱ	47 290	42 100	44 250	3 895	3 131	3 465
MOPSO	49 710	45 000	46 550	4 647	3 907	4 278

算法	适应度函数1			适应度函数2
算法	最大值	最小值	均值	最大值	最小值	均值
HHMOEA	48 240	40 000	43 420	3 345	2 361	2 785
MODE/D	50 180	42 100	45 120	3 649	2 664	3 115
NSGA-Ⅱ	45 660	42 100	43 310	3 686	3 066	3 387
MOPSO	50 230	45 100	46 870	4 311	3 593	3 941

Structure optimization of spare parts supply network based on hyper heuristic algorithm

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节点	后方仓库1	后方仓库2	中转仓库1	中转仓库2	中转仓库3	中转仓库4	维修点
中转仓库1	100	150	Inf	80	50	50	Inf
中转仓库2	120	100	80	Inf	65	72	Inf
中转仓库3	150	110	50	65	Inf	35	Inf
中转仓库4	100	120	50	72	35	Inf	Inf
用装单位1	Inf	Inf	20	22	25	18	10
用装单位2	Inf	Inf	35	30	38	42	15
用装单位3	Inf	Inf	40	35	45	30	12
用装单位4	Inf	Inf	25	30	26	25	16
用装单位5	Inf	Inf	36	40	45	25	15
用装单位6	Inf	Inf	28	30	25	35	16

处理策略	适应度函数1	适应度函数2
M₀=10	48 560	8 965
M₀=1 000	42 270	2 685
M₀=100 000	42 250	2 685
Lagrange	42 200	2 725

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