自动化码头AGV电池备件与充电机配置联合决策

doi:10.12305/j.issn.1001-506X.2023.04.15

摘要/Abstract

摘要：

以自动化集装箱码头换电式自动导引车(automated guided vehicle, AGV)电池换电系统为研究对象, 采用双有限源封闭循环描述AGV电池换电队列与充电队列, 以满足一定工作水平下的总成本最低为目标, 基于马尔可夫过程建立电池备件和充电机配置联合优化模型。通过遗传算法求解, 得到最优电池备件数与充电机数量。研究结果表明, 电池备件数或充电机数过少时, 将产生高额的惩罚成本与利润损失。相较于传统的用一备一策略, 采取最优策略使得运营总成本显著降低。

关键词: 自动化集装箱码头, 自动导引车, 换电, 电池备件, 充电机配置

Abstract:

Taking the battery swapping system of the automated guided vehicle (AGV) in automated container terminal as the research object, the double finite source closed cycle is used to describe the AGV battery swapping queue and the charging queue. To minimize the total cost under a certain working level, a joint optimization model of battery spare parts and charger configuration is established based on the Markov process. The optimal number of battery spare parts and charging devices can be obtained by the genetic algorithm. The results show that when the number of battery spare parts or charging devices is too small, the penalty cost and profit loss will be high. Compared with the traditional one spare part for one device strategy, the optimal strategy can significantly reduce the total operating cost.

Key words: automated container terminal, automated guided vehicle (AGV), swapping battery, battery spare parts, charger configuration

中图分类号:

O224

王孟雅, 李雨青, 李林蔓, 吴伟, 潘尔顺, 刘冉. 自动化码头AGV电池备件与充电机配置联合决策[J]. 系统工程与电子技术, 2023, 45(4): 1072-1081.

Mengya WANG, Yuqing LI, Linman LI, Wei WU, Ershun PAN, Ran LIU. Joint decision-making of AGV battery spare parts and charger configuration in automated terminal[J]. Systems Engineering and Electronics, 2023, 45(4): 1072-1081.

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符号	符号描述
N	AGV数量
M	换电系统内所有电池
s	换电设备总数
c	充电机总数
s=(i, b)	系统状态, i∈[0, N]为换电与换电排队的AGV数量, b∈[0, M－N]为换电系统中满电电池数量

参数符号	符号描述	随机分布性质
λ	换电系统AGV到达数量	泊松分布
T_s	换电设备的一次换电操作时间	常数
T_c	假设充电时间T_c服从, 对应充电服务率μ_c=1/T_c	指数分布

参数	参数实际意义	取值
d	换电机个数/个	10
λ	AGV到达率/(辆/小时)	1/8
T_c	充电时间均值/h	3
T_s	换电操作时间/min	6
ε	最低工作水平	0.9
a_bat	投资年值化系数	0.1
c_bat	电池购买单价/(万元/个)	4
a_con	折旧年值化系数	0.05
f_con	充电桩建设费用/(万元/个)	20
r_loss	利润损失系数/(万元/(辆·年))	7 000
c_pen	惩罚成本/(万元/(辆·天))	1
c_cha	充电单价/(元/千瓦时)	0.4
P_cha	充电功率/kW	5
η_cha	充电效率/%	90
a_year	年值化系数	365
p_c	交叉概率	0.8
p_m	变异概率	0.2
p_e	精英保留比率	0.2
p_ls	邻域搜索概率	0.6
popsize	种群大小	20

算例	MaxGA/万元	MeanGA/万元	OPT/万元	MaxDev/%	MeanDev/%
1	77.52	76.77	76.53	1.29	0.31
2	45.01	44.21	43.11	4.41	2.55
3	61.39	61.39	61.35	0.07	0.07
4	79.61	79.47	79.45	0.20	0.03
5	99.12	98.37	97.34	1.83	1.06

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