Intelligent Optimization of Coal Terminal Unloading Scheduling Based on Improved D3QN Algorithm

doi:10.16182/j.issn1004731x.joss.22-1320

Abstract

Abstract:

Intelligent decision scheduling can improve the operation efficiency of large ports, which is one of the important research directions for the implementation of artificial intelligence technology in the smart port scenario. This article studies the intelligent unloading scheduling tasks of coal terminals and abstracts them as a Markov sequence decision problem. A deep reinforcement learning model for this problem is established, and an improved D3QN algorithm is proposed to realize intelligent optimization of unloading scheduling decisions by considering the characteristics of high action space dimension and sparse feasible action in the model. The simulation results show that for the same set of random task sequences, the optimized scheduling strategy obviously improves the efficiency compared with the random strategy. At the same time, the trained scheduling strategy is directly applied to the randomly generated new task sequence, and the scheduling efficiency is improved by 5%~7%, which indicates that the optimization method has good generalization ability. In addition, with the increasing complexity of decision models, traditional heuristic optimization algorithms are faced with prominent problems such as difficult modeling and low solving efficiency. This article provides a new idea for studying this kind of problem, which is expected to realize the wider application of deep reinforcement learning-based intelligent decision-making in port scheduling tasks.

Key words: terminal unloading scheduling, scheduling strategy optimization, intelligent decision-making, deep reinforcement learning, Dueling Double DQN algorithm

CLC Number:

TP391.9

Qin Baoxin, Zhang Yuxiao, Wu Sirui, Cao Weichong, Li Zhan. Intelligent Optimization of Coal Terminal Unloading Scheduling Based on Improved D3QN Algorithm[J]. Journal of System Simulation, 2024, 36(3): 770-781.

Figures/Tables 18

Fig. 1

Fig. 2

Table 1

Status information of port unloading process

符号	含义
n	火车总数
F	码头翻车机设备集
D	码头堆料机设备集
k	进港火车的序号
i,j	翻车机与堆料机的序号，i,j =1,2,3,4
$π i j k$	第k辆火车选择的翻车机和堆料机
$A i j k$	$1, 第 k 列火车使用第 i 台翻车机 0, 存在设备占用情况动作不可行$
t_i	翻车机i工作的剩余时间
t_j	堆料机j工作的剩余时间
T^k	第k辆火车的作业耗时
T_all	所有火车的总完成时间, $T a l l = ∑ k = 1 n t k$

Table 1

Table 2

Status information of port unloading process

状态空间	数据结构	维数	类型	含义
T_wait	数组	4	Int	火车的等待时间
M_size	数组	42	Int	42个煤堆的存量
M_kind	数组	42	Int	42个煤堆的种类
X_size	数组	4	Int	火车运煤量
X_kind	数组	4	Int	火车运煤种类
Q	数组	4	Bool	传送带占用情况
Z	数组	4	Bool	翻车机占用情况
Q_i	数组	4	Int	4条传送带对应的煤堆
C_i	数组	4	Int	4列火车对应的翻车机
$T o c c u p y i$	数组	4	Int	4条传送带占用时间
Q_z	数组	4	Int	堆料机与翻车机的对应关系

Table 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Table 3

Fig. 10

Table 4

Table 5

Port history scheduling information

翻车机	车型	煤种	作业实绩
翻车机	车型	煤种	作业时间/h	大票吨
CD2	C80	外购55	111.80	4 320
CD1	C80	神混45	90.38	4 320
CD4	C80	外购55	77.50	4 320
CD3	C64	外石5 000	0	768
$⋮$
CD4	C80	外购45	78.63	4 320
CD3	C64	外石5 000	92.78	4 224
CD2	C80	神混45	82.53	4 320

Table 5

Fig. 11

Fig. 12

Table 6

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任务	方法决策	非法动作次数	堆料机切换次数	调度总时长/h	优化比例/%
1	随机策略	基准	[20,60,58,19]	321.21	基准
1	训练模型策略	约10	[16,35,31,16]	301.10	6.7
2	随机策略	基准	[33,55,63,25]	332.45	基准
2	训练模型策略	约12	[21,38,30,18]	309.21	6.4

月份	t/h		优化比例/%
月份	人工排产	智能排产	优化比例/%
1	824.03	691.70	16.0
2	721.24	693.79	3.80
3	658.05	620.14	5.80
4	798.50	621.92	22.4
5	710.46	693.23	2.40
6	807.93	632.84	21.7

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