论文成果

Reinforcement learning based optimization algorithm for maintenance tasks scheduling in coalbed methane gas field

摘要:The Coalbed Methane (CBM) well maintenance tasks scheduling optimization is of importance for improving the CBM production efficiency. Traditionally, this problem is addressed using mathematical model based classical optimization method or some meta-heuristic algorithms. However, due to the large-scale nature of this problem, these trials often fail in practical use. Therefore, the Q-learning algorithm based solving method is proposed in this paper. An interactive environment for reinforcement learning is constructed. To validate the effectiveness of proposed method, scenarios with different scales are provided. For the cases with 10, 14, 20, 30, 47, 60, 80 and 100 maintenance tasks respectively, the required solution time is 3.66 s, 4.94 s, 7.66 s, 12.48 s, 21.82 s, 30.82 s, 48.33 s, 73.17 s respectively. The proposed Q-learning algorithm is insensitive with the problem scale, which is promising. Moreover, the Q-learning based algorithm is more efficient than the traditional algorithm along with the increase of the number of tasks. ? 2022

ISSN号:0098-1354

卷、期、页:v 170,

发表日期:2023-02-01

影响因子:0.000000

期刊分区(SCI为中科院分区):二区

收录情况:EI(工程索引)

发表期刊名称:Computers and Chemical Engineering

参与作者:潘军,李菲菲

通讯作者:彭雕,夔国凤

第一作者:高小永,左信

论文类型:期刊论文

论文概要:高小永,彭雕,夔国凤,潘军,左信,李菲菲,Reinforcement learning based optimization algorithm for maintenance tasks scheduling in coalbed methane gas field,Computers and Chemical Engineering,2023,v 170,

论文题目:Reinforcement learning based optimization algorithm for maintenance tasks scheduling in coalbed methane gas field