Transition to intelligent fleet management systems in open pit mines: A critical review on application of reinforcement-learning-based systems

被引:3
作者
Hazrathosseini, Arman [1 ]
Moradi Afrapoli, Ali [1 ]
机构
[1] Laval Univ, Dept Min Met & Mat Engn, IntelMine Lab, 1728 Pavillon Adrien Pouliot,1065 Ave Medecine, Quebec City, PQ G1V 0A6, Canada
关键词
open-pit mines; fleet management system; truck-shovel system; intelligent dispatching; reinforcement learning; multi-agent algorithm; NEURAL-NETWORKS; OPTIMIZATION; GAME;
D O I
10.1177/25726668231222998
中图分类号
TD [矿业工程];
学科分类号
0819 ;
摘要
The mathematical methods developed so far for addressing truck dispatching problems in fleet management systems (FMSs) of open-pit mines fail to capture the autonomy and dynamicity demanded by Mining 4.0, having led to the popularity of reinforcement learning (RL) methods capable of capturing real-time operational changes. Nonetheless, this nascent field feels the absence of a comprehensive study to elicit the shortfalls of previous studies in favour of more mature future works. To fill the gap, the present study attempts to critically review previously published articles in RL-based mine FMSs through both developing a five-feature-class scale embedded with 29 widely used dispatching features and an insightful review of basics and trends in RL. Results show that 60% of those features were neglected in previous works and that the underlying algorithms have many potentials for improvement. This study also laid out future research directions, pertinent challenges and possible solutions.
引用
收藏
页码:50 / 73
页数:24
相关论文
共 124 条
[1]  
Achiam Joshua, 2018, Spinning up in deep reinforcement learning
[2]   A stochastic hybrid simulation-optimization approach towards haul fleet sizing in surface mines [J].
Afrapoli, Ali Moradi ;
Tabesh, Mohammad ;
Askari-Nasab, Hooman .
MINING TECHNOLOGY-TRANSACTIONS OF THE INSTITUTIONS OF MINING AND METALLURGY, 2019, 128 (01) :9-20
[3]   A multiple objective transportation problem approach to dynamic truck dispatching in surface mines [J].
Afrapoli, Ali Moradi ;
Tabesh, Mohammad ;
Askari-Nasab, Hooman .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2019, 276 (01) :331-342
[4]   An extended hybrid fuzzy multi-criteria decision model for sustainable and resilient supplier selection [J].
Afrasiabi, Ahmadreza ;
Tavana, Madjid ;
Di Caprio, Debora .
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2022, 29 (25) :37291-37314
[5]  
Alarie S., 2002, INT J SURFACE MINING, V16, P59, DOI [10.1076/ijsm.16.1.59.3408, DOI 10.1076/IJSM.16.1.59.3408]
[6]   Decision-Making System for Lane Change Using Deep Reinforcement Learning in Connected and Automated Driving [J].
An, HongIl ;
Jung, Jae-il .
ELECTRONICS, 2019, 8 (05)
[7]  
[Anonymous], 2010, Caterpillar performance handbook
[8]  
Askari-Nasab H., 2014, OREBODY MODELLING ST, P299
[9]  
BAIRD LC, 1994, 1994 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, VOL 1-7, P2448, DOI 10.1109/ICNN.1994.374604
[10]  
Bastos GS, 2011, IEEE INT C INTELL TR, P1057, DOI 10.1109/ITSC.2011.6082902