Reinforcement Learning-Based Fleet Dispatching for Greenhouse Gas Emission Reduction in Open-Pit Mining Operations

Huo, Da; Sari, Yuksel Asli; Kealey, Ryan; Zhang, Qian

doi:10.1016/j.resconrec.2022.106664

Cited by 31 publications

(10 citation statements)

References 25 publications

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“…Furthermore, a well-trained set of agents following the RL approach can make knowledgeable decisions more swiftly. This has been a driving motivation for developing RL-based FMSs in open-pit mines by a number of researchers (Bastos et al, 2011;De Carvalho and Dimitrakopoulos, 2021;Huo et al, 2023;, the works of whom are exclusively discussed in the rest of the present study.…”

Section: An Intelligent Solutionmentioning

confidence: 98%

“…The emission reduction capability of an RL-based dispatching system in open-pit mining operations was assessed by Huo et al (2023) through a combination of truck-shovel simulations and real-time estimations of GHG emissions from haulage fuel consumption. They applied the standard Q-learning algorithm, arguing that their model had limited state space.…”

Section: Modelmentioning

confidence: 99%

“…Most works have been formulated as a multi-agent problem, but none of them enjoys the multiobjective structure. Some works such as Huo et al (2023) encompass three objectives in the reward function, yet the problem is still of single objective because the reward is not in a vector form. The learning scheme in half of these studies is decentralised learning; however, as discussed earlier, this strategy causes non-stationarity and nonconvergence.…”

Section: Research Directionsmentioning

confidence: 99%

“…(1) Single-objective formulation. Huo et al (2023) (1) Multi-agent setting, (2) intricate reward shaping, (3) scalable operation, and (4) addressing some unprecedented features such as truck maintenance and fuel consumption.…”

Section: Articlementioning

confidence: 99%

“…Having treated the dispatching problem as single-objective, the authors in previous works were compelled to insert their desirable objectives into a reward function outputting only a single signal. Therefore, some reward functions such as the one developed by Huo et al (2023) are long-notated to incorporate different goals. In contrast, shaped their reward as a two-variable fraction, with the action duration placed in the denominator to be minimised.…”

Section: Articlementioning

confidence: 99%

See 4 more Smart Citations

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

Hazrathosseini,

Moradi Afrapoli

2024

Mining Technology: Transactions of the Institutions of Mining a

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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.

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Section: An Intelligent Solutionmentioning

confidence: 98%

Section: Modelmentioning

confidence: 99%

Section: Research Directionsmentioning

confidence: 99%

Section: Articlementioning

confidence: 99%

Section: Articlementioning

confidence: 99%

See 3 more Smart Citations

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

Hazrathosseini,

Moradi Afrapoli

2024

Mining Technology: Transactions of the Institutions of Mining a

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Reinforcement learning applications in environmental sustainability: a review

Zuccotto,

Castellini,

Torre

et al. 2024

Artif Intell Rev

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Environmental sustainability is a worldwide key challenge attracting increasing attention due to climate change, pollution, and biodiversity decline. Reinforcement learning, initially employed in gaming contexts, has been recently applied to real-world domains, including the environmental sustainability realm, where uncertainty challenges strategy learning and adaptation. In this work, we survey the literature to identify the main applications of reinforcement learning in environmental sustainability and the predominant methods employed to address these challenges. We analyzed 181 papers and answered seven research questions, e.g., “How many academic studies have been published from 2003 to 2023 about RL for environmental sustainability?” and “What were the application domains and the methodologies used?”. Our analysis reveals an exponential growth in this field over the past two decades, with a rate of 0.42 in the number of publications (from 2 papers in 2007 to 53 in 2022), a strong interest in sustainability issues related to energy fields, and a preference for single-agent RL approaches to deal with sustainability. Finally, this work provides practitioners with a clear overview of the main challenges and open problems that should be tackled in future research.

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Intelligent Fleet Management Systems in Surface Mining: Status, Threats, and Opportunities

Hazrathosseini,

Afrapoli

2023

Mining, Metallurgy & Exploration

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Reinforcement Learning-Based Fleet Dispatching for Greenhouse Gas Emission Reduction in Open-Pit Mining Operations

Cited by 31 publications

References 25 publications

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

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

Reinforcement learning applications in environmental sustainability: a review

Intelligent Fleet Management Systems in Surface Mining: Status, Threats, and Opportunities

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