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A Lean Scheduling Framework for Underground Mines Based on Short Interval Control

Author

Listed:
  • Hao Wang

    (Mine Big Data Research Institute, China Coal Research Institute, Beijing 100013, China
    State Key Laboratory of Coal Mining and Clean Utilization, Beijing 100013, China)

  • Xiaoxia Zhang

    (Mine Big Data Research Institute, China Coal Research Institute, Beijing 100013, China
    State Key Laboratory of Coal Mining and Clean Utilization, Beijing 100013, China)

  • Hui Yuan

    (Mine Big Data Research Institute, China Coal Research Institute, Beijing 100013, China
    State Key Laboratory of Coal Mining and Clean Utilization, Beijing 100013, China)

  • Zhiguang Wu

    (Mine Big Data Research Institute, China Coal Research Institute, Beijing 100013, China
    State Key Laboratory of Coal Mining and Clean Utilization, Beijing 100013, China)

  • Ming Zhou

    (Mine Big Data Research Institute, China Coal Research Institute, Beijing 100013, China
    State Key Laboratory of Coal Mining and Clean Utilization, Beijing 100013, China)

Abstract

Production scheduling management is crucial for optimizing mine productivity. With the trend towards intelligent mines, a lean scheduling management mode is required to align with intelligent conditions. This paper proposes a lean scheduling framework, based on short interval control as an effective tool to adapt intelligent scheduling in underground mines. The framework shortens the production monitoring and adjustment cycle to near-real-time, enabling timely corrective measures to minimize schedule deviations and improve overall production efficiency. An intelligent scheduling platform is implemented by adopting the digital twin platform framework, the intelligent scheduling mobile terminal module, and the integrated scheduling control cockpit module. The results indicate that the platform is effective in promoting mine intelligence by providing benefits in information transparency, flexible scheduling, lean production, and scientific decision-making. The proposed framework provides a practical solution for implementing intelligent scheduling in underground mines, contributing to the overall improvement of mine productivity. Overall, this paper provides insights for implementing intelligent scheduling in underground mines.

Suggested Citation

  • Hao Wang & Xiaoxia Zhang & Hui Yuan & Zhiguang Wu & Ming Zhou, 2023. "A Lean Scheduling Framework for Underground Mines Based on Short Interval Control," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9195-:d:1165353
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    References listed on IDEAS

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    1. Naga Vamsi Krishna Jasti & Rambabu Kodali, 2015. "Lean production: literature review and trends," International Journal of Production Research, Taylor & Francis Journals, vol. 53(3), pages 867-885, February.
    2. Kansake, Bruno Ayaga & Kaba, Felix Adaania & Dumakor-Dupey, Nelson Kofi & Arthur, Clement Kweku, 2019. "The future of mining in Ghana: Are stakeholders prepared for the adoption of autonomous mining systems?," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
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