IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i16p6939-d1455432.html
   My bibliography  Save this article

Improved Multi-Objective Beluga Whale Optimization Algorithm for Truck Scheduling in Open-Pit Mines

Author

Listed:
  • Pengchao Zhang

    (School of Economics and Management, Guangxi University of Science and Technology, Liuzhou 545006, China)

  • Xiang Liu

    (School of Economics and Management, Guangxi University of Science and Technology, Liuzhou 545006, China)

  • Zebang Yi

    (School of Earth Sciences, Guilin University of Technology, Guilin 541004, China)

  • Qiuzhi He

    (School of Economics and Management, Guangxi University of Science and Technology, Liuzhou 545006, China)

Abstract

Big data and artificial intelligence have promoted mining innovation and sustainable development, and the transportation used in open-pit mining has increasingly incorporated unmanned driving, real-time information sharing, and intelligent algorithm applications. However, the traditional manual scheduling used for mining transportation often prioritizes output over efficiency and quality, resulting in high operational expenses, traffic jams, and long lines. In this study, a novel scheduling model with multi-objective optimization was created to overcome these problems. Production, demand, ore grade, and vehicle count were the model’s constraints. The optimization goals were to minimize the shipping cost, total waiting time, and ore grade deviation. An enhanced multi-objective beluga whale optimization (IMOBWO) algorithm was implemented in the model. The algorithm’s superior performance was demonstrated in ten test functions, as well as the IEEE 30-bus system. It was enhanced by optimizing the population initialization, improving the adaptive factor, and adding dynamic domain perturbation. The case analysis showed that, in comparison to the other three conventional multi-objective algorithms, IMOBWO reduced the shipping cost from 7.65 to 0.84%, the total waiting time from 35.7 to 7.54%, and the ore grade deviation from 14.8 to 3.73%. The implementation of this algorithm for truck scheduling in open-pit mines increased operational efficiency, decreased operating costs, and advanced intelligent mine construction and transportation systems. These factors play a significant role in the safety, profitability, and sustainability of open-pit mines.

Suggested Citation

  • Pengchao Zhang & Xiang Liu & Zebang Yi & Qiuzhi He, 2024. "Improved Multi-Objective Beluga Whale Optimization Algorithm for Truck Scheduling in Open-Pit Mines," Sustainability, MDPI, vol. 16(16), pages 1-21, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6939-:d:1455432
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/16/6939/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/16/6939/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kanso, Ali & Smaoui, Nejib, 2009. "Logistic chaotic maps for binary numbers generations," Chaos, Solitons & Fractals, Elsevier, vol. 40(5), pages 2557-2568.
    2. Topal, Erkan & Ramazan, Salih, 2010. "A new MIP model for mine equipment scheduling by minimizing maintenance cost," European Journal of Operational Research, Elsevier, vol. 207(2), pages 1065-1071, December.
    3. Souza, M.J.F. & Coelho, I.M. & Ribas, S. & Santos, H.G. & Merschmann, L.H.C., 2010. "A hybrid heuristic algorithm for the open-pit-mining operational planning problem," European Journal of Operational Research, Elsevier, vol. 207(2), pages 1041-1051, December.
    4. Moradi Afrapoli, Ali & Tabesh, Mohammad & Askari-Nasab, Hooman, 2019. "A multiple objective transportation problem approach to dynamic truck dispatching in surface mines," European Journal of Operational Research, Elsevier, vol. 276(1), pages 331-342.
    5. Choi, Yosoon & Nguyen, Hoang & Bui, Xuan-Nam & Nguyen-Thoi, Trung, 2022. "Optimization of haulage-truck system performance for ore production in open-pit mines using big data and machine learning-based methods," Resources Policy, Elsevier, vol. 75(C).
    6. Patterson, S.R. & Kozan, E. & Hyland, P., 2017. "Energy efficient scheduling of open-pit coal mine trucks," European Journal of Operational Research, Elsevier, vol. 262(2), pages 759-770.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. King, Barry & Goycoolea, Marcos & Newman, A., 2017. "Optimizing the open pit-to-underground mining transition," European Journal of Operational Research, Elsevier, vol. 257(1), pages 297-309.
    2. Pérez, Juan & Maldonado, Sebastián & González-Ramírez, Rosa, 2018. "Decision support for fleet allocation and contract renegotiation in contracted open-pit mine blasting operations," International Journal of Production Economics, Elsevier, vol. 204(C), pages 59-69.
    3. Noriega, Roberto & Pourrahimian, Yashar, 2022. "A systematic review of artificial intelligence and data-driven approaches in strategic open-pit mine planning," Resources Policy, Elsevier, vol. 77(C).
    4. Zeng, Lanyan & Liu, Shi Qiang & Kozan, Erhan & Corry, Paul & Masoud, Mahmoud, 2021. "A comprehensive interdisciplinary review of mine supply chain management," Resources Policy, Elsevier, vol. 74(C).
    5. Chaowasakoo, Patarawan & Seppälä, Heikki & Koivo, Heikki & Zhou, Quan, 2017. "Improving fleet management in mines: The benefit of heterogeneous match factor," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1052-1065.
    6. Matamoros, Martha E. Villalba & Dimitrakopoulos, Roussos, 2016. "Stochastic short-term mine production schedule accounting for fleet allocation, operational considerations and blending restrictions," European Journal of Operational Research, Elsevier, vol. 255(3), pages 911-921.
    7. Lamghari, Amina & Dimitrakopoulos, Roussos, 2012. "A diversified Tabu search approach for the open-pit mine production scheduling problem with metal uncertainty," European Journal of Operational Research, Elsevier, vol. 222(3), pages 642-652.
    8. Franco-Sepúlveda, Giovanni & Del Rio-Cuervo, Juan Camilo & Pachón-Hernández, María Angélica, 2019. "State of the art about metaheuristics and artificial neural networks applied to open pit mining," Resources Policy, Elsevier, vol. 60(C), pages 125-133.
    9. S.R. Patterson & E. Kozan & P. Hyland, 2016. "An integrated model of an open-pit coal mine: improving energy efficiency decisions," International Journal of Production Research, Taylor & Francis Journals, vol. 54(14), pages 4213-4227, July.
    10. Coelho, V.N. & Grasas, A. & Ramalhinho, H. & Coelho, I.M. & Souza, M.J.F. & Cruz, R.C., 2016. "An ILS-based algorithm to solve a large-scale real heterogeneous fleet VRP with multi-trips and docking constraints," European Journal of Operational Research, Elsevier, vol. 250(2), pages 367-376.
    11. Coelho, Vitor N. & Coelho, Igor M. & Coelho, Bruno N. & Cohen, Miri Weiss & Reis, Agnaldo J.R. & Silva, Sidelmo M. & Souza, Marcone J.F. & Fleming, Peter J. & Guimarães, Frederico G., 2016. "Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid," Renewable Energy, Elsevier, vol. 89(C), pages 730-742.
    12. Elmanfaloty, Rania A. & Abou-Bakr, Ehab, 2019. "Random property enhancement of a 1D chaotic PRNG with finite precision implementation," Chaos, Solitons & Fractals, Elsevier, vol. 118(C), pages 134-144.
    13. Jiskani, Izhar Mithal & Cai, Qingxiang & Zhou, Wei & Ali Shah, Syed Ahsan, 2021. "Green and climate-smart mining: A framework to analyze open-pit mines for cleaner mineral production," Resources Policy, Elsevier, vol. 71(C).
    14. Jie Hou & Guoqing Li & Lianyun Chen & Hao Wang & Nailian Hu, 2022. "Optimization of Truck–Loader Matching Based on a Simulation Method for Underground Mines," Sustainability, MDPI, vol. 15(1), pages 1-14, December.
    15. Christina N. Burt & Lou Caccetta, 2014. "Equipment Selection for Surface Mining: A Review," Interfaces, INFORMS, vol. 44(2), pages 143-162, April.
    16. Patterson, S.R. & Kozan, E. & Hyland, P., 2017. "Energy efficient scheduling of open-pit coal mine trucks," European Journal of Operational Research, Elsevier, vol. 262(2), pages 759-770.
    17. Jyrki Savolainen & Michele Urbani, 2021. "Maintenance optimization for a multi-unit system with digital twin simulation," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1953-1973, October.
    18. Xia, Tangbin & Xi, Lifeng & Zhou, Xiaojun & Lee, Jay, 2012. "Dynamic maintenance decision-making for series–parallel manufacturing system based on MAM–MTW methodology," European Journal of Operational Research, Elsevier, vol. 221(1), pages 231-240.
    19. Nakousi, C. & Pascual, R. & Anani, A. & Kristjanpoller, F. & Lillo, P., 2018. "An asset-management oriented methodology for mine haul-fleet usage scheduling," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 336-344.
    20. Strang, Kenneth David, 2012. "Importance of verifying queue model assumptions before planning with simulation software," European Journal of Operational Research, Elsevier, vol. 218(2), pages 493-504.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6939-:d:1455432. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.