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A multiple objective transportation problem approach to dynamic truck dispatching in surface mines

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  • Moradi Afrapoli, Ali
  • Tabesh, Mohammad
  • Askari-Nasab, Hooman

Abstract

In surface mining operations, fleet management systems seek to make optimal decisions to handle material in two steps: path production optimization and real-time truck dispatching. This paper develops a multiple objective transportation model for real-time truck dispatching. The model addresses two major drawbacks of former models. The proposed model dispatches the trucks to destinations trying to simultaneously minimize shovel idle times, truck wait times, and deviations from the path production requirements established by the production optimization stage. To evaluate the performance of the proposed model, we developed a benchmark model based on the backbone of the most widely used fleet management system in the mining industry (Modular Mining DISPATCH). Afterward, we built a discrete event simulation model of the truck and shovel operation using an iron ore mine case study, implemented both of the dispatching models, and compared the results. The implementation of the models suggests that the multiple objective model developed in this paper is able to meet the production requirements of the operation using a fleet at 85% of the size of the deterministically calculated desired fleet. In addition, the model is able to meet the full capacity of the processing plants with a fleet of 30% less trucks than the desired fleet.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:276:y:2019:i:1:p:331-342
    DOI: 10.1016/j.ejor.2019.01.008
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    References listed on IDEAS

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    1. Ta, Chung H. & Ingolfsson, Armann & Doucette, John, 2013. "A linear model for surface mining haul truck allocation incorporating shovel idle probabilities," European Journal of Operational Research, Elsevier, vol. 231(3), pages 770-778.
    2. Montiel, Luis & Dimitrakopoulos, Roussos, 2015. "Optimizing mining complexes with multiple processing and transportation alternatives: An uncertainty-based approach," European Journal of Operational Research, Elsevier, vol. 247(1), pages 166-178.
    3. 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.
    4. Christina N. Burt & Lou Caccetta, 2014. "Equipment Selection for Surface Mining: A Review," Interfaces, INFORMS, vol. 44(2), pages 143-162, April.
    5. 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.
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    Cited by:

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    2. Hocine, Amin & Zhuang, Zheng-Yun & Kouaissah, Noureddine & Li, Der-Chiang, 2020. "Weighted-additive fuzzy multi-choice goal programming (WA-FMCGP) for supporting renewable energy site selection decisions," European Journal of Operational Research, Elsevier, vol. 285(2), pages 642-654.
    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).
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    5. 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.

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