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An optimization model for the real-time truck dispatching problem in open-pit mining operations

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Abstract

This work is concerned with the real-time truck dispatching problem in open-pit mining operations. The proposed methodology is built on a multi-stage dispatching approach to optimize the truck-shovel material handling systems. The procedure consists of two main stages: allocation planning and dynamic allocation. At first, a scenario-based method is presented to estimate the optimal size of trucks in the mining operation. Then, a novel multi-objective mathematical model for solving the problem of dynamically allocating trucks is presented, aiming at minimizing fleet waiting times and deviations from the path production requirements established by the allocation planning. To evaluate the performance of the proposed model, three different heuristic methods, including criteria of minimizing shovel idle time, minimizing ratio variance, and minimizing the deviation from the allocation planning, are developed. Afterward, the dispatching methods are tested by building a discrete-event simulation of a copper ore mine case study to compare the results. The findings suggest that the multi-objective model developed in this paper has a great potential to meet the production requirements of the operation compared to the heuristic algorithms.

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Authors and Affiliations

  1. Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran

    Hossein Mirzaei-Nasirabad & Mehrnaz Mohtasham

  2. Faculty of Mining and Petroleum Engineering, Alberta University, Edmonton, Canada

    Hooman Askari-Nasab

  3. Faculty of Basic Sciences, Sahand University of Technology, Tabriz, Iran

    Behrooz Alizadeh

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  1. Hossein Mirzaei-Nasirabad
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  2. Mehrnaz Mohtasham
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Correspondence to Hossein Mirzaei-Nasirabad.

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Mirzaei-Nasirabad, H., Mohtasham, M., Askari-Nasab, H. et al. An optimization model for the real-time truck dispatching problem in open-pit mining operations. Optim Eng 24, 2449–2473 (2023). https://doi.org/10.1007/s11081-022-09780-x

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