Material transportation is an essential process in mining operations and accounts for a large portion of total energy consumption. With the development of automation technology, autonomous mining trucks have been deployed in open-pit mines to reduce transportation costs. Compared to manned mining trucks, autonomous trucks are more appropriate for automatic scheduling techniques due to their high informatization, high-precision perception, and sophisticated control. However, the existing truck scheduling research does not exploit autonomous trucks’ strengths to perform more delicate control, but still takes a traditional approach toward autonomous trucks. In this paper, we propose a mixed-integer programming model for joint optimization of autonomous trucks trips and speeds to minimize energy consumption. To solve the proposed scheduling model, a novel tabu search algorithm is proposed, which consists of two parts: an improved flow allocation model with matching factor is formulated in the upper part to determine the optimal truck flow, and in the lower part a tabu search procedure guided with the optimal flow is developed. Based on the mathematical model and solution technique, we also propose a real-time scheduling system of autonomous trucks for the stochastic and dynamic mining environment. A coal mine in Inner Mongolia, China, for example, we verified the effectiveness of the proposed truck scheduling model and real-time scheduling approach. We demonstrate that the proposed allocation model effectively accelerates the tabu search procedure, and for short-term decisions, the proposed solution technique satisfies the computational requirements of the real-time scheduling system.


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    Titel :

    Real-Time Scheduling of Autonomous Mining Trucks via Flow Allocation-Accelerated Tabu Search


    Beteiligte:
    Zhang, Xiaotong (Autor:in) / Guo, Ao (Autor:in) / Ai, Yunfeng (Autor:in) / Tian, Bin (Autor:in) / Chen, Long (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.09.2022


    Format / Umfang :

    7888421 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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