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<p>Improved scheduling of underground transportation vehicles in coal mines can significantly enhance work efficiency and contribute to safer production. However, the specific working conditions and limitations of electric vehicles pose significant challenges to effective vehicle scheduling. To address this issue, a constrained single-objective optimization model is developed to minimize transportation costs for low-carbon scheduling of underground electric transportation vehicles (ETVs). The model incorporates constraints related to load capacity, cruising range, and safety regulations. A specific energy consumption model for ETVs is formulated, considering factors such as road conditions, load, and driving state. To solve this problem, an improved ant colony optimization algorithm integrated with Q-learning (ACO-QL) is proposed. Specifically, ant colony optimization explores the global solution space and identifies promising regions, while the split strategy effectively distributes demand across multiple vehicles. Q-learning enhances local search by selecting the most appropriate operator, preventing premature convergence to local optima. Experimental results on four real-world instances demonstrate the superior performance of ACO-QL compared to state-of-the-art algorithms.</p>

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A low-carbon scheduling method based on improved ant colony algorithm for underground electric transportation vehicles

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月30日

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  • Yizhe Zhang,
  • Yinan Guo,
  • Yao Huang,
  • Shirong Ge

摘要

Improved scheduling of underground transportation vehicles in coal mines can significantly enhance work efficiency and contribute to safer production. However, the specific working conditions and limitations of electric vehicles pose significant challenges to effective vehicle scheduling. To address this issue, a constrained single-objective optimization model is developed to minimize transportation costs for low-carbon scheduling of underground electric transportation vehicles (ETVs). The model incorporates constraints related to load capacity, cruising range, and safety regulations. A specific energy consumption model for ETVs is formulated, considering factors such as road conditions, load, and driving state. To solve this problem, an improved ant colony optimization algorithm integrated with Q-learning (ACO-QL) is proposed. Specifically, ant colony optimization explores the global solution space and identifies promising regions, while the split strategy effectively distributes demand across multiple vehicles. Q-learning enhances local search by selecting the most appropriate operator, preventing premature convergence to local optima. Experimental results on four real-world instances demonstrate the superior performance of ACO-QL compared to state-of-the-art algorithms.