This paper presents an algorithm for optimizing the scheduling of trackless equipment in underground mines. With the shortest working interval and maximum productivity as goals, a genetic algorithm (GA) is used to solve the problem, and obtain the optimal working sequence with the most suitable equipment configuration possible. The input for the proposed method is the number of units and capacity of trackless equipment, the production process, ore amount in stopes, and the distance between stopes. The algorithm is verified using four setups of 5 stopes with 5 cycles, 5 stopes with 15 cycles, 10 stopes with 10 cycles, and 10 stopes with 30 cycles. The solution time of the algorithm is no more than 20 min, which is acceptable for practical applications. The results show that the setup of 10 stopes with 30 cycles is closer to the actual production of the mines, and the optimization model can effectively improve the operation efficiency. In this scenario, the robustness of the optimization is tested by simulating equipment failure events. Under the condition of 8% failure rate, the operation time is extended over 3.21–14.56% than expected, which represents strong robustness. The algorithm can quickly provide a feasible and effective solution for the production scheduling decision of trackless equipment in underground mines.

中文翻译：

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基于遗传算法的地下矿山无轨设备调度优化

本文提出了一种优化井下无轨设备调度的算法。以最短的工作间隔和最大的生产率为目标，使用遗传算法（GA）来解决问题，并以最合适的设备配置获得最佳的工作顺序。该方法的输入是无轨设备的台数和产能、生产工艺、采场矿石量和采场间距。该算法使用四种设置进行验证，即 5 个停靠点 5 个循环、5 个停靠点 15 个循环、10 个停靠点 10 个循环和 10 个停靠点 30 个循环。算法求解时间不超过20 min，在实际应用中是可以接受的。结果表明，设置10个30周期采场更接近矿山实际生产情况，优化模型能有效提高作业效率。在这种情况下，通过模拟设备故障事件来测试优化的稳健性。在8%的故障率条件下，运行时间比预期延长了3.21-14.56%，具有很强的鲁棒性。该算法可为井下无轨设备的生产调度决策快速提供可行有效的解决方案。比预期高 56%，代表稳健性强。该算法可为井下无轨设备的生产调度决策快速提供可行有效的解决方案。比预期高 56%，代表稳健性强。该算法可为井下无轨设备的生产调度决策快速提供可行有效的解决方案。