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Quality Control System for the Locomotive Overhaul Process Based on Ant Colony Algorithm

机译:基于蚁群算法的机车大修过程质量控制系统

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When the locomotive system breaks down, it is hard to control the quality of the locomotive overhaul process using traditional network diagram, owing to the complexity of fault phenomena and fault reasons. Moreover, conventional method may lead to damage to some fault-free components easily. Ant colony algorithm (ACA) was adopted to improve the quality in the locomotive overhaul system. In view of the overhaul difficulty, occurrence probability, human resources and time resources in the traditional network diagram, the overhaul plan was optimized based on ant colony algorithm and resource allocation. The experiment results show the ACA-based system is effective for improving the efficiency and quality of the locomotive overhaul process.
机译:当机车系统分解时,由于故障现象的复杂性和故障原因,难以使用传统网络图控制机车大修过程的质量。此外,常规方法可能会容易地导致对某些无故障组件造成损坏。采用蚁群算法(ACA)来提高机车大修系统中的质量。鉴于传统网络图中的大型难度,发生概率,人力资源和时间资源,基于蚁群算法和资源分配优化了大修计划。实验结果表明,基于ACA的系统有效地提高机车大修过程的效率和质量。

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