在装配线平衡问题中使用启发式和人工神经网络方法的解决方法:照明行业的案例研究
1Department of Electric and Energy, University of Marmara, P.O Box 34865, Istanbul, Turkey.
Heliyon
|March 7, 2024
概括
使用启发式方法平衡装配线可显著提高效率和生产能力. 人工神经网络也显示出高精度的装配线平衡 (ALB) 问题.
科学领域:
- 工业工程 工业工程 工业工程
- 运营研究 运营研究
- 制造系统制造系统的制造
背景情况:
- 装配线的效率对于整体制造业绩至关重要.
- 平衡的装配线优化了资源利用,降低了成本.
- 本研究的重点是提高紧急灯具生产线的效率.
研究的目的:
- 为了比较四种启发式装配线平衡 (ALB) 方法的有效性.
- 评估ALB对每日生产能力的影响.
- 在ALB探索人工神经网络 (ANN) 的应用.
主要方法:
- 分析了来自紧急灯具制造商的实际生产数据.
- 通过使用四种不同的启发式ALB方法来平衡装配线:霍夫曼,Comsoal,Moodie&Young (M&Y) 和排名位置权重 (RPW).
- 一个人工神经网络 (ANN) 模型被训练使用研究的结果.
主要成果:
- 启发式方法 (霍夫曼,Comsoal,M&Y) 实现了高线效率93.955%.
- 通过RPW方法获得了84.414%的线路效率.
- 每日生产能力从250个增加到375个单位.
- 对于不同的装配线,ANN模型预测工作站的结果准确率为99.94%.
结论:
- 使用启发式方法的装配线平衡有效地提高了效率和生产能力.
- 在解决 ALB 问题时,ANN 为启发式方法提供了一个非常准确的替代方案或补充.
- 这些发现表明,通过ALB优化,制造业务的实际改进.
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