在智能林业中,对电缆路布局的多目标优化
Carl O Retzlaff1, Christoph Gollob1, Arne Nothdurft2
1Human-Centered AI Lab, Institute of Forest Engineering, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences, Vienna, Austria.
概括
本研究介绍了在的地形中设计有线路布局的多目标优化,提高成本效益,平衡经济和环境因素,以便更好地规划木材采伐.
科学领域:
- 林业工程 林业工程
- 运营研究 运营研究
背景情况:
- 目前用于地采伐木材的电缆路设计方法依赖于手动规划或低分辨率的GIS数据.
- 这限制了知情决策和收获操作的优化.
研究的目的:
- 开发和评估一种新的线缆路设计方法,使用具有现实的表示的多目标优化.
- 为了比较单一目标和多目标优化方法,以生成有效的电缆路布局.
主要方法:
- 实施和评估重量化单一目标,AUGMECON2和NSGA-II多目标优化方法.
- 适应了AUGMECON2的线性编程多目标优化与现实的电缆道路表示.
- 应用了一种多目标遗传算法 (NSGA-II) 模拟化用于电缆道路设计.
主要成果:
- 与手动或单一目标方法相比,多目标优化产生了更具成本效益,平衡和适应性的缆车路布局.
- 该研究表明,经济 (安装成本,收获量) 和环境 (剩余支架损坏,侧面造地工作负载) 考虑因素的成功整合.
- AUGMECON2和NSGA-II在优化现实的电缆公路表示方面表现出明显的优势.
结论:
- 多目标优化显著提高了电缆路设计,用于在地形中采伐木材.
- 这种方法通过平衡多个经济和环境目标来促进知情决策.
- 开发的方法为森林作业规划提供了更有效和可持续的解决方案.
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