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在垂直农场的自适应性生产策略数字双胞胎与Q学习算法
Yujia Luo1,2, Peter Ball3
1School of Business and Society, The University of York, York, United Kingdom.
Scientific reports
|April 29, 2025
概括
数字双胞胎技术通过提高需求满足和资源效率来提高城市粮食生产. 这项研究表明,Q学习模型的表现优于传统方法,支持可持续的城市农业.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 可持续发展 可持续发展 可持续发展
背景情况:
- 城市粮食生产提供了可持续性的好处,如减少土地使用和交通.
- 在城市食品系统中采用数字双胞胎 (DT) 技术的探索比在制造业中少.
- 适应性决策对于在需求波动下优化城市食品生产至关重要.
研究的目的:
- 调查数字双胞胎技术对城市食品生产中的适应性决策的影响.
- 为了比较不同模型 (MILP和Q-learning) 在提高生产决策方面的有效性.
- 评估服务水平,资源利用和能源效率的改善.
主要方法:
- 使用混合整数线性编程 (MILP) 和Q学习模型.
- 使用数字双胞胎数据为生产决策提供信息.
- 专注于"成长它约克"案例研究的实际应用.
主要成果:
- 与MILP模型相比,Q学习模型实现了更高的需求满足 ([公式:见文本]).
- 通过DT增强的Q学习方法,运营效率得到了显著的提高.
- 每个满足需求的电力使用量减少了大约[公式:参见文本].
结论:
- 数字双胞胎技术显著提高了城市食品生产中的适应性决策.
- 与DT集成的Q学习模型比传统的MILP方法提供更高的性能.
- 更多地应用DTs可以促进城市食品系统的经济性和环境可持续性.
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