可持续的供应链管理使用Bi-GRU网络,通过混合海上搜索和救援算法进行优化
1Monash University, Melbourne, 3800, Australia. zhenjielii00202120@outlook.com.
Scientific reports
|December 18, 2025
概括
本研究介绍了一种混合人工智能框架,使用双向门式循环装置 (Bi-GRU) 和混合海上搜救 (HMSR) 来实现可持续的农业供应链. 它增强决策,减少浪费,并在动态干扰中提高利.
科学领域:
- 农业科学 农业科学
- 供应链管理 供应链管理
- 人工智能的人工智能
背景情况:
- 农业和食品供应链面临着浪费,可追溯性问题和利优化等挑战.
- 动态干扰 (天气,运输延迟) 和传统方法的局限性需要先进的解决方案.
研究的目的:
- 为可持续的农业供应链管理提出和评估混合人工智能框架.
- 通过加强决策来应对包括减少废物,可追溯性和利优化在内的挑战.
主要方法:
- 一个混合框架,结合了双向门式循环单元 (Bi-GRU) 网络和混合海上搜救 (HMSR) 算法.
- 使用物联网传感器和市场趋势来获取时间序列数据,并使用Bi-GRU进行预测.
- HMSR算法优化了基于生产能力,储存限制和市场需求的决策.
主要成果:
- 在存储决策中实现了92.4%的准确性.
- 减少了34.2%的废物.
- 利率最大化了28.7%,表现优于基线模型 (GRU,LSTM,GA,PSO,HGSODL-ASCM).
结论:
- 混合人工智能方法,如拟议的Bi-GRU和HMSR框架,可以显著提高农业供应链的可持续性.
- 该框架为农业部门的利能力和废物减少提供了可适应和可扩展的解决方案.
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