在CDP报告分析中,人工智能驱动的自动化用于增强可持续发展努力
Ramya Rangarajan1, Tamilarasi Kathirvel Murugan2, Logeswari Govindaraj1
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.
Scientific reports
|July 7, 2025
概括
本研究介绍了一种混合基因算法 (GA) 和长短期记忆 (LSTM) 网络,以优化供应链的可持续性. 该方法显著减少了排放,提高了运营效率,同时确保了监管合规性.
科学领域:
- 供应链管理 供应链管理
- 环境科学 环境科学
- 人工智能的人工智能
背景情况:
- 对企业施加越来越大的压力,要求他们采用可持续的做法,减少碳足迹.
- 需要在供应链中制定高效的运营策略,以平衡环境和经济目标.
研究的目的:
- 开发一种新的混合方法,将遗传算法 (GA) 和长短期记忆 (LSTM) 网络结合起来,以优化供应链的可持续性.
- 创建一个具有成本效益,可扩展的解决方案,以减少排放,提高效率,并确保监管合规.
主要方法:
- 利用公开可用的碳披露项目 (CDP) 报告的数据进行排放预测和资源分配.
- 采用LSTM网络,根据历史数据预测排放趋势.
- 应用GA优化供应链流程,包括运输和能源消耗,通过多目标优化.
主要成果:
- 总供应链排放量减少了23.67%,间接排放量得到了显著改善.
- 提高了10.98%的运营效率.
- 确保100%遵守环境法规,消除处罚.
结论:
- 混合GA-LSTM框架有效地优化了供应链的可持续性,提供了一个实用,数据驱动的解决方案.
- 该方法为企业提供了有价值的见解,旨在实现可持续发展目标并提高业绩.
- 该系统对于大型企业和中小企业都是可扩展的,促进可持续实践的广泛采用.
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