通过使用实时数据和机器学习的动态反向物流网络进行综合水果废物管理
Mohaddese Geraeli1, Emad Roghanian1
1Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran.
Journal of environmental management
|January 6, 2026
概括
本研究介绍了水果反向物流的实时优化模型,以减少浪费和提高可持续性. 它整合了动态定价和AI/IoT数据,以改善供应链中的利和社会目标.
科学领域:
- 供应链管理 供应链管理
- 运营研究 运营研究
- 可持续性科学 可持续性科学
背景情况:
- 像水果这样的易腐烂产品在供应链中面临大量的浪费,影响可持续性.
- 随着时间的推移,产品的恶化变成不同的品质等级,需要战略管理.
- 水果的反向物流涉及复杂的决定,涉及返回时间和质量,以减少浪费.
研究的目的:
- 为水果反向物流开发一种新的实时优化模型.
- 尽量减少产品浪费,优化利和社会目标.
- 整合动态定价和实时数据,以提高供应链的可持续性.
主要方法:
- 利用了全面的废物减少,回收,回收和处置战略.
- 集成的物联网 (IoT) 技术和人工智能 (AI) 实时保质期数据.
- 开发了一个联合的动态定价和动态反向物流模型.
主要成果:
- 展示了实时数据 (物联网和人工智能) 在管理水果质量和保质期方面的关键作用.
- 通过加拿大水果供应链的案例研究来验证拟议模型的有效性.
- 展示了供应链可持续性的重大改进.
结论:
- 实时数据集成对于优化易腐货物的反向物流至关重要.
- 拟议的模型有效地提高了水果供应链的可持续性.
- 关于产品退货质量和时间的战略决策对于减少浪费至关重要.
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