优化冷食品供应链,以改善气候变化下的食品供应.
David Hernandez-Cuellar1, Krystel K Castillo-Villar1, Fernando Rey Castillo-Villar2
1Mechanical Engineering Department, Texas Sustainable Energy Research Institute, The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX 78249, USA.
Foods (Basel, Switzerland)
|August 14, 2025
概括
本研究引入了气候意识的供应链模型,以降低食品运输成本. 通过考虑天气对作物产量的影响,它优化了适应性农产品物流的分销路线.
科学领域:
- 农业经济学 农业经济学
- 运营研究 运营研究
- 气候科学 气候科学
背景情况:
- 产品供应链对于有效和负担得起地提供新鲜食品至关重要.
- 枢纽式模型优化了冷食品供应链,但传统方法忽视了气候变化对作物产量的影响.
- 气候变化,包括二氧化碳的增加和极端天气,显著影响作物生产率和可用性,导致经销商成本增加.
研究的目的:
- 开发一个随机的集线网络优化模型,最大限度地降低运输成本.
- 将气候变化的对作物产量影响明确纳入供应链路线优化.
- 加强对气候适应性产品供应链的决策.
主要方法:
- 开发了一个随机的枢纽和分支网络优化模型.
- 利用加利福尼亚州的气候模型和真实土壤数据来创建各种天气场景.
- 将该模型应用于加利福尼亚州关键作物草的冷食品供应链 (CFSC).
主要成果:
- 降雨量增加的场景导致产量增加,通过从附近农场采购来降低成本.
- 降雨量减少的场景导致产量降低,需要从遥远的地方采购,增加运输成本.
- 供应链配置对气候模型的选择敏感,需要定期更新.
结论:
- 随机模型通过优化基于气候影响产量的路线,有效地降低运输成本.
- 这项研究为规划气候适应性供应链提供了一个工具,改善了对气候相关干扰的准备.
- 定期更新气候场景输入对于稳健和适应性供应链规划至关重要.
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