可持续农业供应链设计的数据驱动随机编程:迈向循环经济
Hamed Darouni1, Farnaz Barzinpour1, Amin Reza Kalantari Khalil Abad1
1School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
Journal of environmental management
|September 7, 2025
概括
这项研究利用数据驱动的随机模型优化了果产品的可持续农业供应链. 它平衡了经济,社会和环境因素,将废物转化为循环经济的生物炭.
科学领域:
- 农业供应链管理 农业供应链管理
- 运营研究 运营研究
- 可持续性科学 可持续性科学
背景情况:
- 农业供应链面临着气候变化和浪费造成的重大挑战,影响粮食安全和可持续性.
- 在不确定性下有效管理对于资源优化和废物减少至关重要.
- 闭环系统和循环经济原则对于可持续的农业实践至关重要.
研究的目的:
- 开发一个多目标优化模型,用于设计一个可持续的和响应的农业供应链网络,用于果产品.
- 综合经济成本,社会效益和响应能力,考虑设施的位置,流量,库存和短缺管理.
- 用数据驱动的随机方法来解决供应和购买价格的动态和相互关联的不确定性.
主要方法:
- 一个多阶段的随机编程方法与Fuzzy C集成-意味着用于场景生成的机器学习.
- 纳入农业废物的热化学转化为生物炭的循环经济原则.
- 应用增强的e-约束方法来解决多目标模型和管理环境约束 (二氧化碳排放,水消耗).
主要成果:
- 该模型有效地优化了经济成本,社会效益和不确定性下的反应能力.
- 3%的成本增加可以使社会福利提高25%,责任增加24%.
- 环境可持续性是通过对二氧化碳排放和水消耗的严格上限限制来解决的.
结论:
- 开发的框架提供了一个数据驱动的随机方法来管理农业供应链的不确定性.
- 它指导管理人员加强循环农业供应链,提高可持续性和响应能力.
- 伊朗的一项现实实例研究证实了该模型在管理不确定性方面的适用性和有效性.
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