基于机器学习的最佳温度管理模型,用于易腐食品供应链的安全和质量控制
Joy Eze1, Yanqing Duan2, Elias Eze3
1Department of Computing, School of Professional Studies, Science and Technology, Goldsmiths University of London, New Cross, London, SE14 6NW, UK.
Scientific reports
|November 8, 2024
概括
优化食品供应链需要精确的温度控制,以尽量减少浪费. 本研究使用最佳控制模型和机器学习 (k-means集群) 来确定易腐食品的理想储存温度,确保质量和安全.
科学领域:
- 运营研究 运营研究
- 食品科学 食品科学 食品科学
- 计算机科学 计算机科学
背景情况:
- 食品供应链管理面临的挑战是由于易腐烂产品的短保质期和敏感性.
- 温度控制对于保持易腐食品的质量,安全性和保质期至关重要.
- 尽量减少食物浪费需要有效的策略来管理整个供应链中的易腐货物.
研究的目的:
- 提出一种使用数学最佳控制来控制易腐食品储存温度的方法.
- 为了确定单个易腐食品的最佳储存温度.
- 在多种商品供应链中应用机器学习来识别温度集群.
主要方法:
- 开发了一个数学最佳控制模型,其中存储温度是控制变量.
- 应用Pontryagin的最大原则来解决单一商品的最佳控制问题.
- 利用k-means集群技术,一种无监督的机器学习方法,用于多温度商品供应链.
主要成果:
- 最佳控制模型成功地确定了单个易腐食品的最佳储存温度.
- 该k-means集群技术被证明是有效的在识别适合的温度集群的各种易腐烂的商品.
- 描述性分析证实了k-means集群在多种商品供应链的质量控制中的有效性.
结论:
- 最佳的控制模型和机器学习为管理易腐烂的食品供应链提供了强大的解决方案.
- 精确的温度管理是减少食物浪费和确保产品安全和质量的关键.
- k-means集群技术为优化复杂的多商品环境中的存储条件提供了一种有效的方法.
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