Shi Jianli
School of Management Science and engineering, Chongqing Technology and Business University, Chongqing, Chongqing, 400061, China.
Sci Rep. 2024 Sep 28;14(1):22477. doi: 10.1038/s41598-024-72723-2.
Maintaining the quality and integrity of frozen goods throughout the supply chain necessitates a robust and efficient cold chain logistics network. This research proposes a machine learning-based method for optimizing such networks, resulting in significant cost reduction and resource utilization improvement. The method employs a three-phase approach. First, K-means clustering groups sellers based on their geographical proximity, simplifying the problem and enabling more accurate demand prediction. During the second phase of the proposed method, Gaussian Process Regression models predict future sales volume for each seller cluster, leveraging historical sales data. Finally, the Capuchin Search Algorithm simultaneously optimizes distributor location and resource allocation for each cluster, minimizing both transportation and holding costs. This multi-objective approach achieved a 34.76% reduction in costs and a 15.6% reduction in resource wastage compared to the existing system. This novel method offers a valuable tool for frozen goods distribution networks, with advantages such as considering multiple goals for optimization, focusing on demand prediction, potential for reduced complexity, and focusing on managerial insights over compared methods.
在整个供应链中保持冷冻货物的质量和完整性需要一个强大且高效的冷链物流网络。本研究提出了一种基于机器学习的方法来优化此类网络，从而显著降低成本并提高资源利用率。该方法采用三阶段方法。首先，K均值聚类根据卖家的地理位置相近程度对其进行分组，简化问题并实现更准确的需求预测。在所提出方法的第二阶段，高斯过程回归模型利用历史销售数据预测每个卖家集群的未来销售量。最后，卷尾猴搜索算法同时优化每个集群的分销商位置和资源分配，将运输成本和持有成本降至最低。与现有系统相比，这种多目标方法实现了成本降低34.76%，资源浪费减少15.6%。这种新颖的方法为冷冻货物配送网络提供了一个有价值的工具，具有考虑多个优化目标、专注于需求预测、降低复杂性的潜力以及相对于比较方法更注重管理见解等优点。
