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ISCCO:一个基于深度学习特征提取的战略框架,用于动态最小化供应链运输成本损失
1School of Accounting, Xijing University, Xi 'an, Shaanxi, China.
PeerJ. Computer science
|February 3, 2025
概括
智能供应链成本优化 (ISCCO) 使用深度学习和优化来降低运输成本. 这一框架提高了效率,减少了取消,并增加了电子商务物流的利.
科学领域:
- 运营研究 运营研究
- 人工智能的人工智能
- 电子商务物流电子商务物流
背景情况:
- 全球电子商务的增长带来了重要的供应链和运输成本管理挑战.
- 优化物流对于在线零售业的业务利能力和客户满意度至关重要.
研究的目的:
- 引入智能供应链成本优化 (ISCCO) 框架,以尽量降低运输成本.
- 通过改进客户行为预测和动态商品分配来提高供应链效率.
主要方法:
- 集成深度学习 (自动编码器,随机森林) 用于客户细分.
- 应用基因算法增强的整数线性编程模型用于商品分配.
- 利用实时需求数据进行动态资源调整.
主要成果:
- 实现了用户分类准确度从50%提高到95.73%,并将模型损失从0.75降低到0.2.2.
- 订单取消率从50.79%降至41.39%,预发货率为25%.
- 与实际利相比,优化利增加了7.5%.
结论:
- 伊斯科框架显著提高了物流配送的效率和成本效益.
- 数据驱动的决策可以提高供应链的透明度和响应性.
- 优化的预发货政策导致利和客户满意度的提高.
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