一个模糊的数学模型,用于混合库存和采购优化,在一个反向物流系统考虑短缺和仓库容量
Hongyu Tang1, Amruth Ramesh Thelkar2
1College of Management, Guangxi City Vocational University, Guangxi, China.
Science progress
|October 4, 2023
概括
这项研究优化了逆向物流成本和供应链中的交货延迟. 它介绍了一个模糊的多目标模型和可适应的网络设计的Cuckoo优化算法 (COA).
科学领域:
- 运营研究 运营研究
- 供应链管理 供应链管理
- 工业工程 工业工程 工业工程
背景情况:
- 物流网络设计决策具有长期影响.
- 需求和回报率波动,需要适应性的网络.
- 优化反向物流和交付时间对于效率至关重要.
研究的目的:
- 为了最大限度地降低总反向物流成本和客户订单延迟.
- 为一个三级物流网络开发一个多目标的数学模型.
- 将不确定性纳入反向物流参数.
主要方法:
- 使用模糊方法来处理反向物流网络中的不确定性.
- 使用GAMS软件制定并解决了一个多目标的数学模型.
- 在MATLAB中,Cuckoo优化算法 (COA) 应用于大规模问题.
主要成果:
- 拟议的COA在解决数学模型方面表现出理想的性能.
- 算法取得了与总返回值的确切解决方案相比较的结果.
- 在COA中增加的重复导致目标函数值下降.
结论:
- 开发的模型和COA有效地解决了反向物流中的不确定性.
- 该研究为优化适应性和成本效益高的物流网络提供了一个框架.
- 这些发现支持在动态供应链环境中改进决策.
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