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一个随机优化的两级供应链模型:运营风险评估的透方法
Konstantinos Petridis1, Prasanta Kumar Dey2, Amit K Chattopadhyay3
1Department of Business Administration, University of Macedonia, 54006 Thessaloniki, Greece.
这项研究使用学方法,通过考虑不确定性来优化供应链. 纳入随机波动有助于生产者通过选择特定的供应商来降低成本和增加利.
科学领域:
- 运营研究 运营研究
- 供应链管理 供应链管理
- 随机优化 随机优化的优化
背景情况:
- 传统的供应链优化通常依赖于决定性模型.
- 为了最大限度地降低运营风险,需要解决供应线的限制和不确定性.
- 随机波动表示随机性,量化供应链决策中的不确定性或风险.
研究的目的:
- 展示一种基于的方法来优化制造和分销供应链.
- 将主观和客观的不确定性纳入供应链决策中.
- 建模一个加工生产厂,分析随机成本变化.
主要方法:
- 采用方法来模拟供应链的不确定性.
- 分析随机变化的生产和运输成本.
- 使用随机优化技术来确定最佳策略.
- 为了建模成本波动,比较帕雷托分布和高斯分布.
主要成果:
- 随机优化使生产者能够实现更好的财务结果.
- 生产商可以提高销售价格,同时降低优化生产成本.
- 选择具有帕雷托分布式生产成本的生产商可以提高优化.
- 较低的帕雷托指数导致更准确的供应链成本优化预测.
结论:
- 基于的随机方法有效地优化不确定性下的供应链.
- 巴雷托分布提供了优越的成本优化预测,而高斯分布提供了简单性.
- 基于成本分配的战略供应商选择是提高财务业绩的关键.
- 优化性能与实现简单性的平衡对于实际应用至关重要.
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