论文标题
部分可观测时空混沌系统的无模型预测
An Optimization Framework for Efficient and Sustainable Logistics Operations via Transportation Mode Optimization and Shipment Consolidation: A Case Study for GE Gas Power
论文作者
论文摘要
通用电气（GE）气体电源是汽油和汽轮机的领先制造商，在全球发电厂中生产和安装这些涡轮机。他们使用各种运输方式，包括海洋，空中和地面，从全球供应商那里为这些涡轮机采购这些涡轮机的组件，并将这些组件运输到美国的制造和集会地点。这些运输选项的交货时间和成本不同。挑战在于确定符合大量的货物和货运网络的复杂性的最具成本效益的解决方案。为了应对这一挑战，我们开发了一种定制的多个周期（动态），多商品网络流程模型和一种新型的启发式方法，并具有滚动时间范围。该模型包含了中间节点的合并和存储选项，从而使企业可以优化其货物。
General Electric (GE) Gas Power, a leading manufacturer of gas and steam turbines, manufactures and installs these turbines in power generation plants worldwide. They source components for these turbines from suppliers globally and transport these components to manufacturing and assembly locations in the United States using various modes of transportation, including ocean, air, and ground. These transportation options have different lead times and costs. The challenge lies in identifying the most cost-effective solution that meets the assembly requirements, given the high volume of shipments and the complexity of the freight network. To address this challenge, we develop a customized, multi-period (dynamic), multi-commodity network flow model and a novel heuristic approach with a rolling time horizon. This model incorporates consolidation and storage options at intermediate nodes, allowing the business to optimize its shipments.
