A data driven operations research approach to intermodal rail transportation load and block capacity planning problems

针对多式联运铁路运输负荷和区块容量规划问题的数据驱动运筹学方法

• 批准号: 477938-2014
• 负责人: RobertFrejinger, Emma
• 金额: $ 8.07万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=599706
• 关键词: data; driven; operations; research; approach

Intermodal transport refers to the transportation of freight in containers using more than one mode of rail, ship and truck. This project focuses on optimizing the planning and operations of intermodal rail transportation. Canadian railway companies are both network and terminal operators and hence face many complex large-scale optimization problems. Many of these problems have been studied in the operations research (OR) literature for decades but the gap between state-of-the-art and state-of-practice is huge. The railway industry heavily relies on the experience of human resources rather than mathematical models even though the literature reports potential savings in the order of millions of dollars annually by using optimization models. This project addresses this gap by proposing methods to deal with two complementary problems currently faced in intermodal rail transportation, namely, load planning under uncertainty and block capacity planning. We address these problems by combining demand models and OR methods.

多式联运是指采用铁路、船舶和卡车等一种以上方式进行的集装箱货物运输。该项目重点优化多式联运轨道交通的规划和运营。加拿大铁路公司既是网络运营商，也是终端运营商，因此面临许多复杂的大规模优化问题。其中许多问题已经在运筹学 (OR) 文献中研究了数十年，但最先进的技术与实践水平之间的差距巨大。尽管文献报道通过使用优化模型每年可以节省数百万美元，但铁路行业严重依赖人力资源的经验而不是数学模型。该项目通过提出解决当前多式联运铁路运输面临的两个互补问题的方法来解决这一差距，即不确定性下的负荷规划和区块容量规划。我们通过结合需求模型和 OR 方法来解决这些问题。