Optimization of intermodal rail operations and locomotive fleet management

优化多式​​联运铁路运营和机车车队管理

• 批准号: 513259-2017
• 负责人: RobertFrejinger, Emma
• 金额: $ 14.21万
• 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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=618216
• 关键词: Optimization; intermodal; rail; operations; locomotive

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 the state-of-the-art and the state-of-practice is huge. In addition, both the scientific literature and the current industrial practice tend to treat decision problems in isolation of each other and most assume deterministic settings in which all input data are known with certainty. Uncertainty is particularly important as it affects planning and operations on a continuous basis. Decision support tools should thus not only take this uncertainty into account at the planning level but also provide ways to handle deviations from planned operations at the execution level. In this project we deal with two complementary families of problems currently faced in intermodal and freight rail transportation. The first family of problems concerns the planning and operation of intermodal rail transportation. We focus on a tactical network load block planning problem and three related issues: demand forecasting, load planning and track planning. The second family concerns the management of the locomotive fleet that is used to haul all types of cargo, including intermodal. The project addresses two specific problems: locomotive assignment and locomotive routing. Intermodal container freight transportation is the backbone of international trade, as well as of a large part of Canadian and North-American imports and exports. In this context, Canada benefits from having the largest rail network in the world to support its economic development. Rail is also the most environmentally friendly land freight transport mode. It is therefore not only beneficial to the Canadian railway industry but also to the Canadian society to make this mode of transport more efficient and cost effective. The results of this project can contribute to decreased delivery times, costs, and environmental footprint. Furthermore, developing decision aid tools that are based on mathematical models can help personnel in the railway industry make more informed decisions which, in turn, can lead to increased safety.

加拿大铁路公司是网络和终端运营商，因此面临许多复杂的大规模优化问题。数十年来，在运营研究（OR）文献中已经研究了其中许多问题，但是最先进的和练习的差距很大。此外，科学文献和当前的工业实践都倾向于彼此孤立地对待决策问题，并且大多数人都假定确定性的设置，其中所有输入数据都可以肯定。不确定性尤其重要，因为它会连续影响计划和操作。因此，决策支持工具不仅应在计划级别上考虑这种不确定性，而且还应提供处理在执行级别的计划操作偏差的方法。在这个项目中，我们处理了两个联运和货运铁路运输中目前面临的问题的补充家庭。第一个问题家族涉及联运铁路运输的规划和运行。我们专注于战术网络负载块计划问题和三个相关问题：需求预测，负载计划和跟踪计划。第二个家庭涉及用于拖运各种货物的机车车队的管理，包括联运。该项目解决了两个具体问题：机车分配和机车路由。联运容器货运运输是国际贸易的支柱，也是加拿大和北美进出口的很大一部分。在这种情况下，加拿大受益于拥有世界上最大的铁路网络来支持其经济发展。铁路也是最环保的土地运输模式。因此，它不仅对加拿大铁路行业有益，而且对加拿大社会也有益于使这种运输方式更加高效和成本效益。该项目的结果可能导致交付时间，成本和环境足迹的减少。此外，开发基于数学模型的决策援助工具可以帮助铁路行业的人员做出更明智的决策，这反过来又可以提高安全性。