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

多式联运是指使用一种以上的铁路、船舶和卡车方式进行集装箱货物运输。该项目的重点是优化多式联运铁路运输的规划和运营。加拿大铁路公司既是网络运营商又是终端运营商，因此面临着许多复杂的大规模优化问题。这些问题中的许多问题已经在运筹学（OR）文献中研究了几十年，但最先进的技术和实践之间的差距是巨大的。铁路行业在很大程度上依赖于人力资源的经验，而不是数学模型，即使文献报道，潜在的节省数百万美元，每年通过使用优化模型。该项目解决了这一差距，提出的方法来处理目前面临的两个互补的问题，即在不确定性和块容量规划下的负荷规划在多式联运铁路运输。我们解决这些问题相结合的需求模型和OR方法。 加拿大拥有世界上最大的铁路网络之一，铁路是最环保的陆路货运方式。因此，通过提高这种模式的效率和成本效益来增加其市场份额，不仅对加拿大铁路业有利，而且对加拿大社会也有利。该项目中开发的方法和算法将在加拿大最大的铁路公司合作伙伴组织中实施。该方法具有一定的通用性，可供北美其他铁路公司参考。结果可以有助于减少交付时间和成本。此外，开发基于数学模型的决策辅助工具可以帮助铁路行业的人员做出更明智的决策，从而提高安全性。

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. Canada has one of the largest rail networks in the world and rail is 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 try to increase its market share by making this mode more efficient and cost effective. The methods and algorithms developed in this project will be implemented at the partner organization which is the largest railway company in Canada. The methods are general and can be useful to other North American railway companies. The results can contribute to decreased delivery times and costs. Moreover, developing decision aid tools that are based on mathematical models can help personnel in the railway industry to make more informed decisions, which in turn, can lead to increased safety.