EPSRC DTP Hub for Sustainable Transport: Electrical Vehicle Routing Optimization Using Machine Learning

EPSRC DTP 可持续交通中心：使用机器学习的电动汽车路线优化

• 批准号: 2440359
• 金额: --
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2440359
• 关键词: EPSRC; DTP; Hub; Sustainable; Transport

Given the current concerns regarding the environment and global warming, reducing the use of fossil fuels and replacing them with renewable energy sources is becoming increasingly important. Government's ambition is that nearly all cars and vans on our roads are zero emission by 2035 supported by "Automated and Electric Vehicles Act, 2018". Electric vehicles are expected to play a dominant role in decarbonising the transport sector. Electric vehicles have a number of limitations which make their adoption challenging. The greatest of these is the fact that these vehicles have limited driving range meaning that they must be recharged frequently where this recharging can require a significant amount of time.In this project we will develop novel methods for optimizing the routes taken by electrical vehicles toward minimizing detours required for recharging and the corresponding delays caused by this. Delays caused by recharging can be minimized by aligning these events as best possible with existing pauses in the transportation process. For example, if an electrical vehicle carrying goods needs to be reloaded, it may be recharged while this reloading is taking place. The methods developed in this project will be general in nature but for the purposes of this project we will focus on optimizing the transportation logistics of medium to large businesses and organizations. Transportation is usually a significant part of the cost of a product and therefore it is important that it is optimized to support the adoption of electrical vehicles.Optimizing the above electrical vehicle routing problem is provably extremely hard making it difficult to solve exactly. Therefore, in most cases one can only hope to find a relatively good solution through the use of heuristic optimization methods. In this context, a heuristic optimization method is an optimization method which does not provably always perform well but empirically performs well in many cases. Traditionally such optimization methods are manually designed using a combination of domain knowledge and experimentation. In this work we will use machine learning methods which use large volumes of data to learn useful heuristic optimization methods. This approach is motivated by recent applications of machine learning to related optimization problems which have shown to achieve state of the art results.

鉴于目前对环境和全球变暖的担忧，减少化石燃料的使用并用可再生能源代替它们变得越来越重要。政府的目标是，到2035年，在“2018年自动驾驶和电动汽车法案”的支持下，道路上几乎所有的汽车和货车都是零排放的。预计电动汽车将在交通运输部门的脱碳中发挥主导作用。电动汽车有许多限制，这使得它们的采用具有挑战性。其中最大的问题是，这些车辆的行驶里程有限，这意味着它们必须经常充电，而充电可能需要大量的时间。在这个项目中，我们将开发新的方法来优化电动汽车的路线，以最大限度地减少充电所需的弯路和由此造成的相应延误。充电造成的延迟可以通过尽可能将这些事件与运输过程中的现有暂停相结合来最小化。例如，如果一辆载有货物的电动汽车需要重新装货，它可能会在重新装货时充电。在这个项目中开发的方法将是一般性质的，但为了这个项目的目的，我们将专注于优化中型到大型企业和组织的运输物流。运输通常是产品成本的重要组成部分，因此对其进行优化以支持电动汽车的采用非常重要。优化上述电动汽车路径问题被证明是极其困难的，难以精确求解。因此，在大多数情况下，人们只能希望通过使用启发式优化方法找到一个相对较好的解决方案。在这种情况下，启发式优化方法是一种优化方法，它不能证明总是表现良好，但在许多情况下经验表现良好。传统上，这种优化方法是使用领域知识和实验相结合的人工设计的。在这项工作中，我们将使用使用大量数据的机器学习方法来学习有用的启发式优化方法。这种方法的动机是最近机器学习在相关优化问题上的应用，这些问题已经显示出达到最先进的结果。