Combinatorial models for optimisation of placement of refuelling stations and routing for alternative fuel vehicles in road network

替代燃料车辆（AFV），特别是电动和混合动力车辆，在道路运输网络中发挥着越来越大的作用。尽管它们具有优势，但目前存在许多与其使用相关的挑战。例如，电动车辆通常具有相对短的巡航范围，需要大量的电池充电时间，并且通常附接到电网的现有容量。通常，对AFV和相应技术的开发的强烈兴趣是由有限的碳氢化合物资源和与内燃机的使用相关联的大规模环境问题所激发的。AFV背后的主要想法是让它们更节能，更环保，并尽可能使用可再生能源。与AFV和相应技术相关的问题和挑战包括在何处以及如何为AFV提供燃料，如何以有效的方式使用它们，以及如何使它们被公众采用。该博士项目的主要目标是开发新的数学模型和组合优化技术，用于智能和高效地开发道路网络基础设施，用于使用新型能源（例如电力，生物燃料，氢）的车辆导航，并就现有基础设施的有效路由提供建议。这将基于使用图形和有向图开发更精细的数学模型，并通过算法解决相应的组合优化问题，获得相关的分析和结构结果。我们将使用不同类型的支配集概念来研究加权图和有向图的新模型，随后开发新的方法来提供高效和有效的算法解决方案技术。将特别注意将模型扩展到大型道路网络。

Alternative fuel vehicles (AFV's), in particular, electric and hybrid vehicles, play an increasing role in road transport networks. Despite their advantages, currently there are many challenges associated with their usage. For example, electric vehicles usually have a relatively short cruising range, require significant battery charging time, and normally are attached to the existing capacities of the electric grid. In general, a strong interest in development of AFV's and corresponding technologies is motivated by limited resources of hydrocarbons and large-scale environmental issues associated with usage of internal combustion engines. The main ideas behind AFV's are to have them more energy efficient, environmentally friendly and, whenever possible, using renewable energy resources. Problems and challenges associated with AFV's and corresponding technologies include where and how to fuel AFV's, how to use them in an efficient way, and how to make them publicly adopted. The main objective of this PhD project is to develop new mathematical models and combinatorial optimisation techniques for smart and efficient development of road network infrastructure for navigation of vehicles using new types of energies, e.g. electric, biofuel, hydrogen, and to advise on efficient routing with respect to the existing infrastructure. This will be based on development of more subtle mathematical models using graphs and digraphs and done by algorithmically solving the corresponding combinatorial optimisation problems, obtaining related analytical and structural results. We will examine new models on weighted graphs and digraphs using different types of dominating set concepts, subsequently developing new methodologies to provide efficient and effective algorithmic solution techniques. Special attention will be paid to scaling of the models to large-size road networks.