Last Mile Logistics and the Shared Economy: Developing dynamic vehicle routing algorithms that adopt unsupervised learning for novel last-mile initiat

本博士旨在研究创新如何塑造最后一英里物流，并开发一个可以适应这些发展的建模框架。这项工作旨在确定新颖的最后一英里解决方案（众包运输、公共交通搭便车共享）能否在城市环境中协调经济效率和环境可持续性。因此，这项工作将探讨每种解决方案如何影响能源消耗、网络效率和空气污染。该研究将在一个具有不同输入参数（网络拥塞、需求水平等）的模拟测试台上对解决方案进行建模。

This PhD aims to investigate how innovation can shape last-mile logistics and develop a modelling framework that could accommodate these developments. This work seeks to establish whether novel last-mile solutions (Crowdshipping, Public Transport Piggybacking Van-Sharing) could harmonise economic efficiency with environmental sustainability in urban environments. Hence, this work will explore how each solution affects energy consumption, network efficiency and air pollution. The investigation will model the solution on a simulation testbed with varying input parameters (network congestion, demand-levels, etc.).