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

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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.).