Low-Carbon Oriented Routing Optimisation in Logistics Distribution Systems with Road Congestion Considerations

In the context of global decarbonisation initiatives, the logistics sector faces dual challenges: its substantial energy consumption and carbon footprint conflict with societal goals for a low-carbon economy, while escalating pressures from urban traffic congestion also inflate distribution costs. The environmental externalities and economic losses induced by the combination of inefficient routing and congestion have jointly motivated the emerging research field of low-carbon vehicle routing optimisation. To reconcile these issues, this study develops a bi-level programming framework for low-carbon-oriented vehicle routing optimisation that explicitly accounts for road congestion. The upper-level model aims to minimise the total cost (including vehicle fixed cost, transportation cost, carbon emission cost and time-window penalty) by internalising a carbon tax constraint. The lower-level model employs a user equilibrium (UE) model, focusing on minimising travel time from the perspective of road users. A hybrid solution methodology (GA-Tent & Frank-Wolfe) is proposed, integrating an enhanced genetic algorithm with Tent chaos mapping for global optimisation and a modified Frank-Wolfe algorithm for traffic assignment. Finally, a case study using the Sioux Falls network demonstrates that traffic congestion increases carbon emissions, but a moderate carbon tax increase can effectively reduce vehicle carbon emissions. These insights suggest policymakers should implement progressive carbon pricing mechanisms coupled with dynamic congestion pricing, while logistics operators should prioritise route optimisation systems with real-time traffic adaptation capabilities.